From 2f19bfed120cadbf1c88cb16b6d43093f9154eac Mon Sep 17 00:00:00 2001 From: github-actions Date: Sun, 28 Jul 2024 01:08:51 +0000 Subject: [PATCH] update catalog --- .../Daily_Chlorophyll_a/collection.json | 42 ++--- .../Daily_Chlorophyll_a/models/USGSHABs1.json | 2 +- .../models/cb_prophet.json | 40 ++--- .../models/climatology.json | 38 ++--- .../Daily_Chlorophyll_a/models/fTSLM_lag.json | 2 +- .../models/persistenceRW.json | 4 +- .../models/procBlanchardMonod.json | 20 +-- .../models/procCTMIMonod.json | 12 +- .../models/procEppleyNorbergMonod.json | 20 +-- .../models/procEppleyNorbergSteele.json | 16 +- .../models/procHinshelwoodMonod.json | 20 +-- .../models/procHinshelwoodSteele.json | 2 +- .../Daily_Chlorophyll_a/models/tg_arima.json | 22 +-- .../Daily_Chlorophyll_a/models/tg_ets.json | 4 +- .../models/tg_humidity_lm.json | 12 +- .../models/tg_humidity_lm_all_sites.json | 20 +-- .../Daily_Chlorophyll_a/models/tg_lasso.json | 28 ++-- .../models/tg_precip_lm.json | 24 +-- .../models/tg_precip_lm_all_sites.json | 16 +- .../models/tg_randfor.json | 2 +- .../Daily_Chlorophyll_a/models/tg_tbats.json | 22 +-- .../models/tg_temp_lm.json | 20 +-- .../models/tg_temp_lm_all_sites.json | 2 +- .../models/xgboost_parallel.json | 28 ++-- .../Daily_Dissolved_oxygen/collection.json | 22 +-- .../models/AquaticEcosystemsOxygen.json | 4 +- .../Daily_Dissolved_oxygen/models/BBTW.json | 2 +- .../Daily_Dissolved_oxygen/models/BTW.json | 2 +- .../models/GLEON_lm_lag_1day.json | 16 +- .../models/LSAMP_AWPC.json | 2 +- .../models/MSU_ARIMA.json | 2 +- .../models/NDWaterTempDO.json | 2 +- .../models/air2waterSat_2.json | 56 +++---- .../models/cb_prophet.json | 72 ++++---- .../models/climatology.json | 26 +-- .../models/hotdeck.json | 30 ++-- .../models/persistenceRW.json | 70 ++++---- .../models/tg_arima.json | 62 +++---- .../Daily_Dissolved_oxygen/models/tg_ets.json | 66 ++++---- .../models/tg_humidity_lm.json | 20 +-- .../models/tg_humidity_lm_all_sites.json | 28 ++-- .../models/tg_lasso.json | 52 +++--- .../models/tg_precip_lm.json | 60 +++---- .../models/tg_precip_lm_all_sites.json | 16 +- .../models/tg_randfor.json | 64 ++++---- .../models/tg_tbats.json | 30 ++-- .../models/tg_temp_lm.json | 24 +-- .../models/tg_temp_lm_all_sites.json | 36 ++-- .../models/wbears_gp.json | 2 +- .../models/wbears_rnn.json | 2 +- .../models/xgboost_parallel.json | 32 ++-- .../Daily_Water_temperature/collection.json | 44 ++--- .../Daily_Water_temperature/models/BBTW.json | 2 +- .../Daily_Water_temperature/models/BTW.json | 2 +- .../models/GAM_air_wind.json | 14 +- .../models/GLEON_JRabaey_temp_physics.json | 56 +++---- .../models/GLEON_lm_lag_1day.json | 16 +- .../models/GLEON_physics.json | 2 +- .../models/JorritsCrystalBall.json | 2 +- .../models/LSAMP_AWPC.json | 2 +- .../models/TSLM_seasonal_JM.json | 2 +- .../models/acp_fableLM.json | 2 +- .../models/air2waterSat_2.json | 32 ++-- .../models/baseline_ensemble.json | 54 +++--- .../models/bee_bake_RFModel_2024.json | 22 +-- .../models/cb_prophet.json | 56 +++---- .../models/climatology.json | 38 ++--- .../models/fARIMA_clim_ensemble.json | 18 +- .../models/fTSLM_lag.json | 70 ++++---- .../models/flareGLM.json | 4 +- .../models/flareGLM_noDA.json | 22 +-- .../models/flareGOTM_noDA.json | 18 +- .../models/flareSimstrat_noDA.json | 12 +- .../models/flare_ler.json | 12 +- .../models/flare_ler_baselines.json | 12 +- .../models/hotdeck.json | 4 +- .../models/lm_AT_WTL_WS.json | 22 +-- .../models/mkricheldorf_w_lag.json | 14 +- .../models/mlp1_wtempforecast_LF.json | 4 +- .../models/persistenceRW.json | 4 +- .../models/precip_mod.json | 20 +-- .../models/tg_arima.json | 58 +++---- .../models/tg_ets.json | 38 ++--- .../models/tg_humidity_lm.json | 44 ++--- .../models/tg_humidity_lm_all_sites.json | 20 +-- .../models/tg_lasso.json | 20 +-- .../models/tg_precip_lm.json | 32 ++-- .../models/tg_precip_lm_all_sites.json | 60 +++---- .../models/tg_randfor.json | 28 ++-- .../models/tg_tbats.json | 82 +++++----- .../models/tg_temp_lm.json | 44 ++--- .../models/tg_temp_lm_all_sites.json | 28 ++-- .../models/wbears_gp.json | 2 +- .../models/wbears_rnn.json | 2 +- .../models/wtemp_lm_model.json | 2 +- .../models/xgboost_parallel.json | 72 ++++---- .../models/zimmerman_proj1.json | 14 +- catalog/scores/Aquatics/collection.json | 2 +- .../collection.json | 20 +-- .../models/tg_arima.json | 82 +++++----- .../models/tg_ets.json | 38 ++--- .../models/tg_humidity_lm.json | 2 +- .../models/tg_humidity_lm_all_sites.json | 2 +- .../models/tg_lasso.json | 32 ++-- .../models/tg_precip_lm.json | 2 +- .../models/tg_precip_lm_all_sites.json | 2 +- .../models/tg_randfor.json | 16 +- .../models/tg_tbats.json | 14 +- .../models/tg_temp_lm.json | 24 +-- .../models/tg_temp_lm_all_sites.json | 2 +- .../collection.json | 18 +- .../models/tg_arima.json | 154 +++++++++--------- .../models/tg_ets.json | 130 +++++++-------- .../models/tg_humidity_lm.json | 2 +- .../models/tg_humidity_lm_all_sites.json | 2 +- .../models/tg_lasso.json | 48 +++--- .../models/tg_precip_lm.json | 2 +- .../models/tg_precip_lm_all_sites.json | 2 +- .../models/tg_randfor.json | 2 +- .../models/tg_tbats.json | 42 ++--- .../models/tg_temp_lm.json | 2 +- .../models/tg_temp_lm_all_sites.json | 76 ++++----- catalog/scores/Beetles/collection.json | 2 +- .../collection.json | 32 ++-- .../models/CSP_Gwave.json | 20 +-- .../models/CU_Pheno.json | 2 +- .../models/ChlorophyllCrusaders.json | 2 +- .../models/DALEC_SIP.json | 16 +- .../models/EFI_U_P.json | 2 +- .../models/Fourier.json | 2 +- .../models/NEFIpheno.json | 2 +- .../models/PEG.json | 48 +++--- .../models/PEG_RFR.json | 16 +- .../models/PEG_RFR0.json | 2 +- .../models/UCSC_P_EDM.json | 2 +- .../models/VT_Ph_GDD.json | 2 +- .../models/cb_prophet.json | 64 ++++---- .../models/climatology.json | 78 ++++----- .../models/greenbears.json | 2 +- .../models/persistenceRW.json | 26 +-- .../models/tg_arima.json | 54 +++--- .../models/tg_ets.json | 98 +++++------ .../models/tg_humidity_lm.json | 36 ++-- .../models/tg_humidity_lm_all_sites.json | 36 ++-- .../models/tg_lasso.json | 2 +- .../models/tg_precip_lm.json | 12 +- .../models/tg_precip_lm_all_sites.json | 2 +- .../models/tg_randfor.json | 68 ++++---- .../models/tg_tbats.json | 30 ++-- .../models/tg_temp_lm.json | 44 ++--- .../models/tg_temp_lm_all_sites.json | 72 ++++---- .../models/xgboost_parallel.json | 28 ++-- .../collection.json | 6 +- .../models/PEG.json | 100 ++++++------ .../models/baseline_ensemble.json | 78 ++++----- .../models/cb_prophet.json | 2 +- .../models/climatology.json | 102 ++++++------ .../models/persistenceRW.json | 90 +++++----- .../models/tg_arima.json | 46 +++--- .../models/tg_ets.json | 38 ++--- .../models/tg_humidity_lm.json | 16 +- .../models/tg_humidity_lm_all_sites.json | 68 ++++---- .../models/tg_lasso.json | 60 +++---- .../models/tg_precip_lm.json | 72 ++++---- .../models/tg_precip_lm_all_sites.json | 16 +- .../models/tg_randfor.json | 80 ++++----- .../models/tg_tbats.json | 62 +++---- .../models/tg_temp_lm.json | 24 +-- .../models/tg_temp_lm_all_sites.json | 2 +- .../models/xgboost_parallel.json | 100 ++++++------ catalog/scores/Phenology/collection.json | 2 +- .../collection.json | 4 +- .../models/BU_SIPNET.json | 2 +- .../models/IU_Eco2021.json | 2 +- .../models/UCB_XT.json | 2 +- .../models/VT_NEET.json | 2 +- .../models/cb_prophet.json | 92 +++++------ .../models/climatology.json | 40 ++--- .../models/hist30min.json | 2 +- .../30min_latent_heat_flux/collection.json | 4 +- .../models/BU_SIPNET.json | 2 +- .../models/IU_Eco2021.json | 2 +- .../30min_latent_heat_flux/models/UCB_XT.json | 2 +- .../models/VT_NEET.json | 2 +- .../models/cb_prophet.json | 12 +- .../models/climatology.json | 80 ++++----- .../models/hist30min.json | 2 +- .../collection.json | 24 +-- .../models/USUNEEDAILY.json | 2 +- .../models/bookcast_forest.json | 2 +- .../models/cb_prophet.json | 72 ++++---- .../models/climatology.json | 102 ++++++------ .../models/persistenceRW.json | 42 ++--- .../models/tg_arima.json | 22 +-- .../models/tg_ets.json | 50 +++--- .../models/tg_humidity_lm.json | 76 ++++----- .../models/tg_humidity_lm_all_sites.json | 2 +- .../models/tg_precip_lm.json | 12 +- .../models/tg_precip_lm_all_sites.json | 20 +-- .../models/tg_randfor.json | 12 +- .../models/tg_tbats.json | 86 +++++----- .../models/tg_temp_lm.json | 40 ++--- .../models/tg_temp_lm_all_sites.json | 16 +- .../Daily_latent_heat_flux/collection.json | 16 +- .../models/cb_prophet.json | 2 +- .../models/climatology.json | 70 ++++---- .../models/tg_arima.json | 54 +++--- .../Daily_latent_heat_flux/models/tg_ets.json | 18 +- .../models/tg_humidity_lm.json | 72 ++++---- .../models/tg_humidity_lm_all_sites.json | 96 +++++------ .../models/tg_precip_lm.json | 36 ++-- .../models/tg_precip_lm_all_sites.json | 36 ++-- .../models/tg_randfor.json | 72 ++++---- .../models/tg_tbats.json | 4 +- .../models/tg_temp_lm.json | 12 +- .../models/tg_temp_lm_all_sites.json | 2 +- catalog/scores/Terrestrial/collection.json | 2 +- .../collection.json | 10 +- .../models/BU_Dem.json | 2 +- .../models/NJC_ETS_PF.json | 2 +- .../models/NJC_Ticks.json | 2 +- .../models/TickBench.json | 2 +- .../models/Ticks_288.json | 2 +- .../models/UCLA_2022.json | 2 +- .../models/VTicks.json | 2 +- .../models/tg_arima.json | 24 +-- .../models/tg_ets.json | 28 ++-- .../models/tg_humidity_lm.json | 2 +- .../models/tg_humidity_lm_all_sites.json | 2 +- .../models/tg_lasso.json | 2 +- .../models/tg_precip_lm.json | 2 +- .../models/tg_precip_lm_all_sites.json | 2 +- .../models/tg_randfor.json | 2 +- .../models/tg_tbats.json | 24 +-- .../models/tg_temp_lm.json | 2 +- .../models/tg_temp_lm_all_sites.json | 2 +- catalog/scores/Ticks/collection.json | 2 +- catalog/scores/collection.json | 2 +- 238 files changed, 3137 insertions(+), 3137 deletions(-) diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/collection.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/collection.json index 97fcb233c4..ba5352c00c 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/collection.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/collection.json @@ -16,42 +16,42 @@ { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fTSLM_lag.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/procCTMIMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procHinshelwoodMonod.json" + "href": "./models/procEppleyNorbergSteele.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procHinshelwoodSteele.json" + "href": "./models/procHinshelwoodMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procCTMIMonod.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procBlanchardMonod.json" + "href": "./models/fTSLM_lag.json" }, { "rel": "item", @@ -61,7 +61,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/procEppleyNorbergSteele.json" + "href": "./models/procBlanchardMonod.json" }, { "rel": "item", @@ -71,57 +71,57 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/procHinshelwoodSteele.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/xgboost_parallel.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/xgboost_parallel.json" }, { "rel": "parent", @@ -166,7 +166,7 @@ "interval": [ [ "2022-01-01T00:00:00Z", - "2024-07-21T00:00:00Z" + "2024-07-24T00:00:00Z" ] ] } diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json index 150df1dfa9..a5f8cd352b 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json @@ -17,7 +17,7 @@ "properties": { "title": "USGSHABs1", "description": "All scores for the Daily_Chlorophyll_a variable for the USGSHABs1 model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BLWA, FLNT, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-09-01T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json index 8285bc8f03..4973a6c429 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json @@ -9,25 +9,25 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-84.4374, 31.1854], + [-88.1589, 31.8534], [-82.0084, 29.676], - [-105.5442, 40.035], + [-87.7982, 32.5415], + [-82.0177, 29.6878], [-89.4737, 46.2097], - [-96.443, 38.9459], - [-78.1473, 38.8943], - [-149.6106, 68.6307], - [-99.1139, 47.1591], [-89.7048, 45.9983], [-99.2531, 47.1298], - [-87.7982, 32.5415], - [-84.4374, 31.1854], - [-82.0177, 29.6878], - [-88.1589, 31.8534] + [-99.1139, 47.1591], + [-149.6106, 68.6307], + [-105.5442, 40.035], + [-96.443, 38.9459], + [-78.1473, 38.8943] ] }, "properties": { "title": "cb_prophet", - "description": "All scores for the Daily_Chlorophyll_a variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: BARC, COMO, CRAM, MCDI, POSE, TOOK, PRLA, LIRO, PRPO, BLWA, FLNT, SUGG, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: FLNT, TOMB, BARC, BLWA, SUGG, CRAM, LIRO, PRPO, PRLA, TOOK, COMO, MCDI, POSE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-06-01T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ @@ -58,19 +58,19 @@ "chla", "Daily", "P1D", + "FLNT", + "TOMB", "BARC", - "COMO", + "BLWA", + "SUGG", "CRAM", - "MCDI", - "POSE", - "TOOK", - "PRLA", "LIRO", "PRPO", - "BLWA", - "FLNT", - "SUGG", - "TOMB" + "PRLA", + "TOOK", + "COMO", + "MCDI", + "POSE" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json index 8a68503707..68e43d160e 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json @@ -9,27 +9,27 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-82.0177, 29.6878], [-82.0084, 29.676], [-87.7982, 32.5415], + [-89.4737, 46.2097], [-84.4374, 31.1854], - [-88.1589, 31.8534], [-89.7048, 45.9983], - [-89.4737, 46.2097], - [-99.2531, 47.1298], [-99.1139, 47.1591], + [-99.2531, 47.1298], + [-82.0177, 29.6878], + [-88.1589, 31.8534], + [-149.6106, 68.6307], [-105.5442, 40.035], [-96.443, 38.9459], - [-78.1473, 38.8943], - [-149.6106, 68.6307] + [-78.1473, 38.8943] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Chlorophyll_a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SUGG, BARC, BLWA, FLNT, TOMB, USGS-01427510, USGS-01463500, USGS-05543010, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720, LIRO, CRAM, PRPO, PRLA, COMO, MCDI, POSE, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, COMO, MCDI, POSE, USGS-01427510, USGS-01463500, USGS-05543010, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-01-01T00:00:00Z", - "end_datetime": "2024-07-21T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", @@ -58,11 +58,19 @@ "chla", "Daily", "P1D", - "SUGG", "BARC", "BLWA", + "CRAM", "FLNT", + "LIRO", + "PRLA", + "PRPO", + "SUGG", "TOMB", + "TOOK", + "COMO", + "MCDI", + "POSE", "USGS-01427510", "USGS-01463500", "USGS-05543010", @@ -71,15 +79,7 @@ "USGS-05586300", "USGS-14181500", "USGS-14211010", - "USGS-14211720", - "LIRO", - "CRAM", - "PRPO", - "PRLA", - "COMO", - "MCDI", - "POSE", - "TOOK" + "USGS-14211720" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/fTSLM_lag.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/fTSLM_lag.json index 75aca4df0e..0b2480f073 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/fTSLM_lag.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/fTSLM_lag.json @@ -17,7 +17,7 @@ "properties": { "title": "fTSLM_lag", "description": "All scores for the Daily_Chlorophyll_a variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day’s air temperature.\n The model predicts this variable at the following sites: BLWA, FLNT, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-11-22T00:00:00Z", "end_datetime": "2023-01-07T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json index 782b290743..45943bbfa9 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json @@ -24,9 +24,9 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Chlorophyll_a variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-08-25T00:00:00Z", - "end_datetime": "2024-07-21T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json index 18e2323266..f08d4037ea 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307], [-82.0084, 29.676], [-89.4737, 46.2097], [-89.7048, 45.9983], - [-99.1139, 47.1591] + [-99.1139, 47.1591], + [-99.2531, 47.1298], + [-82.0177, 29.6878], + [-149.6106, 68.6307] ] }, "properties": { "title": "procBlanchardMonod", - "description": "All scores for the Daily_Chlorophyll_a variable for the procBlanchardMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: PRPO, SUGG, TOOK, BARC, CRAM, LIRO, PRLA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the procBlanchardMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ @@ -52,13 +52,13 @@ "chla", "Daily", "P1D", - "PRPO", - "SUGG", - "TOOK", "BARC", "CRAM", "LIRO", - "PRLA" + "PRLA", + "PRPO", + "SUGG", + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json index 869e06d814..443626abb9 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0177, 29.6878], [-149.6106, 68.6307], [-82.0084, 29.676], [-89.4737, 46.2097], - [-89.7048, 45.9983] + [-89.7048, 45.9983], + [-99.1139, 47.1591] ] }, "properties": { "title": "procCTMIMonod", - "description": "All scores for the Daily_Chlorophyll_a variable for the procCTMIMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: PRLA, PRPO, SUGG, TOOK, BARC, CRAM, LIRO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the procCTMIMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: PRPO, SUGG, TOOK, BARC, CRAM, LIRO, PRLA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ @@ -52,13 +52,13 @@ "chla", "Daily", "P1D", - "PRLA", "PRPO", "SUGG", "TOOK", "BARC", "CRAM", - "LIRO" + "LIRO", + "PRLA" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json index ed1da782fe..cb58ed2c9e 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], [-82.0177, 29.6878], [-149.6106, 68.6307], [-82.0084, 29.676], - [-89.4737, 46.2097] + [-89.4737, 46.2097], + [-89.7048, 45.9983], + [-99.1139, 47.1591], + [-99.2531, 47.1298] ] }, "properties": { "title": "procEppleyNorbergMonod", - "description": "All scores for the Daily_Chlorophyll_a variable for the procEppleyNorbergMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: LIRO, PRLA, PRPO, SUGG, TOOK, BARC, CRAM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the procEppleyNorbergMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: SUGG, TOOK, BARC, CRAM, LIRO, PRLA, PRPO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ @@ -52,13 +52,13 @@ "chla", "Daily", "P1D", - "LIRO", - "PRLA", - "PRPO", "SUGG", "TOOK", "BARC", - "CRAM" + "CRAM", + "LIRO", + "PRLA", + "PRPO" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json index 016ccf53f0..5963dc6acf 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-149.6106, 68.6307], - [-82.0084, 29.676], [-89.4737, 46.2097], [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], - [-82.0177, 29.6878] + [-82.0177, 29.6878], + [-149.6106, 68.6307], + [-82.0084, 29.676] ] }, "properties": { "title": "procEppleyNorbergSteele", - "description": "All scores for the Daily_Chlorophyll_a variable for the procEppleyNorbergSteele model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: TOOK, BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the procEppleyNorbergSteele model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: CRAM, LIRO, PRLA, PRPO, SUGG, TOOK, BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ @@ -52,13 +52,13 @@ "chla", "Daily", "P1D", - "TOOK", - "BARC", "CRAM", "LIRO", "PRLA", "PRPO", - "SUGG" + "SUGG", + "TOOK", + "BARC" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json index 0c61ec179d..52c3a077b0 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-82.0084, 29.676], + [-89.4737, 46.2097], + [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0177, 29.6878], - [-149.6106, 68.6307], - [-82.0084, 29.676], - [-89.4737, 46.2097], - [-89.7048, 45.9983] + [-149.6106, 68.6307] ] }, "properties": { "title": "procHinshelwoodMonod", - "description": "All scores for the Daily_Chlorophyll_a variable for the procHinshelwoodMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: PRLA, PRPO, SUGG, TOOK, BARC, CRAM, LIRO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the procHinshelwoodMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ @@ -52,13 +52,13 @@ "chla", "Daily", "P1D", + "BARC", + "CRAM", + "LIRO", "PRLA", "PRPO", "SUGG", - "TOOK", - "BARC", - "CRAM", - "LIRO" + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json index a1ad00ede9..b57e9ba826 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json @@ -21,7 +21,7 @@ "properties": { "title": "procHinshelwoodSteele", "description": "All scores for the Daily_Chlorophyll_a variable for the procHinshelwoodSteele model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json index 34e74d4e4d..8f8cd9aba3 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json @@ -9,24 +9,24 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-89.7048, 45.9983], + [-99.1139, 47.1591], + [-99.2531, 47.1298], [-149.6106, 68.6307], + [-89.4737, 46.2097], [-82.0084, 29.676], [-87.7982, 32.5415], - [-89.4737, 46.2097], [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], [-82.0177, 29.6878], [-88.1589, 31.8534] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Chlorophyll_a variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: LIRO, PRLA, PRPO, TOOK, CRAM, BARC, BLWA, FLNT, SUGG, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", - "end_datetime": "2024-07-21T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -55,14 +55,14 @@ "chla", "Daily", "P1D", + "LIRO", + "PRLA", + "PRPO", "TOOK", + "CRAM", "BARC", "BLWA", - "CRAM", "FLNT", - "LIRO", - "PRLA", - "PRPO", "SUGG", "TOMB" ], diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json index 20f3e7b9b9..5446d94386 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json @@ -24,9 +24,9 @@ "properties": { "title": "tg_ets", "description": "All scores for the Daily_Chlorophyll_a variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: PRLA, PRPO, SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT, LIRO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", - "end_datetime": "2024-07-21T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json index 71d0c91adf..323f9920af 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json @@ -9,6 +9,7 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-82.0084, 29.676], [-87.7982, 32.5415], [-89.4737, 46.2097], [-84.4374, 31.1854], @@ -17,14 +18,13 @@ [-99.2531, 47.1298], [-82.0177, 29.6878], [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-82.0084, 29.676] + [-149.6106, 68.6307] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All scores for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -55,6 +55,7 @@ "chla", "Daily", "P1D", + "BARC", "BLWA", "CRAM", "FLNT", @@ -63,8 +64,7 @@ "PRPO", "SUGG", "TOMB", - "TOOK", - "BARC" + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json index fe7bd8c2ec..fd6d029076 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json @@ -9,22 +9,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-82.0177, 29.6878], - [-88.1589, 31.8534], - [-149.6106, 68.6307], [-82.0084, 29.676], [-87.7982, 32.5415], [-89.4737, 46.2097], [-84.4374, 31.1854], [-89.7048, 45.9983], [-99.1139, 47.1591], - [-99.2531, 47.1298] + [-99.2531, 47.1298], + [-82.0177, 29.6878], + [-88.1589, 31.8534], + [-149.6106, 68.6307] ] }, "properties": { "title": "tg_humidity_lm_all_sites", - "description": "All scores for the Daily_Chlorophyll_a variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -55,16 +55,16 @@ "chla", "Daily", "P1D", - "SUGG", - "TOMB", - "TOOK", "BARC", "BLWA", "CRAM", "FLNT", "LIRO", "PRLA", - "PRPO" + "PRPO", + "SUGG", + "TOMB", + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json index 83e74c728f..f2f8d971d8 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json @@ -9,22 +9,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854], - [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0177, 29.6878], [-88.1589, 31.8534], - [-149.6106, 68.6307] + [-149.6106, 68.6307], + [-82.0084, 29.676], + [-87.7982, 32.5415], + [-89.4737, 46.2097], + [-84.4374, 31.1854], + [-89.7048, 45.9983] ] }, "properties": { "title": "tg_lasso", - "description": "All scores for the Daily_Chlorophyll_a variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: PRLA, PRPO, SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT, LIRO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -55,16 +55,16 @@ "chla", "Daily", "P1D", - "BARC", - "BLWA", - "CRAM", - "FLNT", - "LIRO", "PRLA", "PRPO", "SUGG", "TOMB", - "TOOK" + "TOOK", + "BARC", + "BLWA", + "CRAM", + "FLNT", + "LIRO" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json index ccac147a80..3cb063a9a5 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json @@ -9,22 +9,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-82.0084, 29.676], + [-87.7982, 32.5415], + [-89.4737, 46.2097], + [-84.4374, 31.1854], [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0177, 29.6878], [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-82.0084, 29.676], - [-87.7982, 32.5415], - [-89.4737, 46.2097], - [-84.4374, 31.1854] + [-149.6106, 68.6307] ] }, "properties": { "title": "tg_precip_lm", - "description": "All scores for the Daily_Chlorophyll_a variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -55,16 +55,16 @@ "chla", "Daily", "P1D", + "BARC", + "BLWA", + "CRAM", + "FLNT", "LIRO", "PRLA", "PRPO", "SUGG", "TOMB", - "TOOK", - "BARC", - "BLWA", - "CRAM", - "FLNT" + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json index 1859e8ebeb..28abd0e0ee 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json @@ -9,8 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-89.4737, 46.2097], - [-84.4374, 31.1854], [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], @@ -18,13 +16,15 @@ [-88.1589, 31.8534], [-149.6106, 68.6307], [-82.0084, 29.676], - [-87.7982, 32.5415] + [-87.7982, 32.5415], + [-89.4737, 46.2097], + [-84.4374, 31.1854] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All scores for the Daily_Chlorophyll_a variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -55,8 +55,6 @@ "chla", "Daily", "P1D", - "CRAM", - "FLNT", "LIRO", "PRLA", "PRPO", @@ -64,7 +62,9 @@ "TOMB", "TOOK", "BARC", - "BLWA" + "BLWA", + "CRAM", + "FLNT" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json index 1d87117ccd..4281396fd9 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_randfor", "description": "All scores for the Daily_Chlorophyll_a variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: PRLA, PRPO, SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT, LIRO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json index 131ed62930..74df9af72f 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json @@ -9,24 +9,24 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-84.4374, 31.1854], [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], [-149.6106, 68.6307], + [-89.4737, 46.2097], [-82.0084, 29.676], [-87.7982, 32.5415], - [-89.4737, 46.2097] + [-84.4374, 31.1854], + [-82.0177, 29.6878], + [-88.1589, 31.8534] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC, BLWA, CRAM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: LIRO, PRLA, PRPO, TOOK, CRAM, BARC, BLWA, FLNT, SUGG, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", - "end_datetime": "2024-07-21T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -55,16 +55,16 @@ "chla", "Daily", "P1D", - "FLNT", "LIRO", "PRLA", "PRPO", - "SUGG", - "TOMB", "TOOK", + "CRAM", "BARC", "BLWA", - "CRAM" + "FLNT", + "SUGG", + "TOMB" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json index 8240a43ecf..ccc2096e98 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json @@ -10,21 +10,21 @@ "type": "MultiPoint", "coordinates": [ [-149.6106, 68.6307], + [-99.1139, 47.1591], + [-89.7048, 45.9983], + [-99.2531, 47.1298], + [-89.4737, 46.2097], [-82.0084, 29.676], [-87.7982, 32.5415], - [-89.4737, 46.2097], [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], [-82.0177, 29.6878], [-88.1589, 31.8534] ] }, "properties": { "title": "tg_temp_lm", - "description": "All scores for the Daily_Chlorophyll_a variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: TOOK, PRLA, LIRO, PRPO, CRAM, BARC, BLWA, FLNT, SUGG, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -56,13 +56,13 @@ "Daily", "P1D", "TOOK", + "PRLA", + "LIRO", + "PRPO", + "CRAM", "BARC", "BLWA", - "CRAM", "FLNT", - "LIRO", - "PRLA", - "PRPO", "SUGG", "TOMB" ], diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json index be5c5a587d..5e38f357c0 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All scores for the Daily_Chlorophyll_a variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/xgboost_parallel.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/xgboost_parallel.json index 34ef8cdb07..892da16672 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/xgboost_parallel.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/xgboost_parallel.json @@ -9,22 +9,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-82.0084, 29.676], [-87.7982, 32.5415], - [-89.4737, 46.2097], [-84.4374, 31.1854], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], [-82.0177, 29.6878], [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-82.0084, 29.676] + [-89.4737, 46.2097], + [-89.7048, 45.9983], + [-99.2531, 47.1298], + [-99.1139, 47.1591], + [-149.6106, 68.6307] ] }, "properties": { "title": "xgboost_parallel", - "description": "All scores for the Daily_Chlorophyll_a variable for the xgboost_parallel model. Information for the model is provided as follows: The XGBoost model is an extreme gradient boosted random forest (XGBoost) machine learning\nmodel that uses predicted atmospheric conditions and day of year as covariates. This model utilises the\nxgboost R package (Chen & Guestrin 2016; Chen et al., 2023)..\n The model predicts this variable at the following sites: BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the xgboost_parallel model. Information for the model is provided as follows: The XGBoost model is an extreme gradient boosted random forest (XGBoost) machine learning\nmodel that uses predicted atmospheric conditions and day of year as covariates. This model utilises the\nxgboost R package (Chen & Guestrin 2016; Chen et al., 2023)..\n The model predicts this variable at the following sites: BARC, BLWA, FLNT, SUGG, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2023-12-08T00:00:00Z", "providers": [ @@ -55,16 +55,16 @@ "chla", "Daily", "P1D", + "BARC", "BLWA", - "CRAM", "FLNT", - "LIRO", - "PRLA", - "PRPO", "SUGG", "TOMB", - "TOOK", - "BARC" + "CRAM", + "LIRO", + "PRPO", + "PRLA", + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json index 20c6725e30..0ac4fde50d 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json @@ -16,52 +16,52 @@ { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/AquaticEcosystemsOxygen.json" }, { "rel": "item", "type": "application/json", - "href": "./models/LSAMP_AWPC.json" + "href": "./models/BBTW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/MSU_ARIMA.json" + "href": "./models/BTW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/NDWaterTempDO.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/LSAMP_AWPC.json" }, { "rel": "item", "type": "application/json", - "href": "./models/AquaticEcosystemsOxygen.json" + "href": "./models/MSU_ARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/BBTW.json" + "href": "./models/NDWaterTempDO.json" }, { "rel": "item", "type": "application/json", - "href": "./models/BTW.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/hotdeck.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/hotdeck.json" }, { "rel": "item", @@ -181,7 +181,7 @@ "interval": [ [ "2017-08-27T00:00:00Z", - "2024-07-20T00:00:00Z" + "2024-07-24T00:00:00Z" ] ] } diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json index 969713e5fe..1d4a164e7c 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json @@ -17,9 +17,9 @@ "properties": { "title": "AquaticEcosystemsOxygen", "description": "All scores for the Daily_Dissolved_oxygen variable for the AquaticEcosystemsOxygen model. Information for the model is provided as follows: Used a Bayesian Dynamic Linear Model using the fit_dlm function from the ecoforecastR package.\n The model predicts this variable at the following sites: BARC, ARIK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2024-04-03T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "pending", diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/BBTW.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/BBTW.json index ee2f2c68c7..45194db555 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/BBTW.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/BBTW.json @@ -15,7 +15,7 @@ "properties": { "title": "BBTW", "description": "All scores for the Daily_Dissolved_oxygen variable for the BBTW model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-05-01T00:00:00Z", "end_datetime": "2021-05-08T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/BTW.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/BTW.json index f705a89303..93b233adfd 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/BTW.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/BTW.json @@ -15,7 +15,7 @@ "properties": { "title": "BTW", "description": "All scores for the Daily_Dissolved_oxygen variable for the BTW model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: POSE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-05-01T00:00:00Z", "end_datetime": "2021-05-08T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json index 4fe8dc031a..1c2677813d 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-82.0084, 29.676], + [-89.4737, 46.2097], [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0177, 29.6878], - [-149.6106, 68.6307], - [-82.0084, 29.676], - [-89.4737, 46.2097] + [-149.6106, 68.6307] ] }, "properties": { "title": "GLEON_lm_lag_1day", - "description": "All scores for the Daily_Dissolved_oxygen variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: LIRO, PRLA, PRPO, SUGG, TOOK, BARC, CRAM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-11-01T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ @@ -52,13 +52,13 @@ "oxygen", "Daily", "P1D", + "BARC", + "CRAM", "LIRO", "PRLA", "PRPO", "SUGG", - "TOOK", - "BARC", - "CRAM" + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/LSAMP_AWPC.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/LSAMP_AWPC.json index b287c6254c..8d752ba152 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/LSAMP_AWPC.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/LSAMP_AWPC.json @@ -15,7 +15,7 @@ "properties": { "title": "LSAMP_AWPC", "description": "All scores for the Daily_Dissolved_oxygen variable for the LSAMP_AWPC model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-07-02T00:00:00Z", "end_datetime": "2021-08-07T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/MSU_ARIMA.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/MSU_ARIMA.json index bad91d69b1..2fb548da6b 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/MSU_ARIMA.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/MSU_ARIMA.json @@ -15,7 +15,7 @@ "properties": { "title": "MSU_ARIMA", "description": "All scores for the Daily_Dissolved_oxygen variable for the MSU_ARIMA model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: POSE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-07-01T00:00:00Z", "end_datetime": "2021-07-07T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/NDWaterTempDO.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/NDWaterTempDO.json index fa2947a39b..454f21d681 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/NDWaterTempDO.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/NDWaterTempDO.json @@ -15,7 +15,7 @@ "properties": { "title": "NDWaterTempDO", "description": "All scores for the Daily_Dissolved_oxygen variable for the NDWaterTempDO model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2017-08-27T00:00:00Z", "end_datetime": "2023-01-15T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json index 568167acd7..08f6261b09 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json @@ -9,10 +9,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-97.7823, 33.3785], + [-99.1139, 47.1591], + [-99.2531, 47.1298], + [-111.7979, 40.7839], + [-82.0177, 29.6878], + [-111.5081, 33.751], + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], [-110.5871, 44.9501], + [-96.6242, 34.4442], [-87.7982, 32.5415], [-147.504, 65.1532], [-105.5442, 40.035], @@ -30,25 +42,13 @@ [-96.443, 38.9459], [-122.1655, 44.2596], [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-96.6242, 34.4442] + [-78.1473, 38.8943] ] }, "properties": { "title": "air2waterSat_2", - "description": "All scores for the Daily_Dissolved_oxygen variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, BLUE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,10 +79,22 @@ "oxygen", "Daily", "P1D", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", "BLDE", + "BLUE", "BLWA", "CARI", "COMO", @@ -100,19 +112,7 @@ "MCDI", "MCRA", "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", - "BLUE" + "POSE" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json index 7ad2471490..f30807e552 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json @@ -9,7 +9,23 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-82.0084, 29.676], [-105.5442, 40.035], + [-89.4737, 46.2097], + [-96.443, 38.9459], + [-78.1473, 38.8943], + [-149.143, 68.6698], + [-149.6106, 68.6307], + [-99.1139, 47.1591], + [-89.7048, 45.9983], + [-99.2531, 47.1298], + [-111.5081, 33.751], + [-102.4471, 39.7582], + [-119.2575, 37.0597], + [-110.5871, 44.9501], + [-96.6242, 34.4442], + [-87.7982, 32.5415], + [-147.504, 65.1532], [-66.9868, 18.1135], [-84.4374, 31.1854], [-66.7987, 18.1741], @@ -19,36 +35,20 @@ [-77.9832, 39.0956], [-121.9338, 45.7908], [-87.4077, 32.9604], - [-96.443, 38.9459], [-122.1655, 44.2596], - [-78.1473, 38.8943], [-97.7823, 33.3785], [-111.7979, 40.7839], [-82.0177, 29.6878], - [-111.5081, 33.751], [-119.0274, 36.9559], [-88.1589, 31.8534], [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-149.143, 68.6698], - [-149.6106, 68.6307] + [-105.9154, 39.8914] ] }, "properties": { "title": "cb_prophet", - "description": "All scores for the Daily_Dissolved_oxygen variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, CRAM, LIRO, PRLA, PRPO, OKSR, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: BARC, COMO, CRAM, MCDI, POSE, OKSR, TOOK, PRLA, LIRO, PRPO, SYCA, ARIK, BIGC, BLDE, BLUE, BLWA, CARI, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCRA, PRIN, REDB, SUGG, TECR, TOMB, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-06-01T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ @@ -79,7 +79,23 @@ "oxygen", "Daily", "P1D", + "BARC", "COMO", + "CRAM", + "MCDI", + "POSE", + "OKSR", + "TOOK", + "PRLA", + "LIRO", + "PRPO", + "SYCA", + "ARIK", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", "CUPE", "FLNT", "GUIL", @@ -89,30 +105,14 @@ "LEWI", "MART", "MAYF", - "MCDI", "MCRA", - "POSE", "PRIN", "REDB", "SUGG", - "SYCA", "TECR", "TOMB", "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "CRAM", - "LIRO", - "PRLA", - "PRPO", - "OKSR", - "TOOK" + "WLOU" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json index 18a1d7fcef..6f73af1eb6 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json @@ -9,10 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-84.2793, 35.9574], [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], @@ -37,20 +33,24 @@ [-122.1655, 44.2596], [-78.1473, 38.8943], [-97.7823, 33.3785], + [-99.1139, 47.1591], [-99.2531, 47.1298], [-111.7979, 40.7839], [-82.0177, 29.6878], - [-99.1139, 47.1591], + [-111.5081, 33.751], + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-84.2793, 35.9574], [-149.143, 68.6698], [-149.6106, 68.6307] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRPO, REDB, SUGG, PRLA, OKSR, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, OKSR, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-05-01T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", @@ -79,10 +79,6 @@ "oxygen", "Daily", "P1D", - "SYCA", - "TECR", - "TOMB", - "WALK", "WLOU", "ARIK", "BARC", @@ -107,10 +103,14 @@ "MCRA", "POSE", "PRIN", + "PRLA", "PRPO", "REDB", "SUGG", - "PRLA", + "SYCA", + "TECR", + "TOMB", + "WALK", "OKSR", "TOOK" ], diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json index 95582c4868..3c9a2241db 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json @@ -9,6 +9,11 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-82.0177, 29.6878], + [-111.5081, 33.751], + [-82.0084, 29.676], + [-119.2575, 37.0597], + [-110.5871, 44.9501], [-89.4737, 46.2097], [-96.6038, 39.1051], [-77.9832, 39.0956], @@ -17,20 +22,15 @@ [-122.1655, 44.2596], [-78.1473, 38.8943], [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501] + [-111.7979, 40.7839] ] }, "properties": { "title": "hotdeck", - "description": "All scores for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: CRAM, KING, LEWI, LIRO, MAYF, MCRA, POSE, PRIN, REDB, SUGG, SYCA, BARC, BIGC, BLDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: SUGG, SYCA, BARC, BIGC, BLDE, CRAM, KING, LEWI, LIRO, MAYF, MCRA, POSE, PRIN, REDB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2024-04-05T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/swpease/hotdeckfc", @@ -59,6 +59,11 @@ "oxygen", "Daily", "P1D", + "SUGG", + "SYCA", + "BARC", + "BIGC", + "BLDE", "CRAM", "KING", "LEWI", @@ -67,12 +72,7 @@ "MCRA", "POSE", "PRIN", - "REDB", - "SUGG", - "SYCA", - "BARC", - "BIGC", - "BLDE" + "REDB" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json index df085763cd..e34d1886a9 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json @@ -9,21 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], [-77.9832, 39.0956], [-89.7048, 45.9983], [-121.9338, 45.7908], @@ -42,15 +27,30 @@ [-88.1589, 31.8534], [-149.6106, 68.6307], [-84.2793, 35.9574], - [-105.9154, 39.8914] + [-105.9154, 39.8914], + [-102.4471, 39.7582], + [-82.0084, 29.676], + [-119.2575, 37.0597], + [-110.5871, 44.9501], + [-96.6242, 34.4442], + [-87.7982, 32.5415], + [-147.504, 65.1532], + [-105.5442, 40.035], + [-89.4737, 46.2097], + [-66.9868, 18.1135], + [-84.4374, 31.1854], + [-66.7987, 18.1741], + [-72.3295, 42.4719], + [-96.6038, 39.1051], + [-83.5038, 35.6904] ] }, "properties": { "title": "persistenceRW", - "description": "All scores for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-08-25T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", @@ -79,21 +79,6 @@ "oxygen", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "LECO", "LEWI", "LIRO", "MART", @@ -112,7 +97,22 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING", + "LECO" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json index ce611a4ed7..5ca28ee38b 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json @@ -9,6 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914], + [-102.4471, 39.7582], + [-82.0084, 29.676], + [-119.2575, 37.0597], + [-110.5871, 44.9501], + [-96.6242, 34.4442], + [-87.7982, 32.5415], + [-147.504, 65.1532], + [-105.5442, 40.035], [-89.4737, 46.2097], [-66.9868, 18.1135], [-84.4374, 31.1854], @@ -29,28 +42,15 @@ [-99.2531, 47.1298], [-111.7979, 40.7839], [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035] + [-111.5081, 33.751] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -79,6 +79,19 @@ "oxygen", "Daily", "P1D", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", "CRAM", "CUPE", "FLNT", @@ -99,20 +112,7 @@ "PRPO", "REDB", "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO" + "SYCA" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json index 7dfec4d1f5..bdd684caea 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json @@ -9,24 +9,11 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-149.143, 68.6698], - [-149.6106, 68.6307], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], [-82.0177, 29.6878], [-111.5081, 33.751], [-119.0274, 36.9559], [-88.1589, 31.8534], + [-149.6106, 68.6307], [-84.2793, 35.9574], [-105.9154, 39.8914], [-102.4471, 39.7582], @@ -42,15 +29,28 @@ [-84.4374, 31.1854], [-66.7987, 18.1741], [-72.3295, 42.4719], - [-96.6038, 39.1051] + [-96.6038, 39.1051], + [-83.5038, 35.6904], + [-77.9832, 39.0956], + [-89.7048, 45.9983], + [-121.9338, 45.7908], + [-87.4077, 32.9604], + [-96.443, 38.9459], + [-122.1655, 44.2596], + [-149.143, 68.6698], + [-78.1473, 38.8943], + [-97.7823, 33.3785], + [-99.1139, 47.1591], + [-99.2531, 47.1298], + [-111.7979, 40.7839] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: OKSR, TOOK, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -79,24 +79,11 @@ "oxygen", "Daily", "P1D", - "OKSR", - "TOOK", - "LECO", - "LEWI", - "LIRO", - "MART", - "MAYF", - "MCDI", - "MCRA", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", "SUGG", "SYCA", "TECR", "TOMB", + "TOOK", "WALK", "WLOU", "ARIK", @@ -112,7 +99,20 @@ "FLNT", "GUIL", "HOPB", - "KING" + "KING", + "LECO", + "LEWI", + "LIRO", + "MART", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json index e88845a39e..6896ce572e 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json @@ -9,9 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], [-105.5442, 40.035], [-89.4737, 46.2097], [-66.9868, 18.1135], @@ -42,13 +39,16 @@ [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], - [-110.5871, 44.9501] + [-110.5871, 44.9501], + [-96.6242, 34.4442], + [-87.7982, 32.5415], + [-147.504, 65.1532] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,9 +79,6 @@ "oxygen", "Daily", "P1D", - "BLUE", - "BLWA", - "CARI", "COMO", "CRAM", "CUPE", @@ -112,7 +109,10 @@ "ARIK", "BARC", "BIGC", - "BLDE" + "BLDE", + "BLUE", + "BLWA", + "CARI" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json index 5b867174f6..a6f3b5c07e 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json @@ -9,6 +9,11 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], @@ -37,18 +42,13 @@ [-99.2531, 47.1298], [-111.7979, 40.7839], [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] + [-111.5081, 33.751] ] }, "properties": { "title": "tg_humidity_lm_all_sites", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,6 +79,11 @@ "oxygen", "Daily", "P1D", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -107,12 +112,7 @@ "PRPO", "REDB", "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "SYCA" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json index 28a0a1cc59..083513d02f 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json @@ -9,17 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], [-87.7982, 32.5415], [-147.504, 65.1532], [-105.5442, 40.035], @@ -42,13 +31,24 @@ [-99.1139, 47.1591], [-99.2531, 47.1298], [-111.7979, 40.7839], - [-82.0177, 29.6878] + [-82.0177, 29.6878], + [-111.5081, 33.751], + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914], + [-102.4471, 39.7582], + [-82.0084, 29.676], + [-119.2575, 37.0597], + [-110.5871, 44.9501], + [-96.6242, 34.4442] ] }, "properties": { "title": "tg_lasso", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -79,17 +79,6 @@ "oxygen", "Daily", "P1D", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", "BLWA", "CARI", "COMO", @@ -112,7 +101,18 @@ "PRLA", "PRPO", "REDB", - "SUGG" + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json index 53a844fd6f..77b99f43a8 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json @@ -9,6 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-66.7987, 18.1741], + [-72.3295, 42.4719], + [-96.6038, 39.1051], + [-83.5038, 35.6904], + [-77.9832, 39.0956], + [-89.7048, 45.9983], + [-121.9338, 45.7908], + [-87.4077, 32.9604], + [-96.443, 38.9459], + [-122.1655, 44.2596], + [-149.143, 68.6698], + [-78.1473, 38.8943], + [-97.7823, 33.3785], [-99.1139, 47.1591], [-99.2531, 47.1298], [-111.7979, 40.7839], @@ -29,26 +42,13 @@ [-105.5442, 40.035], [-89.4737, 46.2097], [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785] + [-84.4374, 31.1854] ] }, "properties": { "title": "tg_precip_lm", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,6 +79,19 @@ "oxygen", "Daily", "P1D", + "GUIL", + "HOPB", + "KING", + "LECO", + "LEWI", + "LIRO", + "MART", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", "PRLA", "PRPO", "REDB", @@ -99,20 +112,7 @@ "COMO", "CRAM", "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "LECO", - "LEWI", - "LIRO", - "MART", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN" + "FLNT" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json index 0cedd08c19..fe8a8b7e96 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json @@ -9,6 +9,8 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-102.4471, 39.7582], + [-82.0084, 29.676], [-119.2575, 37.0597], [-110.5871, 44.9501], [-96.6242, 34.4442], @@ -40,15 +42,13 @@ [-88.1589, 31.8534], [-149.6106, 68.6307], [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676] + [-105.9154, 39.8914] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,6 +79,8 @@ "oxygen", "Daily", "P1D", + "ARIK", + "BARC", "BIGC", "BLDE", "BLUE", @@ -110,9 +112,7 @@ "TOMB", "TOOK", "WALK", - "WLOU", - "ARIK", - "BARC" + "WLOU" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json index 749109f0d3..6e1a2e37c3 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json @@ -9,6 +9,20 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-102.4471, 39.7582], + [-82.0084, 29.676], + [-119.2575, 37.0597], + [-110.5871, 44.9501], + [-96.6242, 34.4442], + [-87.7982, 32.5415], + [-147.504, 65.1532], + [-105.5442, 40.035], + [-89.4737, 46.2097], + [-66.9868, 18.1135], + [-84.4374, 31.1854], + [-66.7987, 18.1741], + [-72.3295, 42.4719], + [-96.6038, 39.1051], [-83.5038, 35.6904], [-77.9832, 39.0956], [-89.7048, 45.9983], @@ -28,27 +42,13 @@ [-88.1589, 31.8534], [-149.6106, 68.6307], [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051] + [-105.9154, 39.8914] ] }, "properties": { "title": "tg_randfor", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -79,6 +79,20 @@ "oxygen", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING", "LECO", "LEWI", "LIRO", @@ -98,21 +112,7 @@ "TOMB", "TOOK", "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING" + "WLOU" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json index 63f7e434b3..e44a6c8e98 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json @@ -9,11 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], @@ -42,15 +37,20 @@ [-99.1139, 47.1591], [-99.2531, 47.1298], [-111.7979, 40.7839], - [-82.0177, 29.6878] + [-82.0177, 29.6878], + [-111.5081, 33.751], + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-84.2793, 35.9574] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -79,11 +79,6 @@ "oxygen", "Daily", "P1D", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", "WLOU", "ARIK", "BARC", @@ -112,7 +107,12 @@ "PRLA", "PRPO", "REDB", - "SUGG" + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json index ae9a91de27..173a4b436c 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json @@ -9,6 +9,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-111.5081, 33.751], + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-149.6106, 68.6307], [-84.2793, 35.9574], [-105.9154, 39.8914], [-102.4471, 39.7582], @@ -38,17 +42,13 @@ [-99.1139, 47.1591], [-99.2531, 47.1298], [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307] + [-82.0177, 29.6878] ] }, "properties": { "title": "tg_temp_lm", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,6 +79,10 @@ "oxygen", "Daily", "P1D", + "SYCA", + "TECR", + "TOMB", + "TOOK", "WALK", "WLOU", "ARIK", @@ -108,11 +112,7 @@ "PRLA", "PRPO", "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK" + "SUGG" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json index ce00f17c14..f8b2366c7d 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json @@ -9,6 +9,13 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-147.504, 65.1532], + [-105.5442, 40.035], + [-89.4737, 46.2097], + [-66.9868, 18.1135], + [-84.4374, 31.1854], + [-66.7987, 18.1741], + [-72.3295, 42.4719], [-96.6038, 39.1051], [-83.5038, 35.6904], [-77.9832, 39.0956], @@ -35,20 +42,13 @@ [-119.2575, 37.0597], [-110.5871, 44.9501], [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719] + [-87.7982, 32.5415] ] }, "properties": { "title": "tg_temp_lm_all_sites", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,6 +79,13 @@ "oxygen", "Daily", "P1D", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", "KING", "LECO", "LEWI", @@ -105,14 +112,7 @@ "BIGC", "BLDE", "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB" + "BLWA" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/wbears_gp.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/wbears_gp.json index 03ac7678ed..d35f72b13c 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/wbears_gp.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/wbears_gp.json @@ -16,7 +16,7 @@ "properties": { "title": "wbears_gp", "description": "All scores for the Daily_Dissolved_oxygen variable for the wbears_gp model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, POSE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-05-01T00:00:00Z", "end_datetime": "2021-05-07T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/wbears_rnn.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/wbears_rnn.json index fa48eea082..f20ed79bd7 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/wbears_rnn.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/wbears_rnn.json @@ -16,7 +16,7 @@ "properties": { "title": "wbears_rnn", "description": "All scores for the Daily_Dissolved_oxygen variable for the wbears_rnn model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, POSE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-05-01T00:00:00Z", "end_datetime": "2021-06-07T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/xgboost_parallel.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/xgboost_parallel.json index 054f35c179..6398449fe0 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/xgboost_parallel.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/xgboost_parallel.json @@ -9,12 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-88.1589, 31.8534], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], [-110.5871, 44.9501], [-96.6242, 34.4442], [-87.7982, 32.5415], @@ -42,13 +36,19 @@ [-82.0177, 29.6878], [-111.5081, 33.751], [-119.0274, 36.9559], - [-149.6106, 68.6307] + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914], + [-82.0084, 29.676], + [-119.2575, 37.0597], + [-88.1589, 31.8534], + [-102.4471, 39.7582] ] }, "properties": { "title": "xgboost_parallel", - "description": "All scores for the Daily_Dissolved_oxygen variable for the xgboost_parallel model. Information for the model is provided as follows: The XGBoost model is an extreme gradient boosted random forest (XGBoost) machine learning\nmodel that uses predicted atmospheric conditions and day of year as covariates. This model utilises the\nxgboost R package (Chen & Guestrin 2016; Chen et al., 2023)..\n The model predicts this variable at the following sites: TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the xgboost_parallel model. Information for the model is provided as follows: The XGBoost model is an extreme gradient boosted random forest (XGBoost) machine learning\nmodel that uses predicted atmospheric conditions and day of year as covariates. This model utilises the\nxgboost R package (Chen & Guestrin 2016; Chen et al., 2023)..\n The model predicts this variable at the following sites: BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOOK, WALK, WLOU, BARC, BIGC, TOMB, ARIK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2023-12-08T00:00:00Z", "providers": [ @@ -79,12 +79,6 @@ "oxygen", "Daily", "P1D", - "TOMB", - "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", "BLDE", "BLUE", "BLWA", @@ -112,7 +106,13 @@ "SUGG", "SYCA", "TECR", - "TOOK" + "TOOK", + "WALK", + "WLOU", + "BARC", + "BIGC", + "TOMB", + "ARIK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/collection.json b/catalog/scores/Aquatics/Daily_Water_temperature/collection.json index 02218905da..319a316aec 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/collection.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/collection.json @@ -11,67 +11,67 @@ { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/GLEON_JRabaey_temp_physics.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_physics.json" + "href": "./models/BBTW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_JRabaey_temp_physics.json" + "href": "./models/BTW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/air2waterSat_2.json" + "href": "./models/GAM_air_wind.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GAM_air_wind.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/JorritsCrystalBall.json" + "href": "./models/GLEON_physics.json" }, { "rel": "item", "type": "application/json", - "href": "./models/LSAMP_AWPC.json" + "href": "./models/JorritsCrystalBall.json" }, { "rel": "item", "type": "application/json", - "href": "./models/TSLM_seasonal_JM.json" + "href": "./models/LSAMP_AWPC.json" }, { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/TSLM_seasonal_JM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/BBTW.json" + "href": "./models/acp_fableLM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/BTW.json" + "href": "./models/air2waterSat_2.json" }, { "rel": "item", "type": "application/json", - "href": "./models/acp_fableLM.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/bee_bake_RFModel_2024.json" + "href": "./models/climatology.json" }, { "rel": "item", @@ -81,7 +81,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/bee_bake_RFModel_2024.json" }, { "rel": "item", @@ -106,27 +106,27 @@ { "rel": "item", "type": "application/json", - "href": "./models/flareSimstrat_noDA.json" + "href": "./models/flare_ler.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flare_ler.json" + "href": "./models/flareGOTM_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flare_ler_baselines.json" + "href": "./models/flareSimstrat_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/hotdeck.json" + "href": "./models/flare_ler_baselines.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGOTM_noDA.json" + "href": "./models/hotdeck.json" }, { "rel": "item", @@ -151,12 +151,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/precip_mod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/precip_mod.json" + "href": "./models/tg_arima.json" }, { "rel": "item", @@ -276,7 +276,7 @@ "interval": [ [ "2020-09-02T00:00:00Z", - "2024-07-20T00:00:00Z" + "2024-07-24T00:00:00Z" ] ] } diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/BBTW.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/BBTW.json index 5be620f538..0807e9ddc5 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/BBTW.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/BBTW.json @@ -15,7 +15,7 @@ "properties": { "title": "BBTW", "description": "All scores for the Daily_Water_temperature variable for the BBTW model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-05-01T00:00:00Z", "end_datetime": "2021-05-08T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/BTW.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/BTW.json index 1c7f1e9e6f..fed420a209 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/BTW.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/BTW.json @@ -15,7 +15,7 @@ "properties": { "title": "BTW", "description": "All scores for the Daily_Water_temperature variable for the BTW model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: POSE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-05-01T00:00:00Z", "end_datetime": "2021-05-08T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json index 59a8dc3bfc..828d0a0104 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json @@ -9,21 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-82.0084, 29.676], [-89.4737, 46.2097], [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0177, 29.6878], - [-149.6106, 68.6307], - [-82.0084, 29.676] + [-149.6106, 68.6307] ] }, "properties": { "title": "GAM_air_wind", - "description": "All scores for the Daily_Water_temperature variable for the GAM_air_wind model. Information for the model is provided as follows: I used a GAM (mgcv) with a linear relationship to air temperature and smoothing for eastward and northward winds..\n The model predicts this variable at the following sites: CRAM, LIRO, PRLA, PRPO, SUGG, TOOK, BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the GAM_air_wind model. Information for the model is provided as follows: I used a GAM (mgcv) with a linear relationship to air temperature and smoothing for eastward and northward winds..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "pending", @@ -52,13 +52,13 @@ "temperature", "Daily", "P1D", + "BARC", "CRAM", "LIRO", "PRLA", "PRPO", "SUGG", - "TOOK", - "BARC" + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json index 01c35f0abc..df77dceba1 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json @@ -9,6 +9,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-110.5871, 44.9501], + [-87.7982, 32.5415], + [-147.504, 65.1532], + [-105.5442, 40.035], + [-89.4737, 46.2097], + [-66.9868, 18.1135], + [-84.4374, 31.1854], + [-66.7987, 18.1741], + [-72.3295, 42.4719], + [-96.6038, 39.1051], + [-83.5038, 35.6904], + [-77.9832, 39.0956], [-89.7048, 45.9983], [-121.9338, 45.7908], [-87.4077, 32.9604], @@ -30,25 +42,13 @@ [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956] + [-96.6242, 34.4442] ] }, "properties": { "title": "GLEON_JRabaey_temp_physics", - "description": "All scores for the Daily_Water_temperature variable for the GLEON_JRabaey_temp_physics model. Information for the model is provided as follows: The JR-physics model is a simple process model based on the assumption that surface water\ntemperature should trend towards equilibration with air temperature with a lag factor..\n The model predicts this variable at the following sites: LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the GLEON_JRabaey_temp_physics model. Information for the model is provided as follows: The JR-physics model is a simple process model based on the assumption that surface water\ntemperature should trend towards equilibration with air temperature with a lag factor..\n The model predicts this variable at the following sites: BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLUE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-11-15T00:00:00Z", "end_datetime": "2024-03-12T00:00:00Z", "providers": [ @@ -79,6 +79,18 @@ "temperature", "Daily", "P1D", + "BLDE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING", + "LECO", + "LEWI", "LIRO", "MART", "MAYF", @@ -100,19 +112,7 @@ "ARIK", "BARC", "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "LECO", - "LEWI" + "BLUE" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json index b6a0bac1dd..961e122ca9 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-99.2531, 47.1298], - [-82.0177, 29.6878], [-149.6106, 68.6307], [-82.0084, 29.676], [-89.4737, 46.2097], [-89.7048, 45.9983], - [-99.1139, 47.1591] + [-99.1139, 47.1591], + [-99.2531, 47.1298], + [-82.0177, 29.6878] ] }, "properties": { "title": "GLEON_lm_lag_1day", - "description": "All scores for the Daily_Water_temperature variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: PRPO, SUGG, TOOK, BARC, CRAM, LIRO, PRLA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: TOOK, BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-10-30T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ @@ -52,13 +52,13 @@ "temperature", "Daily", "P1D", - "PRPO", - "SUGG", "TOOK", "BARC", "CRAM", "LIRO", - "PRLA" + "PRLA", + "PRPO", + "SUGG" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_physics.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_physics.json index 3d596759d8..a50f7c91ff 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_physics.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/GLEON_physics.json @@ -21,7 +21,7 @@ "properties": { "title": "GLEON_physics", "description": "All scores for the Daily_Water_temperature variable for the GLEON_physics model. Information for the model is provided as follows: A simple, process-based model was developed to replicate the water temperature dynamics of a\nsurface water layer sensu Chapra (2008). The model focus was only on quantifying the impacts of\natmosphere-water heat flux exchanges on the idealized near-surface water temperature dynamics.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-11-01T00:00:00Z", "end_datetime": "2023-12-22T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/JorritsCrystalBall.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/JorritsCrystalBall.json index e754442d5e..4af04579f5 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/JorritsCrystalBall.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/JorritsCrystalBall.json @@ -21,7 +21,7 @@ "properties": { "title": "JorritsCrystalBall", "description": "All scores for the Daily_Water_temperature variable for the JorritsCrystalBall model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-07-25T00:00:00Z", "end_datetime": "2023-08-28T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/LSAMP_AWPC.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/LSAMP_AWPC.json index edaaea8e1d..acb3e98558 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/LSAMP_AWPC.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/LSAMP_AWPC.json @@ -15,7 +15,7 @@ "properties": { "title": "LSAMP_AWPC", "description": "All scores for the Daily_Water_temperature variable for the LSAMP_AWPC model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-07-02T00:00:00Z", "end_datetime": "2021-08-07T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json index 2b511bd859..b5378f0ce0 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json @@ -21,7 +21,7 @@ "properties": { "title": "TSLM_seasonal_JM", "description": "All scores for the Daily_Water_temperature variable for the TSLM_seasonal_JM model. Information for the model is provided as follows: My model uses the fable package TSLM, and uses built in exogenous regressors to represent the trend and seasonality of the data as well as air temperature to predict water temperature..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", "end_datetime": "2024-06-02T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/acp_fableLM.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/acp_fableLM.json index 9d77e5884e..53bbe4decd 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/acp_fableLM.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/acp_fableLM.json @@ -21,7 +21,7 @@ "properties": { "title": "acp_fableLM", "description": "All scores for the Daily_Water_temperature variable for the acp_fableLM model. Information for the model is provided as follows: Time series linear model with FABLE.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2024-03-11T00:00:00Z", "end_datetime": "2024-04-13T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json index 5a8778b363..565326d369 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json @@ -9,6 +9,13 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-102.4471, 39.7582], + [-82.0084, 29.676], + [-119.2575, 37.0597], + [-110.5871, 44.9501], + [-87.7982, 32.5415], + [-147.504, 65.1532], + [-105.5442, 40.035], [-89.4737, 46.2097], [-66.9868, 18.1135], [-84.4374, 31.1854], @@ -35,20 +42,13 @@ [-149.6106, 68.6307], [-84.2793, 35.9574], [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], [-96.6242, 34.4442] ] }, "properties": { "title": "air2waterSat_2", - "description": "All scores for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLWA, CARI, COMO, BLUE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, BLUE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,6 +79,13 @@ "temperature", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLWA", + "CARI", + "COMO", "CRAM", "CUPE", "FLNT", @@ -105,13 +112,6 @@ "TOOK", "WALK", "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLWA", - "CARI", - "COMO", "BLUE" ], "table:columns": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json index dd991ad14f..0191e7be0f 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json @@ -9,31 +9,20 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], [-122.1655, 44.2596], [-78.1473, 38.8943], [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], [-111.7979, 40.7839], [-82.0177, 29.6878], [-111.5081, 33.751], [-119.0274, 36.9559], - [-88.1589, 31.8534], [-84.2793, 35.9574], [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], [-105.5442, 40.035], - [-89.4737, 46.2097], [-66.9868, 18.1135], [-84.4374, 31.1854], [-66.7987, 18.1741], @@ -41,16 +30,27 @@ [-96.6038, 39.1051], [-83.5038, 35.6904], [-77.9832, 39.0956], + [-121.9338, 45.7908], + [-87.4077, 32.9604], + [-96.443, 38.9459], + [-87.7982, 32.5415], + [-147.504, 65.1532], + [-89.4737, 46.2097], + [-89.7048, 45.9983], [-149.143, 68.6698], - [-149.6106, 68.6307] + [-99.1139, 47.1591], + [-99.2531, 47.1298], + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-96.6242, 34.4442] ] }, "properties": { "title": "baseline_ensemble", - "description": "All scores for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, OKSR, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, BLWA, CARI, CRAM, LIRO, OKSR, PRLA, PRPO, TOMB, TOOK, BLUE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/NEON-simple-baselines/blob/main/Models/baseline_ensemble.R", @@ -79,31 +79,20 @@ "temperature", "Daily", "P1D", - "LIRO", - "MART", - "MAYF", - "MCDI", "MCRA", "POSE", "PRIN", - "PRLA", - "PRPO", "REDB", "SUGG", "SYCA", "TECR", - "TOMB", "WALK", "WLOU", "ARIK", "BARC", "BIGC", "BLDE", - "BLUE", - "BLWA", - "CARI", "COMO", - "CRAM", "CUPE", "FLNT", "GUIL", @@ -111,8 +100,19 @@ "KING", "LECO", "LEWI", + "MART", + "MAYF", + "MCDI", + "BLWA", + "CARI", + "CRAM", + "LIRO", "OKSR", - "TOOK" + "PRLA", + "PRPO", + "TOMB", + "TOOK", + "BLUE" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json index e32632ac4d..1ada3f9745 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json @@ -9,21 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0177, 29.6878], [-149.6106, 68.6307], - [-82.0084, 29.676] + [-82.0084, 29.676], + [-89.4737, 46.2097], + [-89.7048, 45.9983], + [-99.1139, 47.1591] ] }, "properties": { "title": "bee_bake_RFModel_2024", - "description": "All scores for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: CRAM, LIRO, PRLA, PRPO, SUGG, TOOK, BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: PRPO, SUGG, TOOK, BARC, CRAM, LIRO, PRLA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": null, @@ -52,13 +52,13 @@ "temperature", "Daily", "P1D", - "CRAM", - "LIRO", - "PRLA", "PRPO", "SUGG", "TOOK", - "BARC" + "BARC", + "CRAM", + "LIRO", + "PRLA" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/cb_prophet.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/cb_prophet.json index 83644a6044..f598f4af49 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/cb_prophet.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/cb_prophet.json @@ -9,46 +9,46 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-84.4374, 31.1854], + [-66.7987, 18.1741], + [-72.3295, 42.4719], + [-96.6038, 39.1051], + [-83.5038, 35.6904], + [-77.9832, 39.0956], [-121.9338, 45.7908], [-87.4077, 32.9604], [-96.443, 38.9459], - [-122.1655, 44.2596], [-78.1473, 38.8943], [-97.7823, 33.3785], [-111.7979, 40.7839], [-82.0177, 29.6878], + [-111.5081, 33.751], [-88.1589, 31.8534], [-84.2793, 35.9574], [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-110.5871, 44.9501], + [-66.9868, 18.1135], [-87.7982, 32.5415], - [-147.504, 65.1532], + [-96.6242, 34.4442], [-105.5442, 40.035], [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], [-89.7048, 45.9983], - [-119.0274, 36.9559], - [-111.5081, 33.751], - [-119.2575, 37.0597], - [-96.6242, 34.4442], [-99.1139, 47.1591], [-99.2531, 47.1298], + [-147.504, 65.1532], + [-119.0274, 36.9559], + [-119.2575, 37.0597], + [-122.1655, 44.2596], [-149.143, 68.6698], [-149.6106, 68.6307] ] }, "properties": { "title": "cb_prophet", - "description": "All scores for the Daily_Water_temperature variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, TOMB, WALK, WLOU, ARIK, BARC, BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, TECR, SYCA, BIGC, BLUE, PRLA, PRPO, OKSR, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, POSE, PRIN, REDB, SUGG, SYCA, TOMB, WALK, WLOU, ARIK, BARC, BLDE, CUPE, BLWA, BLUE, COMO, CRAM, LIRO, PRLA, PRPO, CARI, TECR, BIGC, MCRA, OKSR, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-12-18T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ @@ -79,38 +79,38 @@ "temperature", "Daily", "P1D", + "FLNT", + "GUIL", + "HOPB", + "KING", + "LECO", + "LEWI", "MART", "MAYF", "MCDI", - "MCRA", "POSE", "PRIN", "REDB", "SUGG", + "SYCA", "TOMB", "WALK", "WLOU", "ARIK", "BARC", "BLDE", + "CUPE", "BLWA", - "CARI", + "BLUE", "COMO", "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "LECO", - "LEWI", "LIRO", - "TECR", - "SYCA", - "BIGC", - "BLUE", "PRLA", "PRPO", + "CARI", + "TECR", + "BIGC", + "MCRA", "OKSR", "TOOK" ], diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/climatology.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/climatology.json index 45ddcf83db..394779588c 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/climatology.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/climatology.json @@ -9,6 +9,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-96.6038, 39.1051], + [-83.5038, 35.6904], + [-77.9832, 39.0956], + [-121.9338, 45.7908], [-87.4077, 32.9604], [-96.443, 38.9459], [-122.1655, 44.2596], @@ -30,27 +34,23 @@ [-84.4374, 31.1854], [-66.7987, 18.1741], [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-121.9338, 45.7908], + [-96.6242, 34.4442], [-88.1589, 31.8534], - [-89.4737, 46.2097], - [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], [-147.504, 65.1532], + [-89.4737, 46.2097], + [-89.7048, 45.9983], [-149.143, 68.6698], - [-149.6106, 68.6307], - [-96.6242, 34.4442] + [-149.6106, 68.6307] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, TOMB, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK, BLUE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, BLUE, TOMB, PRLA, PRPO, CARI, CRAM, LIRO, OKSR, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2021-05-01T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", @@ -79,6 +79,10 @@ "temperature", "Daily", "P1D", + "KING", + "LECO", + "LEWI", + "MART", "MAYF", "MCDI", "MCRA", @@ -100,19 +104,15 @@ "FLNT", "GUIL", "HOPB", - "KING", - "LECO", - "LEWI", - "MART", + "BLUE", "TOMB", - "CRAM", - "LIRO", "PRLA", "PRPO", "CARI", + "CRAM", + "LIRO", "OKSR", - "TOOK", - "BLUE" + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json index 8d1b90ee32..fb66e60e2f 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json @@ -38,19 +38,19 @@ [-89.4737, 46.2097], [-96.6038, 39.1051], [-89.7048, 45.9983], + [-149.143, 68.6698], [-99.1139, 47.1591], [-99.2531, 47.1298], - [-149.143, 68.6698], - [-149.6106, 68.6307], - [-88.1589, 31.8534] + [-88.1589, 31.8534], + [-149.6106, 68.6307] ] }, "properties": { "title": "fARIMA_clim_ensemble", - "description": "All scores for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLWA, COMO, CUPE, LECO, LEWI, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, FLNT, GUIL, HOPB, MART, BLUE, CARI, CRAM, KING, LIRO, PRLA, PRPO, OKSR, TOOK, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLWA, COMO, CUPE, LECO, LEWI, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, FLNT, GUIL, HOPB, MART, BLUE, CARI, CRAM, KING, LIRO, OKSR, PRLA, PRPO, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/NEON-simple-baselines/blob/main/Models/fARIMA_clim_ensemble.R", @@ -108,11 +108,11 @@ "CRAM", "KING", "LIRO", + "OKSR", "PRLA", "PRPO", - "OKSR", - "TOOK", - "TOMB" + "TOMB", + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json index 53105e75d6..7085681bf5 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json @@ -9,22 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], @@ -42,15 +26,31 @@ [-77.9832, 39.0956], [-89.7048, 45.9983], [-121.9338, 45.7908], + [-87.4077, 32.9604], + [-96.443, 38.9459], + [-122.1655, 44.2596], + [-149.143, 68.6698], + [-78.1473, 38.8943], + [-97.7823, 33.3785], + [-99.1139, 47.1591], + [-99.2531, 47.1298], + [-111.7979, 40.7839], + [-82.0177, 29.6878], + [-111.5081, 33.751], + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914], [-96.6242, 34.4442] ] }, "properties": { "title": "fTSLM_lag", - "description": "All scores for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day’s air temperature.\n The model predicts this variable at the following sites: MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, BLUE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day’s air temperature.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, BLUE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-10-21T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/NEON-simple-baselines/blob/main/Models/ARIMA_model.R", @@ -79,22 +79,6 @@ "temperature", "Daily", "P1D", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -112,6 +96,22 @@ "LEWI", "LIRO", "MART", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "BLUE" ], "table:columns": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json index 79abfef38d..e762a489a3 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json @@ -21,9 +21,9 @@ "properties": { "title": "flareGLM", "description": "All scores for the Daily_Water_temperature variable for the flareGLM model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019) and data assimilation algorithm to generate\nensemble forecasts of lake water temperature..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2022-12-08T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/NEON-forecast-code/workflows/default", diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json index f152ec9fe5..87ee16e4c0 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json @@ -9,21 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307], [-82.0084, 29.676], + [-82.0177, 29.6878], [-89.4737, 46.2097], [-89.7048, 45.9983], - [-99.1139, 47.1591] + [-99.1139, 47.1591], + [-99.2531, 47.1298], + [-149.6106, 68.6307] ] }, "properties": { "title": "flareGLM_noDA", - "description": "All scores for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: PRPO, SUGG, TOOK, BARC, CRAM, LIRO, PRLA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: BARC, SUGG, CRAM, LIRO, PRLA, PRPO, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-03-02T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/NEON-forecast-code/workflows/default", @@ -52,13 +52,13 @@ "temperature", "Daily", "P1D", - "PRPO", - "SUGG", - "TOOK", "BARC", + "SUGG", "CRAM", "LIRO", - "PRLA" + "PRLA", + "PRPO", + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json index 39c414669e..b18fa8c86b 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json @@ -9,18 +9,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-99.1139, 47.1591], - [-89.7048, 45.9983], - [-82.0084, 29.676], - [-82.0177, 29.6878], [-89.4737, 46.2097], + [-89.7048, 45.9983], + [-99.1139, 47.1591], [-99.2531, 47.1298], + [-82.0177, 29.6878], + [-82.0084, 29.676], [-149.6106, 68.6307] ] }, "properties": { "title": "flareGOTM_noDA", - "description": "All scores for the Daily_Water_temperature variable for the flareGOTM_noDA model. Information for the model is provided as follows: FLARE-GOTM uses the General Ocean Turbulence Model (GOTM) hydrodynamic model. GOTM is a 1-D\nhydrodynamic turbulence model (Umlauf et al., 2005) that estimates water column temperatures.\n The model predicts this variable at the following sites: PRLA, LIRO, BARC, SUGG, CRAM, PRPO, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the flareGOTM_noDA model. Information for the model is provided as follows: FLARE-GOTM uses the General Ocean Turbulence Model (GOTM) hydrodynamic model. GOTM is a 1-D\nhydrodynamic turbulence model (Umlauf et al., 2005) that estimates water column temperatures.\n The model predicts this variable at the following sites: CRAM, LIRO, PRLA, PRPO, SUGG, BARC, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-20T00:00:00Z", @@ -52,12 +52,12 @@ "temperature", "Daily", "P1D", - "PRLA", - "LIRO", - "BARC", - "SUGG", "CRAM", + "LIRO", + "PRLA", "PRPO", + "SUGG", + "BARC", "TOOK" ], "table:columns": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json index eac1f87c64..a975b0654a 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json @@ -9,18 +9,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-82.0177, 29.6878], + [-99.2531, 47.1298], [-82.0084, 29.676], + [-82.0177, 29.6878], [-89.4737, 46.2097], [-99.1139, 47.1591], - [-99.2531, 47.1298], [-149.6106, 68.6307] ] }, "properties": { "title": "flareSimstrat_noDA", - "description": "All scores for the Daily_Water_temperature variable for the flareSimstrat_noDA model. Information for the model is provided as follows: FLARE-Simstrat uses the same principles and overarching framework as FLARE-GLM with the\nhydrodynamic model replaced with Simstrat. Simstrat is a 1-D hydrodynamic turbulence model\n(Goudsmit et al., 2002) that estimates water column temperatures..\n The model predicts this variable at the following sites: SUGG, BARC, CRAM, PRLA, PRPO, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the flareSimstrat_noDA model. Information for the model is provided as follows: FLARE-Simstrat uses the same principles and overarching framework as FLARE-GLM with the\nhydrodynamic model replaced with Simstrat. Simstrat is a 1-D hydrodynamic turbulence model\n(Goudsmit et al., 2002) that estimates water column temperatures..\n The model predicts this variable at the following sites: PRPO, BARC, SUGG, CRAM, PRLA, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ @@ -51,11 +51,11 @@ "temperature", "Daily", "P1D", - "SUGG", + "PRPO", "BARC", + "SUGG", "CRAM", "PRLA", - "PRPO", "TOOK" ], "table:columns": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/flare_ler.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/flare_ler.json index 2575d954af..d786f4e555 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/flare_ler.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/flare_ler.json @@ -9,10 +9,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-89.7048, 45.9983], - [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0084, 29.676], + [-89.7048, 45.9983], + [-99.1139, 47.1591], [-82.0177, 29.6878], [-89.4737, 46.2097], [-149.6106, 68.6307] @@ -20,8 +20,8 @@ }, "properties": { "title": "flare_ler", - "description": "All scores for the Daily_Water_temperature variable for the flare_ler model. Information for the model is provided as follows: The LER MME is a multi-model ensemble (MME) derived from the three process models from\nFLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat). To generate the MME, an ensemble\nforecast was generated by sampling from the submitted models’ ensemble members.\n The model predicts this variable at the following sites: LIRO, PRLA, PRPO, BARC, SUGG, CRAM, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-26T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the flare_ler model. Information for the model is provided as follows: The LER MME is a multi-model ensemble (MME) derived from the three process models from\nFLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat). To generate the MME, an ensemble\nforecast was generated by sampling from the submitted models’ ensemble members.\n The model predicts this variable at the following sites: PRPO, BARC, LIRO, PRLA, SUGG, CRAM, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-27T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ @@ -52,10 +52,10 @@ "temperature", "Daily", "P1D", - "LIRO", - "PRLA", "PRPO", "BARC", + "LIRO", + "PRLA", "SUGG", "CRAM", "TOOK" diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json index 9f74deece1..f454ea2e4a 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json @@ -9,12 +9,12 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-99.2531, 47.1298], - [-82.0177, 29.6878], [-82.0084, 29.676], + [-82.0177, 29.6878], [-89.4737, 46.2097], [-89.7048, 45.9983], [-99.1139, 47.1591], + [-99.2531, 47.1298], [-102.4471, 39.7582], [-119.2575, 37.0597], [-110.5871, 44.9501], @@ -46,8 +46,8 @@ }, "properties": { "title": "flare_ler_baselines", - "description": "All scores for the Daily_Water_temperature variable for the flare_ler_baselines model. Information for the model is provided as follows: The LER-baselines model is a multi-model ensemble (MME) comprised of the three process\nmodels from FLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat) and the two baseline\nmodels (day-of-year, persistence), submitted by Challenge organisers. To generate the MME, an\nensemble forecast was generated by sampling from the submitted model’s ensemble members (either\nfrom an ensemble forecast in the case of the FLARE models and persistence, or from the distribution for\nthe day-of-year forecasts).\n The model predicts this variable at the following sites: PRPO, SUGG, BARC, CRAM, LIRO, PRLA, ARIK, BIGC, BLDE, BLWA, CARI, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the flare_ler_baselines model. Information for the model is provided as follows: The LER-baselines model is a multi-model ensemble (MME) comprised of the three process\nmodels from FLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat) and the two baseline\nmodels (day-of-year, persistence), submitted by Challenge organisers. To generate the MME, an\nensemble forecast was generated by sampling from the submitted model’s ensemble members (either\nfrom an ensemble forecast in the case of the FLARE models and persistence, or from the distribution for\nthe day-of-year forecasts).\n The model predicts this variable at the following sites: BARC, SUGG, CRAM, LIRO, PRLA, PRPO, ARIK, BIGC, BLDE, BLWA, CARI, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-03-17T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ @@ -78,12 +78,12 @@ "temperature", "Daily", "P1D", - "PRPO", - "SUGG", "BARC", + "SUGG", "CRAM", "LIRO", "PRLA", + "PRPO", "ARIK", "BIGC", "BLDE", diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json index 860f20491b..0beb3d3f2d 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json @@ -44,9 +44,9 @@ "properties": { "title": "hotdeck", "description": "All scores for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, BLWA, FLNT, KING, LECO, LEWI, MAYF, POSE, PRIN, SYCA, TOMB, ARIK, HOPB, MCRA, REDB, TECR, BLDE, COMO, WLOU, CRAM, CARI, BIGC, BLUE, CUPE, GUIL, WALK, LIRO, PRLA, PRPO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/swpease/hotdeckfc", diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json index 472e344cc1..0d448744ba 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json @@ -9,21 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-99.2531, 47.1298], + [-82.0177, 29.6878], + [-149.6106, 68.6307], [-82.0084, 29.676], [-89.4737, 46.2097], [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] + [-99.1139, 47.1591] ] }, "properties": { "title": "lm_AT_WTL_WS", - "description": "All scores for the Daily_Water_temperature variable for the lm_AT_WTL_WS model. Information for the model is provided as follows: This forecast of water temperature at NEON Lake sites uses a linear model, incorporating air temperature, wind speed, and the previous day's forecasted water temperature as variables..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the lm_AT_WTL_WS model. Information for the model is provided as follows: This forecast of water temperature at NEON Lake sites uses a linear model, incorporating air temperature, wind speed, and the previous day's forecasted water temperature as variables..\n The model predicts this variable at the following sites: PRPO, SUGG, TOOK, BARC, CRAM, LIRO, PRLA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/cvickery123/NEON-forecast-challenge-workshop/blob/821f7af9bc83951722bb5bcecbd601c62cbe66e0/Submit_forecast/forecast_project1.Rmd", @@ -52,13 +52,13 @@ "temperature", "Daily", "P1D", + "PRPO", + "SUGG", + "TOOK", "BARC", "CRAM", "LIRO", - "PRLA", - "PRPO", - "SUGG", - "TOOK" + "PRLA" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json index c2fe6f9de1..29832672f9 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json @@ -9,21 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-82.0084, 29.676], [-89.4737, 46.2097], [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0177, 29.6878], - [-149.6106, 68.6307], - [-82.0084, 29.676] + [-149.6106, 68.6307] ] }, "properties": { "title": "mkricheldorf_w_lag", - "description": "All scores for the Daily_Water_temperature variable for the mkricheldorf_w_lag model. Information for the model is provided as follows: I used an autoregressive linear model using the lm() function.\n The model predicts this variable at the following sites: CRAM, LIRO, PRLA, PRPO, SUGG, TOOK, BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the mkricheldorf_w_lag model. Information for the model is provided as follows: I used an autoregressive linear model using the lm() function.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2024-03-06T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/michael-kricheldorf/first-forecast", @@ -52,13 +52,13 @@ "temperature", "Daily", "P1D", + "BARC", "CRAM", "LIRO", "PRLA", "PRPO", "SUGG", - "TOOK", - "BARC" + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json index b1bb5176ec..887abd9e27 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json @@ -21,9 +21,9 @@ "properties": { "title": "mlp1_wtempforecast_LF", "description": "All scores for the Daily_Water_temperature variable for the mlp1_wtempforecast_LF model. Information for the model is provided as follows: Modelling for water temperature using a single layer neural network (mlp() in tidymodels). Used relative humidity, precipitation flux and air temperature as drivers. Hypertuned parameters for models to be run with 100 epochs and penalty value of 0.01..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/lindsey-finks/NEON-forecast-challenge-workshop", diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json index a6fd553e13..cfdef43a6d 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json @@ -48,9 +48,9 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, BLUE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-08-25T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/precip_mod.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/precip_mod.json index b16d92eeea..c68e9d5848 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/precip_mod.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/precip_mod.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307], [-82.0084, 29.676], [-89.4737, 46.2097], [-89.7048, 45.9983], - [-99.1139, 47.1591] + [-99.1139, 47.1591], + [-99.2531, 47.1298], + [-82.0177, 29.6878], + [-149.6106, 68.6307] ] }, "properties": { "title": "precip_mod", - "description": "All scores for the Daily_Water_temperature variable for the precip_mod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: PRPO, SUGG, TOOK, BARC, CRAM, LIRO, PRLA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the precip_mod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-12-24T00:00:00Z", "end_datetime": "2024-01-24T00:00:00Z", "providers": [ @@ -52,13 +52,13 @@ "temperature", "Daily", "P1D", - "PRPO", - "SUGG", - "TOOK", "BARC", "CRAM", "LIRO", - "PRLA" + "PRLA", + "PRPO", + "SUGG", + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json index b7bf659a7c..80d96e16c4 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json @@ -9,48 +9,48 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-149.143, 68.6698], - [-149.6106, 68.6307], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-96.6038, 39.1051], - [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.5081, 33.751], - [-102.4471, 39.7582], - [-82.0084, 29.676], [-119.2575, 37.0597], [-110.5871, 44.9501], [-96.6242, 34.4442], [-87.7982, 32.5415], + [-147.504, 65.1532], + [-105.5442, 40.035], + [-89.4737, 46.2097], [-66.9868, 18.1135], [-84.4374, 31.1854], [-66.7987, 18.1741], [-72.3295, 42.4719], + [-96.6038, 39.1051], [-83.5038, 35.6904], [-77.9832, 39.0956], + [-89.7048, 45.9983], [-121.9338, 45.7908], [-87.4077, 32.9604], [-96.443, 38.9459], [-122.1655, 44.2596], + [-149.143, 68.6698], [-78.1473, 38.8943], [-97.7823, 33.3785], + [-99.1139, 47.1591], + [-99.2531, 47.1298], [-111.7979, 40.7839], [-82.0177, 29.6878], + [-111.5081, 33.751], [-119.0274, 36.9559], [-88.1589, 31.8534], + [-149.6106, 68.6307], [-84.2793, 35.9574], - [-105.9154, 39.8914] + [-105.9154, 39.8914], + [-102.4471, 39.7582], + [-82.0084, 29.676] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: OKSR, TOOK, CARI, COMO, CRAM, KING, LIRO, PRLA, PRPO, SYCA, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CUPE, FLNT, GUIL, HOPB, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, TECR, TOMB, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-12-18T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -79,40 +79,40 @@ "temperature", "Daily", "P1D", - "OKSR", - "TOOK", - "CARI", - "COMO", - "CRAM", - "KING", - "LIRO", - "PRLA", - "PRPO", - "SYCA", - "ARIK", - "BARC", "BIGC", "BLDE", "BLUE", "BLWA", + "CARI", + "COMO", + "CRAM", "CUPE", "FLNT", "GUIL", "HOPB", + "KING", "LECO", "LEWI", + "LIRO", "MART", "MAYF", "MCDI", "MCRA", + "OKSR", "POSE", "PRIN", + "PRLA", + "PRPO", "REDB", "SUGG", + "SYCA", "TECR", "TOMB", + "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json index 719cd4255f..6351804250 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json @@ -10,20 +10,18 @@ "type": "MultiPoint", "coordinates": [ [-149.6106, 68.6307], + [-119.2575, 37.0597], + [-147.504, 65.1532], [-105.5442, 40.035], - [-149.143, 68.6698], - [-111.5081, 33.751], + [-89.4737, 46.2097], [-96.6038, 39.1051], - [-147.504, 65.1532], - [-99.1139, 47.1591], [-89.7048, 45.9983], - [-99.2531, 47.1298], - [-89.4737, 46.2097], [-121.9338, 45.7908], - [-88.1589, 31.8534], + [-149.143, 68.6698], + [-99.1139, 47.1591], + [-99.2531, 47.1298], [-102.4471, 39.7582], [-82.0084, 29.676], - [-119.2575, 37.0597], [-110.5871, 44.9501], [-96.6242, 34.4442], [-87.7982, 32.5415], @@ -40,17 +38,19 @@ [-97.7823, 33.3785], [-111.7979, 40.7839], [-82.0177, 29.6878], + [-111.5081, 33.751], [-119.0274, 36.9559], + [-88.1589, 31.8534], [-84.2793, 35.9574], [-105.9154, 39.8914] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Daily_Water_temperature variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: TOOK, COMO, OKSR, SYCA, KING, CARI, PRLA, LIRO, PRPO, CRAM, MART, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CUPE, FLNT, GUIL, HOPB, LECO, LEWI, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, TECR, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: TOOK, BIGC, CARI, COMO, CRAM, KING, LIRO, MART, OKSR, PRLA, PRPO, ARIK, BARC, BLDE, BLUE, BLWA, CUPE, FLNT, GUIL, HOPB, LECO, LEWI, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-08-06T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -80,20 +80,18 @@ "Daily", "P1D", "TOOK", + "BIGC", + "CARI", "COMO", - "OKSR", - "SYCA", + "CRAM", "KING", - "CARI", - "PRLA", "LIRO", - "PRPO", - "CRAM", "MART", - "TOMB", + "OKSR", + "PRLA", + "PRPO", "ARIK", "BARC", - "BIGC", "BLDE", "BLUE", "BLWA", @@ -110,7 +108,9 @@ "PRIN", "REDB", "SUGG", + "SYCA", "TECR", + "TOMB", "WALK", "WLOU" ], diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json index 8b19ff3cac..2b7704f64e 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json @@ -9,6 +9,15 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-102.4471, 39.7582], + [-82.0084, 29.676], + [-119.2575, 37.0597], + [-110.5871, 44.9501], + [-96.6242, 34.4442], + [-87.7982, 32.5415], + [-147.504, 65.1532], + [-105.5442, 40.035], + [-89.4737, 46.2097], [-66.9868, 18.1135], [-84.4374, 31.1854], [-66.7987, 18.1741], @@ -33,22 +42,13 @@ [-88.1589, 31.8534], [-149.6106, 68.6307], [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097] + [-105.9154, 39.8914] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All scores for the Daily_Water_temperature variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,6 +79,15 @@ "temperature", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", "CUPE", "FLNT", "GUIL", @@ -103,16 +112,7 @@ "TOMB", "TOOK", "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM" + "WLOU" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json index 960e02a6bf..6a3aad8183 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json @@ -9,6 +9,9 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], @@ -39,16 +42,13 @@ [-82.0177, 29.6878], [-111.5081, 33.751], [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] + [-88.1589, 31.8534] ] }, "properties": { "title": "tg_humidity_lm_all_sites", - "description": "All scores for the Daily_Water_temperature variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,6 +79,9 @@ "temperature", "Daily", "P1D", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -109,10 +112,7 @@ "SUGG", "SYCA", "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "TOMB" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_lasso.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_lasso.json index 7499dba7e3..e8910963a7 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_lasso.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_lasso.json @@ -9,6 +9,9 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], @@ -39,16 +42,13 @@ [-82.0177, 29.6878], [-111.5081, 33.751], [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] + [-88.1589, 31.8534] ] }, "properties": { "title": "tg_lasso", - "description": "All scores for the Daily_Water_temperature variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -79,6 +79,9 @@ "temperature", "Daily", "P1D", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -109,10 +112,7 @@ "SUGG", "SYCA", "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "TOMB" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json index 0bed895150..2afea0bea7 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json @@ -9,6 +9,12 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-102.4471, 39.7582], + [-82.0084, 29.676], + [-119.2575, 37.0597], + [-110.5871, 44.9501], + [-96.6242, 34.4442], + [-87.7982, 32.5415], [-147.504, 65.1532], [-105.5442, 40.035], [-89.4737, 46.2097], @@ -36,19 +42,13 @@ [-88.1589, 31.8534], [-149.6106, 68.6307], [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415] + [-105.9154, 39.8914] ] }, "properties": { "title": "tg_precip_lm", - "description": "All scores for the Daily_Water_temperature variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,6 +79,12 @@ "temperature", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", "CARI", "COMO", "CRAM", @@ -106,13 +112,7 @@ "TOMB", "TOOK", "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA" + "WLOU" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json index b3cbcbea6d..0061f6eea2 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json @@ -9,19 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], [-149.6106, 68.6307], [-84.2793, 35.9574], [-105.9154, 39.8914], @@ -42,13 +29,26 @@ [-83.5038, 35.6904], [-77.9832, 39.0956], [-89.7048, 45.9983], - [-121.9338, 45.7908] + [-121.9338, 45.7908], + [-87.4077, 32.9604], + [-96.443, 38.9459], + [-122.1655, 44.2596], + [-149.143, 68.6698], + [-78.1473, 38.8943], + [-97.7823, 33.3785], + [-99.1139, 47.1591], + [-99.2531, 47.1298], + [-111.7979, 40.7839], + [-82.0177, 29.6878], + [-111.5081, 33.751], + [-119.0274, 36.9559], + [-88.1589, 31.8534] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All scores for the Daily_Water_temperature variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,19 +79,6 @@ "temperature", "Daily", "P1D", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", "TOOK", "WALK", "WLOU", @@ -112,7 +99,20 @@ "LECO", "LEWI", "LIRO", - "MART" + "MART", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_randfor.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_randfor.json index 8c05355a75..e357753aeb 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_randfor.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_randfor.json @@ -9,6 +9,11 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-105.9154, 39.8914], + [-102.4471, 39.7582], + [-82.0084, 29.676], + [-119.2575, 37.0597], + [-110.5871, 44.9501], [-96.6242, 34.4442], [-87.7982, 32.5415], [-147.504, 65.1532], @@ -37,18 +42,13 @@ [-119.0274, 36.9559], [-88.1589, 31.8534], [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501] + [-84.2793, 35.9574] ] }, "properties": { "title": "tg_randfor", - "description": "All scores for the Daily_Water_temperature variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -79,6 +79,11 @@ "temperature", "Daily", "P1D", + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", "BLUE", "BLWA", "CARI", @@ -107,12 +112,7 @@ "TECR", "TOMB", "TOOK", - "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE" + "WALK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json index 0c182a3c23..b94f2c9e3b 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json @@ -9,48 +9,48 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-111.5081, 33.751], - [-105.5442, 40.035], [-149.143, 68.6698], [-149.6106, 68.6307], + [-147.504, 65.1532], + [-105.5442, 40.035], + [-96.6038, 39.1051], + [-99.1139, 47.1591], + [-111.5081, 33.751], + [-89.7048, 45.9983], + [-99.2531, 47.1298], + [-89.4737, 46.2097], + [-119.2575, 37.0597], + [-121.9338, 45.7908], + [-122.1655, 44.2596], + [-102.4471, 39.7582], + [-82.0084, 29.676], + [-110.5871, 44.9501], + [-96.6242, 34.4442], + [-87.7982, 32.5415], + [-66.9868, 18.1135], + [-84.4374, 31.1854], [-66.7987, 18.1741], [-72.3295, 42.4719], - [-96.6038, 39.1051], [-83.5038, 35.6904], [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], [-87.4077, 32.9604], [-96.443, 38.9459], - [-122.1655, 44.2596], [-78.1473, 38.8943], [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], [-111.7979, 40.7839], [-82.0177, 29.6878], [-119.0274, 36.9559], [-88.1589, 31.8534], [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854] + [-105.9154, 39.8914] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Water_temperature variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: SYCA, COMO, OKSR, TOOK, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, CRAM, CUPE, FLNT.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: OKSR, TOOK, CARI, COMO, KING, PRLA, SYCA, LIRO, PRPO, CRAM, BIGC, MART, MCRA, ARIK, BARC, BLDE, BLUE, BLWA, CUPE, FLNT, GUIL, HOPB, LECO, LEWI, MAYF, MCDI, POSE, PRIN, REDB, SUGG, TECR, TOMB, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-08-06T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -79,40 +79,40 @@ "temperature", "Daily", "P1D", - "SYCA", - "COMO", "OKSR", "TOOK", + "CARI", + "COMO", + "KING", + "PRLA", + "SYCA", + "LIRO", + "PRPO", + "CRAM", + "BIGC", + "MART", + "MCRA", + "ARIK", + "BARC", + "BLDE", + "BLUE", + "BLWA", + "CUPE", + "FLNT", "GUIL", "HOPB", - "KING", "LECO", "LEWI", - "LIRO", - "MART", "MAYF", "MCDI", - "MCRA", "POSE", "PRIN", - "PRLA", - "PRPO", "REDB", "SUGG", "TECR", "TOMB", "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "CRAM", - "CUPE", - "FLNT" + "WLOU" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json index 14fde5a112..3097bbbadc 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json @@ -9,20 +9,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-119.2575, 37.0597], - [-149.143, 68.6698], [-149.6106, 68.6307], - [-147.504, 65.1532], - [-99.1139, 47.1591], - [-89.7048, 45.9983], - [-99.2531, 47.1298], - [-89.4737, 46.2097], + [-84.2793, 35.9574], + [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], + [-119.2575, 37.0597], [-110.5871, 44.9501], [-96.6242, 34.4442], [-87.7982, 32.5415], + [-147.504, 65.1532], [-105.5442, 40.035], + [-89.4737, 46.2097], [-66.9868, 18.1135], [-84.4374, 31.1854], [-66.7987, 18.1741], @@ -30,25 +28,27 @@ [-96.6038, 39.1051], [-83.5038, 35.6904], [-77.9832, 39.0956], + [-89.7048, 45.9983], [-121.9338, 45.7908], [-87.4077, 32.9604], [-96.443, 38.9459], [-122.1655, 44.2596], + [-149.143, 68.6698], [-78.1473, 38.8943], [-97.7823, 33.3785], + [-99.1139, 47.1591], + [-99.2531, 47.1298], [-111.7979, 40.7839], [-82.0177, 29.6878], [-111.5081, 33.751], [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-84.2793, 35.9574], - [-105.9154, 39.8914] + [-88.1589, 31.8534] ] }, "properties": { "title": "tg_temp_lm", - "description": "All scores for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BIGC, OKSR, TOOK, CARI, PRLA, LIRO, PRPO, CRAM, ARIK, BARC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-12-18T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,20 +79,18 @@ "temperature", "Daily", "P1D", - "BIGC", - "OKSR", "TOOK", - "CARI", - "PRLA", - "LIRO", - "PRPO", - "CRAM", + "WALK", + "WLOU", "ARIK", "BARC", + "BIGC", "BLDE", "BLUE", "BLWA", + "CARI", "COMO", + "CRAM", "CUPE", "FLNT", "GUIL", @@ -100,19 +98,21 @@ "KING", "LECO", "LEWI", + "LIRO", "MART", "MAYF", "MCDI", "MCRA", + "OKSR", "POSE", "PRIN", + "PRLA", + "PRPO", "REDB", "SUGG", "SYCA", "TECR", - "TOMB", - "WALK", - "WLOU" + "TOMB" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json index 2f604ec108..dee6a65662 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json @@ -9,6 +9,11 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], @@ -37,18 +42,13 @@ [-99.2531, 47.1298], [-111.7979, 40.7839], [-82.0177, 29.6878], - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] + [-111.5081, 33.751] ] }, "properties": { "title": "tg_temp_lm_all_sites", - "description": "All scores for the Daily_Water_temperature variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,6 +79,11 @@ "temperature", "Daily", "P1D", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -107,12 +112,7 @@ "PRPO", "REDB", "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "SYCA" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/wbears_gp.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/wbears_gp.json index 73b8ead95d..8398156240 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/wbears_gp.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/wbears_gp.json @@ -16,7 +16,7 @@ "properties": { "title": "wbears_gp", "description": "All scores for the Daily_Water_temperature variable for the wbears_gp model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, POSE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-05-01T00:00:00Z", "end_datetime": "2021-05-07T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/wbears_rnn.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/wbears_rnn.json index 121b3fddfc..8574389350 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/wbears_rnn.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/wbears_rnn.json @@ -16,7 +16,7 @@ "properties": { "title": "wbears_rnn", "description": "All scores for the Daily_Water_temperature variable for the wbears_rnn model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, POSE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-06-01T00:00:00Z", "end_datetime": "2021-06-07T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/wtemp_lm_model.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/wtemp_lm_model.json index 1bd76f2621..431f15a23f 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/wtemp_lm_model.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/wtemp_lm_model.json @@ -21,7 +21,7 @@ "properties": { "title": "wtemp_lm_model", "description": "All scores for the Daily_Water_temperature variable for the wtemp_lm_model model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-09T00:00:00Z", "end_datetime": "2023-02-09T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/xgboost_parallel.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/xgboost_parallel.json index ace9d4b5f3..f4110a4ad8 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/xgboost_parallel.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/xgboost_parallel.json @@ -9,10 +9,24 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-84.4374, 31.1854], + [-66.7987, 18.1741], + [-72.3295, 42.4719], + [-96.6038, 39.1051], + [-83.5038, 35.6904], + [-77.9832, 39.0956], + [-89.7048, 45.9983], + [-121.9338, 45.7908], + [-87.4077, 32.9604], + [-96.443, 38.9459], + [-78.1473, 38.8943], + [-97.7823, 33.3785], + [-99.2531, 47.1298], + [-111.7979, 40.7839], + [-82.0177, 29.6878], [-111.5081, 33.751], [-119.0274, 36.9559], [-88.1589, 31.8534], - [-149.6106, 68.6307], [-84.2793, 35.9574], [-105.9154, 39.8914], [-102.4471, 39.7582], @@ -25,30 +39,16 @@ [-105.5442, 40.035], [-89.4737, 46.2097], [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], + [-99.1139, 47.1591], [-122.1655, 44.2596], [-149.143, 68.6698], - [-78.1473, 38.8943], - [-97.7823, 33.3785], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-111.7979, 40.7839], - [-82.0177, 29.6878] + [-149.6106, 68.6307] ] }, "properties": { "title": "xgboost_parallel", - "description": "All scores for the Daily_Water_temperature variable for the xgboost_parallel model. Information for the model is provided as follows: The XGBoost model is an extreme gradient boosted random forest (XGBoost) machine learning\nmodel that uses predicted atmospheric conditions and day of year as covariates. This model utilises the\nxgboost R package (Chen & Guestrin 2016; Chen et al., 2023)..\n The model predicts this variable at the following sites: SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the xgboost_parallel model. Information for the model is provided as follows: The XGBoost model is an extreme gradient boosted random forest (XGBoost) machine learning\nmodel that uses predicted atmospheric conditions and day of year as covariates. This model utilises the\nxgboost R package (Chen & Guestrin 2016; Chen et al., 2023)..\n The model predicts this variable at the following sites: FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, POSE, PRIN, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, PRLA, MCRA, OKSR, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2023-12-08T00:00:00Z", "providers": [ @@ -79,10 +79,24 @@ "temperature", "Daily", "P1D", + "FLNT", + "GUIL", + "HOPB", + "KING", + "LECO", + "LEWI", + "LIRO", + "MART", + "MAYF", + "MCDI", + "POSE", + "PRIN", + "PRPO", + "REDB", + "SUGG", "SYCA", "TECR", "TOMB", - "TOOK", "WALK", "WLOU", "ARIK", @@ -95,24 +109,10 @@ "COMO", "CRAM", "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "LECO", - "LEWI", - "LIRO", - "MART", - "MAYF", - "MCDI", + "PRLA", "MCRA", "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG" + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json index a030ae963b..5e4fd97a51 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json @@ -9,21 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-149.6106, 68.6307], [-82.0084, 29.676], [-89.4737, 46.2097], [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-149.6106, 68.6307] + [-82.0177, 29.6878] ] }, "properties": { "title": "zimmerman_proj1", - "description": "All scores for the Daily_Water_temperature variable for the zimmerman_proj1 model. Information for the model is provided as follows: I used an ARIMA model with one autoregressive term. I also included air pressure and air temperature.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the zimmerman_proj1 model. Information for the model is provided as follows: I used an ARIMA model with one autoregressive term. I also included air pressure and air temperature.\n The model predicts this variable at the following sites: TOOK, BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/poz3615/NEON-forecast-challenge-workshop/blob/main/Submit_forecast/forecast_code_template.Rmd", @@ -52,13 +52,13 @@ "temperature", "Daily", "P1D", + "TOOK", "BARC", "CRAM", "LIRO", "PRLA", "PRPO", - "SUGG", - "TOOK" + "SUGG" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/collection.json b/catalog/scores/Aquatics/collection.json index f8ef980019..6f76cb7529 100644 --- a/catalog/scores/Aquatics/collection.json +++ b/catalog/scores/Aquatics/collection.json @@ -78,7 +78,7 @@ "interval": [ [ "2017-02-01T00:00:00Z", - "2024-07-21T00:00:00Z" + "2024-07-24T00:00:00Z" ] ] } diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/collection.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/collection.json index 82321271e5..f82bf9486d 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/collection.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/collection.json @@ -8,11 +8,6 @@ ], "type": "Collection", "links": [ - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_arima.json" - }, { "rel": "item", "type": "application/json", @@ -21,12 +16,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", @@ -41,12 +36,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", @@ -58,6 +53,11 @@ "type": "application/json", "href": "./models/tg_temp_lm.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_humidity_lm_all_sites.json" + }, { "rel": "item", "type": "application/json", @@ -106,7 +106,7 @@ "interval": [ [ "2019-08-12T00:00:00Z", - "2024-06-24T00:00:00Z" + "2024-07-15T00:00:00Z" ] ] } diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json index 61ba657c81..534c8b52ce 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json @@ -9,24 +9,7 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], + [-119.7323, 37.1088], [-119.2622, 37.0334], [-110.8355, 31.9107], [-89.5864, 45.5089], @@ -54,16 +37,33 @@ [-83.5019, 35.689], [-66.8687, 17.9696], [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-67.0769, 18.0213], + [-88.1612, 31.8539], + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], [-99.0588, 35.4106], - [-119.7323, 37.1088] + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, OAES, SJER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", - "end_datetime": "2024-06-24T00:00:00Z", + "end_datetime": "2024-07-15T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,24 +92,7 @@ "abundance", "Weekly", "P1W", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", + "SJER", "SOAP", "SRER", "STEI", @@ -137,8 +120,25 @@ "GRSM", "GUAN", "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", "OAES", - "SJER" + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC" ], "table:columns": [ { diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json index d441787d9e..ffd386c1eb 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json @@ -9,6 +9,13 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], [-81.9934, 29.6893], [-155.3173, 19.5531], [-105.546, 40.2759], @@ -48,22 +55,15 @@ [-96.6129, 39.1104], [-96.5631, 39.1008], [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641] + [-88.1612, 31.8539] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", - "end_datetime": "2024-06-24T00:00:00Z", + "end_datetime": "2024-07-15T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,6 +92,13 @@ "abundance", "Weekly", "P1W", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", "OSBS", "PUUM", "RMNP", @@ -131,14 +138,7 @@ "KONA", "KONZ", "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL" + "LENO" ], "table:columns": [ { diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json index dce7f27332..0a7ada5aa3 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json index ce4f9ebf94..3028202898 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json index 2b6e0ab441..0833c4d765 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json @@ -9,12 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-122.3303, 45.7624], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], [-99.1066, 47.1617], [-145.7514, 63.8811], [-87.8039, 32.5417], @@ -53,13 +47,19 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155] ] }, "properties": { "title": "tg_lasso", - "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BART, BLAN, BONA, CLBJ, CPER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-12-26T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -90,12 +90,6 @@ "abundance", "Weekly", "P1W", - "ABBY", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", "DCFS", "DEJU", "DELA", @@ -134,7 +128,13 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER" ], "table:columns": [ { diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json index 80d79549f6..bbbc9a6bfc 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json index f2af8b5a63..205eeff238 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json index e96603c3fc..7e71a50915 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json @@ -9,6 +9,7 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-122.3303, 45.7624], [-71.2874, 44.0639], [-78.0418, 39.0337], [-147.5026, 65.154], @@ -47,21 +48,20 @@ [-103.0293, 40.4619], [-87.3933, 32.9505], [-119.006, 37.0058], - [-149.3705, 68.6611], [-89.5857, 45.4937], [-95.1921, 39.0404], [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824] + [-156.6194, 71.2824], + [-149.3705, 68.6611] ] }, "properties": { "title": "tg_randfor", - "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BARR, TOOL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-12-26T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ @@ -92,6 +92,7 @@ "abundance", "Weekly", "P1W", + "ABBY", "BART", "BLAN", "BONA", @@ -130,15 +131,14 @@ "STER", "TALL", "TEAK", - "TOOL", "TREE", "UKFS", "UNDE", "WOOD", "WREF", "YELL", - "ABBY", - "BARR" + "BARR", + "TOOL" ], "table:columns": [ { diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json index 6310c6f6eb..ec00373dab 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json @@ -9,7 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-89.5864, 45.5089], [-103.0293, 40.4619], [-87.3933, 32.9505], [-119.006, 37.0058], @@ -55,15 +54,16 @@ [-76.56, 38.8901], [-119.7323, 37.1088], [-119.2622, 37.0334], - [-110.8355, 31.9107] + [-110.8355, 31.9107], + [-89.5864, 45.5089] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", - "end_datetime": "2024-06-24T00:00:00Z", + "end_datetime": "2024-07-15T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,7 +92,6 @@ "abundance", "Weekly", "P1W", - "STEI", "STER", "TALL", "TEAK", @@ -138,7 +137,8 @@ "SERC", "SJER", "SOAP", - "SRER" + "SRER", + "STEI" ], "table:columns": [ { diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json index 69b94b0b27..f5facf1ae9 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json @@ -9,10 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -55,13 +51,17 @@ [-119.006, 37.0058], [-149.3705, 68.6611], [-89.5857, 45.4937], - [-95.1921, 39.0404] + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_temp_lm", - "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -92,10 +92,6 @@ "abundance", "Weekly", "P1W", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +134,11 @@ "TEAK", "TOOL", "TREE", - "UKFS" + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json index 8a3ab3ba85..2e9fd57f10 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/collection.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/collection.json index 2c634f08c4..5c1858c946 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/collection.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/collection.json @@ -26,42 +26,42 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_randfor.json" }, { "rel": "parent", @@ -106,7 +106,7 @@ "interval": [ [ "2019-08-12T00:00:00Z", - "2024-06-24T00:00:00Z" + "2024-07-15T00:00:00Z" ] ] } diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json index ec98e9bcfe..270ce26055 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json @@ -9,61 +9,61 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], [-119.7323, 37.1088], - [-119.2622, 37.0334], + [-66.8687, 17.9696], + [-112.4524, 40.1776], + [-156.6194, 71.2824], [-110.8355, 31.9107], - [-89.5864, 45.5089], + [-110.5391, 44.9535], + [-147.5026, 65.154], + [-106.8425, 32.5907], + [-149.3705, 68.6611], + [-145.7514, 63.8811], + [-149.2133, 63.8758], [-103.0293, 40.4619], - [-87.3933, 32.9505], + [-122.3303, 45.7624], + [-99.1066, 47.1617], + [-84.4686, 31.1948], + [-119.2622, 37.0334], [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], [-99.2413, 47.1282], + [-81.4362, 28.1251], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-155.3173, 19.5531], + [-89.5373, 46.2339], [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], + [-83.5019, 35.689], + [-72.1727, 42.5369], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-84.2826, 35.9641], + [-105.546, 40.2759], + [-89.5857, 45.4937], [-71.2874, 44.0639], [-78.0418, 39.0337], - [-147.5026, 65.154], + [-87.8039, 32.5417], + [-80.5248, 37.3783], + [-81.9934, 29.6893], + [-78.1395, 38.8929], + [-89.5864, 45.5089], + [-76.56, 38.8901], + [-87.3933, 32.9505], + [-88.1612, 31.8539], + [-109.3883, 38.2483], + [-95.1921, 39.0404], [-97.57, 33.4012], [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104] + [-67.0769, 18.0213], + [-99.0588, 35.4106] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Weekly_beetle_community_richness variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_richness variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SJER, GUAN, ONAQ, BARR, SRER, YELL, BONA, JORN, TOOL, DEJU, HEAL, STER, ABBY, DCFS, JERC, SOAP, TEAK, WOOD, DSNY, NIWO, NOGP, PUUM, UNDE, WREF, GRSM, HARV, KONA, KONZ, ORNL, RMNP, TREE, BART, BLAN, DELA, MLBS, OSBS, SCBI, STEI, SERC, TALL, LENO, MOAB, UKFS, CLBJ, CPER, LAJA, OAES.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", - "end_datetime": "2024-06-24T00:00:00Z", + "end_datetime": "2024-07-15T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,53 +92,53 @@ "richness", "Weekly", "P1W", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", "SJER", - "SOAP", + "GUAN", + "ONAQ", + "BARR", "SRER", - "STEI", + "YELL", + "BONA", + "JORN", + "TOOL", + "DEJU", + "HEAL", "STER", - "TALL", + "ABBY", + "DCFS", + "JERC", + "SOAP", "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", "WOOD", + "DSNY", + "NIWO", + "NOGP", + "PUUM", + "UNDE", "WREF", - "YELL", - "ABBY", - "BARR", + "GRSM", + "HARV", + "KONA", + "KONZ", + "ORNL", + "RMNP", + "TREE", "BART", "BLAN", - "BONA", + "DELA", + "MLBS", + "OSBS", + "SCBI", + "STEI", + "SERC", + "TALL", + "LENO", + "MOAB", + "UKFS", "CLBJ", "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA" + "LAJA", + "OAES" ], "table:columns": [ { diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json index 105dd20cab..d402317dc0 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json @@ -9,61 +9,61 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-119.7323, 37.1088], - [-66.8687, 17.9696], - [-112.4524, 40.1776], [-156.6194, 71.2824], - [-110.8355, 31.9107], - [-110.5391, 44.9535], [-147.5026, 65.154], - [-106.8425, 32.5907], [-149.3705, 68.6611], - [-145.7514, 63.8811], - [-149.2133, 63.8758], - [-103.0293, 40.4619], [-122.3303, 45.7624], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-97.57, 33.4012], + [-104.7456, 40.8155], [-99.1066, 47.1617], - [-84.4686, 31.1948], - [-119.2622, 37.0334], - [-119.006, 37.0058], - [-99.2413, 47.1282], + [-145.7514, 63.8811], + [-87.8039, 32.5417], [-81.4362, 28.1251], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-155.3173, 19.5531], - [-89.5373, 46.2339], - [-121.9519, 45.8205], [-83.5019, 35.689], + [-66.8687, 17.9696], [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907], [-96.6129, 39.1104], [-96.5631, 39.1008], - [-84.2826, 35.9641], - [-105.546, 40.2759], - [-89.5857, 45.4937], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-87.8039, 32.5417], + [-67.0769, 18.0213], + [-88.1612, 31.8539], [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], [-78.1395, 38.8929], - [-89.5864, 45.5089], [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], [-87.3933, 32.9505], - [-88.1612, 31.8539], - [-109.3883, 38.2483], + [-119.006, 37.0058], + [-89.5857, 45.4937], [-95.1921, 39.0404], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-67.0769, 18.0213], - [-99.0588, 35.4106] + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: SJER, GUAN, ONAQ, BARR, SRER, YELL, BONA, JORN, TOOL, DEJU, HEAL, STER, ABBY, DCFS, JERC, SOAP, TEAK, WOOD, DSNY, NIWO, NOGP, PUUM, UNDE, WREF, GRSM, HARV, KONA, KONZ, ORNL, RMNP, TREE, BART, BLAN, DELA, MLBS, OSBS, SCBI, STEI, SERC, TALL, LENO, MOAB, UKFS, CLBJ, CPER, LAJA, OAES.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BARR, BONA, TOOL, ABBY, BART, BLAN, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", - "end_datetime": "2024-06-24T00:00:00Z", + "end_datetime": "2024-07-15T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,53 +92,53 @@ "richness", "Weekly", "P1W", - "SJER", - "GUAN", - "ONAQ", "BARR", - "SRER", - "YELL", "BONA", - "JORN", "TOOL", - "DEJU", - "HEAL", - "STER", "ABBY", + "BART", + "BLAN", + "CLBJ", + "CPER", "DCFS", - "JERC", - "SOAP", - "TEAK", - "WOOD", + "DEJU", + "DELA", "DSNY", - "NIWO", - "NOGP", - "PUUM", - "UNDE", - "WREF", "GRSM", + "GUAN", "HARV", + "HEAL", + "JERC", + "JORN", "KONA", "KONZ", - "ORNL", - "RMNP", - "TREE", - "BART", - "BLAN", - "DELA", + "LAJA", + "LENO", "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", "OSBS", + "PUUM", + "RMNP", "SCBI", - "STEI", "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", "TALL", - "LENO", - "MOAB", + "TEAK", + "TREE", "UKFS", - "CLBJ", - "CPER", - "LAJA", - "OAES" + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json index d105479e3c..b9123f6fe6 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All scores for the Weekly_beetle_community_richness variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json index 9bae23d37e..4245927d83 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All scores for the Weekly_beetle_community_richness variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json index 211b9de287..c9ecb5406d 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json @@ -9,6 +9,16 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], [-99.1066, 47.1617], [-145.7514, 63.8811], [-87.8039, 32.5417], @@ -43,23 +53,13 @@ [-87.3933, 32.9505], [-119.006, 37.0058], [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155] + [-95.1921, 39.0404] ] }, "properties": { "title": "tg_lasso", - "description": "All scores for the Weekly_beetle_community_richness variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BART, BLAN, BONA, CLBJ, CPER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_richness variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-12-26T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -90,6 +90,16 @@ "richness", "Weekly", "P1W", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", "DCFS", "DEJU", "DELA", @@ -124,17 +134,7 @@ "TALL", "TEAK", "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER" + "UKFS" ], "table:columns": [ { diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json index 5ff27d1c90..4ed2536fc3 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All scores for the Weekly_beetle_community_richness variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json index 50a0d086e1..1818c0cbfe 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All scores for the Weekly_beetle_community_richness variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json index d72b2a8ded..2207c34f7b 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_randfor", "description": "All scores for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BARR, TOOL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-12-26T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json index 72ab6d1f38..c059e77453 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json @@ -9,6 +9,14 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], [-147.5026, 65.154], [-97.57, 33.4012], [-104.7456, 40.8155], @@ -47,23 +55,15 @@ [-119.006, 37.0058], [-149.3705, 68.6611], [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337] + [-95.1921, 39.0404] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Weekly_beetle_community_richness variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_richness variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", - "end_datetime": "2024-06-24T00:00:00Z", + "end_datetime": "2024-07-15T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,6 +92,14 @@ "richness", "Weekly", "P1W", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", "BONA", "CLBJ", "CPER", @@ -130,15 +138,7 @@ "TEAK", "TOOL", "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN" + "UKFS" ], "table:columns": [ { diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json index 1e18e22c67..54452de2cc 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm", "description": "All scores for the Weekly_beetle_community_richness variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json index e06e7f4f4b..21829d1b6b 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json @@ -9,23 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], [-96.6129, 39.1104], [-96.5631, 39.1008], [-67.0769, 18.0213], @@ -55,13 +38,30 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907] ] }, "properties": { "title": "tg_temp_lm_all_sites", - "description": "All scores for the Weekly_beetle_community_richness variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_richness variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -92,23 +92,6 @@ "richness", "Weekly", "P1W", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", "KONA", "KONZ", "LAJA", @@ -138,7 +121,24 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN" ], "table:columns": [ { diff --git a/catalog/scores/Beetles/collection.json b/catalog/scores/Beetles/collection.json index 209461d86b..68ec664e9a 100644 --- a/catalog/scores/Beetles/collection.json +++ b/catalog/scores/Beetles/collection.json @@ -64,7 +64,7 @@ "interval": [ [ "2017-02-01T00:00:00Z", - "2024-07-21T00:00:00Z" + "2024-07-24T00:00:00Z" ] ] } diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json index 9453b9eace..c3826b1e89 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json @@ -11,62 +11,62 @@ { "rel": "item", "type": "application/json", - "href": "./models/PEG.json" + "href": "./models/NEFIpheno.json" }, { "rel": "item", "type": "application/json", - "href": "./models/DALEC_SIP.json" + "href": "./models/PEG.json" }, { "rel": "item", "type": "application/json", - "href": "./models/EFI_U_P.json" + "href": "./models/Fourier.json" }, { "rel": "item", "type": "application/json", - "href": "./models/PEG_RFR.json" + "href": "./models/CSP_Gwave.json" }, { "rel": "item", "type": "application/json", - "href": "./models/CSP_Gwave.json" + "href": "./models/CU_Pheno.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/ChlorophyllCrusaders.json" }, { "rel": "item", "type": "application/json", - "href": "./models/CU_Pheno.json" + "href": "./models/DALEC_SIP.json" }, { "rel": "item", "type": "application/json", - "href": "./models/ChlorophyllCrusaders.json" + "href": "./models/PEG_RFR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/Fourier.json" + "href": "./models/PEG_RFR0.json" }, { "rel": "item", "type": "application/json", - "href": "./models/NEFIpheno.json" + "href": "./models/EFI_U_P.json" }, { "rel": "item", "type": "application/json", - "href": "./models/PEG_RFR0.json" + "href": "./models/UCSC_P_EDM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/UCSC_P_EDM.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", @@ -81,17 +81,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/greenbears.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/greenbears.json" }, { "rel": "item", @@ -191,7 +191,7 @@ "interval": [ [ "2020-04-27T00:00:00Z", - "2024-07-20T00:00:00Z" + "2024-07-24T00:00:00Z" ] ] } diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/CSP_Gwave.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/CSP_Gwave.json index c84a5d360e..a031ca32ad 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/CSP_Gwave.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/CSP_Gwave.json @@ -9,20 +9,20 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-78.1395, 38.8929], + [-89.5864, 45.5089], + [-95.1921, 39.0404], [-71.2874, 44.0639], [-97.57, 33.4012], [-87.8039, 32.5417], [-83.5019, 35.689], - [-72.1727, 42.5369], - [-78.1395, 38.8929], - [-89.5864, 45.5089], - [-95.1921, 39.0404] + [-72.1727, 42.5369] ] }, "properties": { "title": "CSP_Gwave", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the CSP_Gwave model. Information for the model is provided as follows: The CSP-Gwave (“greenwave”) model represented seasonality using a double logistic\nfunction. Within a single year (growing season), the function consisted of two primary components: one curve for greenup, and another for “brown-down”. It used a corrected sum of the curves for each year to obtain a single, continuously differentiable curve spanning the time period of interest.\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the CSP_Gwave model. Information for the model is provided as follows: The CSP-Gwave (“greenwave”) model represented seasonality using a double logistic\nfunction. Within a single year (growing season), the function consisted of two primary components: one curve for greenup, and another for “brown-down”. It used a corrected sum of the curves for each year to obtain a single, continuously differentiable curve spanning the time period of interest.\n The model predicts this variable at the following sites: SCBI, STEI, UKFS, BART, CLBJ, DELA, GRSM, HARV.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-04-08T00:00:00Z", "end_datetime": "2021-11-21T00:00:00Z", "providers": [ @@ -53,14 +53,14 @@ "gcc_90", "Daily", "P1D", + "SCBI", + "STEI", + "UKFS", "BART", "CLBJ", "DELA", "GRSM", - "HARV", - "SCBI", - "STEI", - "UKFS" + "HARV" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/CU_Pheno.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/CU_Pheno.json index e44dfc07b0..031094fe8d 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/CU_Pheno.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/CU_Pheno.json @@ -22,7 +22,7 @@ "properties": { "title": "CU_Pheno", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the CU_Pheno model. Information for the model is provided as follows: The CU_Pheno model was a deterministic, discrete time compartment model that simulated the transition of pixels between green (G) and non-green (N) color channels as the forest moves through yearly transitions.\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-05-08T00:00:00Z", "end_datetime": "2021-06-27T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json index 5943d506ee..a167e5eedf 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json @@ -16,7 +16,7 @@ "properties": { "title": "ChlorophyllCrusaders", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the ChlorophyllCrusaders model. Information for the model is provided as follows: Our project utilizes a historical GCC data to fit a Dynamic Linear Model (DLM). After this DLM is trained, we utilize forecasted temperature data to predict future GCC data..\n The model predicts this variable at the following sites: HARV, HEAL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-20T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/DALEC_SIP.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/DALEC_SIP.json index 9b2b9fa615..c6288d8def 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/DALEC_SIP.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/DALEC_SIP.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-71.2874, 44.0639], + [-97.57, 33.4012], [-87.8039, 32.5417], [-83.5019, 35.689], [-72.1727, 42.5369], [-89.5864, 45.5089], - [-95.1921, 39.0404], - [-71.2874, 44.0639], - [-97.57, 33.4012] + [-95.1921, 39.0404] ] }, "properties": { "title": "DALEC_SIP", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the DALEC_SIP model. Information for the model is provided as follows: The DALEC-SIP model was a process-based carbon cycle and ecosystem model. It coupled a leaf-to-canopy radiative transfer model based on the spectral invariant properties (SIP) to the Data Assimilation Linked Carbon (DALEC) model.\n The model predicts this variable at the following sites: DELA, GRSM, HARV, STEI, UKFS, BART, CLBJ.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the DALEC_SIP model. Information for the model is provided as follows: The DALEC-SIP model was a process-based carbon cycle and ecosystem model. It coupled a leaf-to-canopy radiative transfer model based on the spectral invariant properties (SIP) to the Data Assimilation Linked Carbon (DALEC) model.\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, STEI, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-03-01T00:00:00Z", "end_datetime": "2021-08-19T00:00:00Z", "providers": [ @@ -52,13 +52,13 @@ "gcc_90", "Daily", "P1D", + "BART", + "CLBJ", "DELA", "GRSM", "HARV", "STEI", - "UKFS", - "BART", - "CLBJ" + "UKFS" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/EFI_U_P.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/EFI_U_P.json index a62992ab98..d2866f828d 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/EFI_U_P.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/EFI_U_P.json @@ -22,7 +22,7 @@ "properties": { "title": "EFI_U_P", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the EFI_U_P model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-05-04T00:00:00Z", "end_datetime": "2021-06-08T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/Fourier.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/Fourier.json index ff89874ca6..594b3cf27b 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/Fourier.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/Fourier.json @@ -22,7 +22,7 @@ "properties": { "title": "Fourier", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the Fourier model. Information for the model is provided as follows: The Fourier model captured the time-dependence of the greening process using the Fourier regression method and a set of temporal basis functions.\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-02-24T00:00:00Z", "end_datetime": "2021-05-31T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/NEFIpheno.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/NEFIpheno.json index 0e433193ea..6456c78d4f 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/NEFIpheno.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/NEFIpheno.json @@ -22,7 +22,7 @@ "properties": { "title": "NEFIpheno", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the NEFIpheno model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-08-25T00:00:00Z", "end_datetime": "2022-01-20T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json index e5d115a069..262ae6f1d7 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json @@ -17,6 +17,16 @@ [-78.1395, 38.8929], [-89.5864, 45.5089], [-95.1921, 39.0404], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624], [-156.6194, 71.2824], [-78.0418, 39.0337], [-147.5026, 65.154], @@ -45,23 +55,13 @@ [-76.56, 38.8901], [-119.7323, 37.1088], [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624] + [-110.8355, 31.9107] ] }, "properties": { "title": "PEG", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS, BARR, BLAN, BONA, CPER, DCFS, DEJU, DSNY, GUAN, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SERC, SJER, SOAP, SRER, STER, TALL, TEAK, TOOL, TREE, UNDE, WOOD, WREF, YELL, ABBY.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS, STER, TALL, TEAK, TOOL, TREE, UNDE, WOOD, WREF, YELL, ABBY, BARR, BLAN, BONA, CPER, DCFS, DEJU, DSNY, GUAN, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SERC, SJER, SOAP, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-02-24T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ @@ -100,6 +100,16 @@ "SCBI", "STEI", "UKFS", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", "BARR", "BLAN", "BONA", @@ -128,17 +138,7 @@ "SERC", "SJER", "SOAP", - "SRER", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY" + "SRER" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG_RFR.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG_RFR.json index 3a3c459e62..89512a3cbf 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG_RFR.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG_RFR.json @@ -9,20 +9,20 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-71.2874, 44.0639], - [-97.57, 33.4012], [-87.8039, 32.5417], [-83.5019, 35.689], [-72.1727, 42.5369], [-78.1395, 38.8929], [-89.5864, 45.5089], - [-95.1921, 39.0404] + [-95.1921, 39.0404], + [-71.2874, 44.0639], + [-97.57, 33.4012] ] }, "properties": { "title": "PEG_RFR", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the PEG_RFR model. Information for the model is provided as follows: The PEG_RFR model was also a multi-output regression model, which predicted 36 days of GCC (t to t+35 days) using immediate past GCC (last 5 days) and GCC value from the last year, meaning (t-5)th to (t+14)th days GCC from last year.\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the PEG_RFR model. Information for the model is provided as follows: The PEG_RFR model was also a multi-output regression model, which predicted 36 days of GCC (t to t+35 days) using immediate past GCC (last 5 days) and GCC value from the last year, meaning (t-5)th to (t+14)th days GCC from last year.\n The model predicts this variable at the following sites: DELA, GRSM, HARV, SCBI, STEI, UKFS, BART, CLBJ.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-03-26T00:00:00Z", "end_datetime": "2021-08-24T00:00:00Z", "providers": [ @@ -53,14 +53,14 @@ "gcc_90", "Daily", "P1D", - "BART", - "CLBJ", "DELA", "GRSM", "HARV", "SCBI", "STEI", - "UKFS" + "UKFS", + "BART", + "CLBJ" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG_RFR0.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG_RFR0.json index 52e25a8575..c0bb676b78 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG_RFR0.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG_RFR0.json @@ -22,7 +22,7 @@ "properties": { "title": "PEG_RFR0", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the PEG_RFR0 model. Information for the model is provided as follows: The PEG_RFR0 model was a multi-output regression model, which predicted 36 days of GCC (to next 35 days) using immediate past GCC (last 5 days) as well as GCC value from the last year (25 days)..\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-03-26T00:00:00Z", "end_datetime": "2021-08-24T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/UCSC_P_EDM.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/UCSC_P_EDM.json index e55d8725de..a41194627f 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/UCSC_P_EDM.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/UCSC_P_EDM.json @@ -22,7 +22,7 @@ "properties": { "title": "UCSC_P_EDM", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the UCSC_P_EDM model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2020-04-27T00:00:00Z", "end_datetime": "2021-11-22T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/VT_Ph_GDD.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/VT_Ph_GDD.json index 94a3629df6..7b9908e7dd 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/VT_Ph_GDD.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/VT_Ph_GDD.json @@ -22,7 +22,7 @@ "properties": { "title": "VT_Ph_GDD", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the VT_Ph_GDD model. Information for the model is provided as follows: The model was built using a logistic growth process model (Eq. S14) fit in a Bayesian framework to predict spring greenness, where = cumulative growing degree day (GDD) on the 𝑥𝑡 day being predicted. GDD was calculated using Eq. (S15) on each day a forecast was made, where is the maximum air temperature and is the minimum air temperature on 𝑚𝑎𝑥𝑡𝑒𝑚𝑝 𝑚𝑖𝑛𝑡𝑒𝑚𝑝 a given day, and 𝑇 was a constant set at 10°C.\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-04-15T00:00:00Z", "end_datetime": "2021-05-30T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json index e4c25a3adc..3e6fb27627 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json @@ -9,20 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], [-89.5864, 45.5089], [-103.0293, 40.4619], [-87.3933, 32.9505], @@ -55,13 +41,27 @@ [-96.5631, 39.1008], [-67.0769, 18.0213], [-88.1612, 31.8539], - [-80.5248, 37.3783] + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107] ] }, "properties": { "title": "cb_prophet", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-05-29T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ @@ -92,20 +92,6 @@ "gcc_90", "Daily", "P1D", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", "STEI", "STER", "TALL", @@ -138,7 +124,21 @@ "KONZ", "LAJA", "LENO", - "MLBS" + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json index 13a359e745..bafb441e83 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json @@ -9,13 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-112.4524, 40.1776], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-95.1921, 39.0404], - [-99.2413, 47.1282], [-71.2874, 44.0639], [-97.57, 33.4012], [-104.7456, 40.8155], @@ -27,16 +20,13 @@ [-96.5631, 39.1008], [-80.5248, 37.3783], [-99.0588, 35.4106], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-89.5373, 46.2339], - [-121.9519, 45.8205], - [-110.5391, 44.9535], + [-112.4524, 40.1776], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-95.1921, 39.0404], + [-99.2413, 47.1282], [-122.3303, 45.7624], [-156.6194, 71.2824], [-78.0418, 39.0337], @@ -55,15 +45,25 @@ [-84.2826, 35.9641], [-81.9934, 29.6893], [-155.3173, 19.5531], - [-105.546, 40.2759] + [-105.546, 40.2759], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-89.5373, 46.2339], + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ONAQ, SCBI, SERC, SRER, STEI, UKFS, WOOD, BART, CLBJ, CPER, DELA, DSNY, GRSM, HARV, JORN, KONZ, MLBS, OAES, SJER, SOAP, STER, TALL, TEAK, TOOL, TREE, UNDE, WREF, YELL, ABBY, BARR, BLAN, BONA, DCFS, DEJU, GUAN, HEAL, JERC, KONA, LAJA, LENO, MOAB, NIWO, NOGP, ORNL, OSBS, PUUM, RMNP.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BART, CLBJ, CPER, DELA, DSNY, GRSM, HARV, JORN, KONZ, MLBS, OAES, ONAQ, SCBI, SERC, SRER, STEI, UKFS, WOOD, ABBY, BARR, BLAN, BONA, DCFS, DEJU, GUAN, HEAL, JERC, KONA, LAJA, LENO, MOAB, NIWO, NOGP, ORNL, OSBS, PUUM, RMNP, SJER, SOAP, STER, TALL, TEAK, TOOL, TREE, UNDE, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-01-01T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", @@ -92,13 +92,6 @@ "gcc_90", "Daily", "P1D", - "ONAQ", - "SCBI", - "SERC", - "SRER", - "STEI", - "UKFS", - "WOOD", "BART", "CLBJ", "CPER", @@ -110,16 +103,13 @@ "KONZ", "MLBS", "OAES", - "SJER", - "SOAP", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UNDE", - "WREF", - "YELL", + "ONAQ", + "SCBI", + "SERC", + "SRER", + "STEI", + "UKFS", + "WOOD", "ABBY", "BARR", "BLAN", @@ -138,7 +128,17 @@ "ORNL", "OSBS", "PUUM", - "RMNP" + "RMNP", + "SJER", + "SOAP", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UNDE", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/greenbears.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/greenbears.json index 9f070f33ec..09648dcf13 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/greenbears.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/greenbears.json @@ -22,7 +22,7 @@ "properties": { "title": "greenbears", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the greenbears model. Information for the model is provided as follows: They started with a simple parametric approach, and tried to fit a smooth cyclic curve based on day-of-year for each site. They used generalized additive models (GAMs) to estimate an annual smooth cycle in the GCC greenness index (Wood, 2017).\n The model predicts this variable at the following sites: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-02-22T00:00:00Z", "end_datetime": "2021-03-28T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json index 6d06b29b5e..6770edba99 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json @@ -9,6 +9,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -51,19 +55,15 @@ [-119.006, 37.0058], [-149.3705, 68.6611], [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-95.1921, 39.0404] ] }, "properties": { "title": "persistenceRW", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-08-24T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", @@ -92,6 +92,10 @@ "gcc_90", "Daily", "P1D", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -134,11 +138,7 @@ "TEAK", "TOOL", "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "UKFS" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json index 45f529b746..46b07691a7 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json @@ -9,6 +9,17 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], [-145.7514, 63.8811], [-87.8039, 32.5417], [-81.4362, 28.1251], @@ -44,26 +55,15 @@ [-149.3705, 68.6611], [-89.5857, 45.4937], [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617] + [-89.5373, 46.2339] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,6 +92,17 @@ "gcc_90", "Daily", "P1D", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", "DEJU", "DELA", "DSNY", @@ -127,18 +138,7 @@ "TOOL", "TREE", "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS" + "UNDE" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json index 27ad017347..1ebfb1a50d 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json @@ -9,6 +9,28 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-67.0769, 18.0213], + [-88.1612, 31.8539], + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], [-78.1395, 38.8929], [-76.56, 38.8901], [-119.7323, 37.1088], @@ -33,37 +55,15 @@ [-97.57, 33.4012], [-104.7456, 40.8155], [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759] + [-145.7514, 63.8811] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,6 +92,28 @@ "gcc_90", "Daily", "P1D", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", "SCBI", "SERC", "SJER", @@ -116,29 +138,7 @@ "CLBJ", "CPER", "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP" + "DEJU" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json index 5656522a05..6fb1622e21 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json @@ -9,13 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -55,13 +48,20 @@ [-89.5864, 45.5089], [-103.0293, 40.4619], [-87.3933, 32.9505], - [-119.006, 37.0058] + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,13 +92,6 @@ "gcc_90", "Daily", "P1D", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +131,14 @@ "STEI", "STER", "TALL", - "TEAK" + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index fef232c4da..99e7e32738 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -9,13 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], [-119.7323, 37.1088], [-119.2622, 37.0334], [-110.8355, 31.9107], @@ -55,13 +48,20 @@ [-109.3883, 38.2483], [-105.5824, 40.0543], [-100.9154, 46.7697], - [-99.0588, 35.4106] + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901] ] }, "properties": { "title": "tg_humidity_lm_all_sites", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,13 +92,6 @@ "gcc_90", "Daily", "P1D", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", "SJER", "SOAP", "SRER", @@ -138,7 +131,14 @@ "MOAB", "NIWO", "NOGP", - "OAES" + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json index 83debf3efd..ccbb71eea5 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_lasso", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json index c73bed9975..4f310eb0b4 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json @@ -9,7 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -55,13 +54,14 @@ [-95.1921, 39.0404], [-89.5373, 46.2339], [-99.2413, 47.1282], - [-121.9519, 45.8205] + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_precip_lm", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,7 +92,6 @@ "gcc_90", "Daily", "P1D", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +137,8 @@ "UKFS", "UNDE", "WOOD", - "WREF" + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json index 21bb618509..f6d4705b7a 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json index 6c7903baee..9c45fbf411 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json @@ -9,21 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -55,13 +40,28 @@ [-81.9934, 29.6893], [-155.3173, 19.5531], [-105.546, 40.2759], - [-78.1395, 38.8929] + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_randfor", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,21 +92,6 @@ "gcc_90", "Daily", "P1D", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +123,22 @@ "OSBS", "PUUM", "RMNP", - "SCBI" + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json index ea9ea4ce20..6d56b797db 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json @@ -9,11 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], [-149.2133, 63.8758], [-84.4686, 31.1948], [-106.8425, 32.5907], @@ -55,15 +50,20 @@ [-97.57, 33.4012], [-104.7456, 40.8155], [-99.1066, 47.1617], - [-145.7514, 63.8811] + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", - "end_datetime": "2024-07-20T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,11 +92,6 @@ "gcc_90", "Daily", "P1D", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", "HEAL", "JERC", "JORN", @@ -138,7 +133,12 @@ "CLBJ", "CPER", "DCFS", - "DEJU" + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json index 2116bb0264..95a6c890b1 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json @@ -9,15 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], [-87.8039, 32.5417], [-81.4362, 28.1251], [-83.5019, 35.689], @@ -55,13 +46,22 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811] ] }, "properties": { "title": "tg_temp_lm", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,15 +92,6 @@ "gcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", "DELA", "DSNY", "GRSM", @@ -138,7 +129,16 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json index 978d903304..30acd06dc7 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -9,22 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], [-78.0418, 39.0337], @@ -55,13 +39,29 @@ [-81.9934, 29.6893], [-155.3173, 19.5531], [-105.546, 40.2759], - [-78.1395, 38.8929] + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624] ] }, "properties": { "title": "tg_temp_lm_all_sites", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,22 +92,6 @@ "gcc_90", "Daily", "P1D", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", "BARR", "BART", "BLAN", @@ -138,7 +122,23 @@ "OSBS", "PUUM", "RMNP", - "SCBI" + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/xgboost_parallel.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/xgboost_parallel.json index 79bc74ccdf..9669f95e21 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/xgboost_parallel.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/xgboost_parallel.json @@ -9,11 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], [-88.1612, 31.8539], [-80.5248, 37.3783], [-109.3883, 38.2483], @@ -55,13 +50,18 @@ [-83.5019, 35.689], [-66.8687, 17.9696], [-72.1727, 42.5369], - [-149.2133, 63.8758] + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-67.0769, 18.0213] ] }, "properties": { "title": "xgboost_parallel", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the xgboost_parallel model. Information for the model is provided as follows: The XGBoost model is an extreme gradient boosted random forest (XGBoost) machine learning\nmodel that uses predicted atmospheric conditions and day of year as covariates. This model utilises the\nxgboost R package (Chen & Guestrin 2016; Chen et al., 2023)..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the xgboost_parallel model. Information for the model is provided as follows: The XGBoost model is an extreme gradient boosted random forest (XGBoost) machine learning\nmodel that uses predicted atmospheric conditions and day of year as covariates. This model utilises the\nxgboost R package (Chen & Guestrin 2016; Chen et al., 2023)..\n The model predicts this variable at the following sites: LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-03-28T00:00:00Z", "end_datetime": "2023-12-08T00:00:00Z", "providers": [ @@ -92,11 +92,6 @@ "gcc_90", "Daily", "P1D", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", "LENO", "MLBS", "MOAB", @@ -138,7 +133,12 @@ "GRSM", "GUAN", "HARV", - "HEAL" + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json index 1f8a746383..dec8191693 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json @@ -51,12 +51,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", @@ -136,7 +136,7 @@ "interval": [ [ "2021-01-01T00:00:00Z", - "2024-07-21T00:00:00Z" + "2024-07-24T00:00:00Z" ] ] } diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json index 1d82ab6d96..970cd28411 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json @@ -9,6 +9,29 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-83.5019, 35.689], + [-72.1727, 42.5369], + [-78.1395, 38.8929], + [-89.5864, 45.5089], + [-95.1921, 39.0404], + [-71.2874, 44.0639], + [-97.57, 33.4012], + [-87.8039, 32.5417], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-81.4362, 28.1251], + [-66.8687, 17.9696], + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907], + [-96.6129, 39.1104], [-96.5631, 39.1008], [-67.0769, 18.0213], [-88.1612, 31.8539], @@ -22,46 +45,23 @@ [-81.9934, 29.6893], [-155.3173, 19.5531], [-105.546, 40.2759], - [-78.1395, 38.8929], [-76.56, 38.8901], [-119.7323, 37.1088], [-119.2622, 37.0334], [-110.8355, 31.9107], - [-89.5864, 45.5089], [-103.0293, 40.4619], [-87.3933, 32.9505], [-119.006, 37.0058], [-149.3705, 68.6611], [-89.5857, 45.4937], - [-95.1921, 39.0404], [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104] + [-99.2413, 47.1282] ] }, "properties": { "title": "PEG", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: GRSM, HARV, SCBI, STEI, UKFS, BART, CLBJ, DELA, WREF, YELL, ABBY, BARR, BLAN, BONA, CPER, DCFS, DEJU, DSNY, GUAN, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SERC, SJER, SOAP, SRER, STER, TALL, TEAK, TOOL, TREE, UNDE, WOOD.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-09-12T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ @@ -92,6 +92,29 @@ "rcc_90", "Daily", "P1D", + "GRSM", + "HARV", + "SCBI", + "STEI", + "UKFS", + "BART", + "CLBJ", + "DELA", + "WREF", + "YELL", + "ABBY", + "BARR", + "BLAN", + "BONA", + "CPER", + "DCFS", + "DEJU", + "DSNY", + "GUAN", + "HEAL", + "JERC", + "JORN", + "KONA", "KONZ", "LAJA", "LENO", @@ -105,40 +128,17 @@ "OSBS", "PUUM", "RMNP", - "SCBI", "SERC", "SJER", "SOAP", "SRER", - "STEI", "STER", "TALL", "TEAK", "TOOL", "TREE", - "UKFS", "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA" + "WOOD" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json index 1496907b46..c77e290fa7 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json @@ -9,23 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-122.3303, 45.7624], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], [-80.5248, 37.3783], [-109.3883, 38.2483], [-105.5824, 40.0543], @@ -51,19 +34,36 @@ [-99.2413, 47.1282], [-121.9519, 45.8205], [-110.5391, 44.9535], - [-147.5026, 65.154], + [-122.3303, 45.7624], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-84.4686, 31.1948], + [-106.8425, 32.5907], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-67.0769, 18.0213], + [-88.1612, 31.8539], [-145.7514, 63.8811], [-149.2133, 63.8758], + [-147.5026, 65.154], [-149.3705, 68.6611], [-156.6194, 71.2824] ] }, "properties": { "title": "baseline_ensemble", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, TOOL, BARR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, DEJU, HEAL, BONA, TOOL, BARR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", - "end_datetime": "2024-07-21T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/NEON-simple-baselines/blob/main/Models/baseline_ensemble.R", @@ -92,23 +92,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BART", - "BLAN", - "CLBJ", - "CPER", - "DCFS", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", "MLBS", "MOAB", "NIWO", @@ -134,9 +117,26 @@ "WOOD", "WREF", "YELL", - "BONA", + "ABBY", + "BART", + "BLAN", + "CLBJ", + "CPER", + "DCFS", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", "DEJU", "HEAL", + "BONA", "TOOL", "BARR" ], diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json index 3dac8e078f..1ca84d6f8d 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json @@ -61,7 +61,7 @@ "properties": { "title": "cb_prophet", "description": "All scores for the Daily_Red_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-05-29T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json index 3ec4013058..0d20dc98d0 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json @@ -9,15 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-80.5248, 37.3783], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-95.1921, 39.0404], - [-99.2413, 47.1282], [-71.2874, 44.0639], [-97.57, 33.4012], [-104.7456, 40.8155], @@ -27,6 +18,29 @@ [-72.1727, 42.5369], [-106.8425, 32.5907], [-96.5631, 39.1008], + [-80.5248, 37.3783], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-95.1921, 39.0404], + [-99.2413, 47.1282], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-89.5373, 46.2339], + [-121.9519, 45.8205], + [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-78.0418, 39.0337], @@ -41,29 +55,15 @@ [-88.1612, 31.8539], [-109.3883, 38.2483], [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-89.5373, 46.2339], - [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-100.9154, 46.7697] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: MLBS, OAES, ONAQ, SCBI, SERC, SRER, STEI, UKFS, WOOD, BART, CLBJ, CPER, DELA, DSNY, GRSM, HARV, JORN, KONZ, ABBY, BARR, BLAN, BONA, DCFS, DEJU, GUAN, HEAL, JERC, KONA, LAJA, LENO, MOAB, NIWO, NOGP, ORNL, OSBS, PUUM, RMNP, SJER, SOAP, STER, TALL, TEAK, TOOL, TREE, UNDE, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BART, CLBJ, CPER, DELA, DSNY, GRSM, HARV, JORN, KONZ, MLBS, OAES, ONAQ, SCBI, SERC, SRER, STEI, UKFS, WOOD, ORNL, OSBS, PUUM, RMNP, SJER, SOAP, STER, TALL, TEAK, TOOL, TREE, UNDE, WREF, YELL, ABBY, BARR, BLAN, BONA, DCFS, DEJU, GUAN, HEAL, JERC, KONA, LAJA, LENO, MOAB, NIWO, NOGP.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-01-01T00:00:00Z", - "end_datetime": "2024-07-21T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", @@ -92,15 +92,6 @@ "rcc_90", "Daily", "P1D", - "MLBS", - "OAES", - "ONAQ", - "SCBI", - "SERC", - "SRER", - "STEI", - "UKFS", - "WOOD", "BART", "CLBJ", "CPER", @@ -110,6 +101,29 @@ "HARV", "JORN", "KONZ", + "MLBS", + "OAES", + "ONAQ", + "SCBI", + "SERC", + "SRER", + "STEI", + "UKFS", + "WOOD", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SJER", + "SOAP", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UNDE", + "WREF", + "YELL", "ABBY", "BARR", "BLAN", @@ -124,21 +138,7 @@ "LENO", "MOAB", "NIWO", - "NOGP", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SJER", - "SOAP", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UNDE", - "WREF", - "YELL" + "NOGP" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json index 16a05e5933..fef2b85b5e 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json @@ -9,26 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -55,15 +35,35 @@ [-105.5824, 40.0543], [-100.9154, 46.7697], [-99.0588, 35.4106], - [-112.4524, 40.1776] + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "persistenceRW", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-08-24T00:00:00Z", - "end_datetime": "2024-07-21T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", @@ -92,26 +92,6 @@ "rcc_90", "Daily", "P1D", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +118,27 @@ "NIWO", "NOGP", "OAES", - "ONAQ" + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json index edc199404d..f5e63b1fa8 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json @@ -9,6 +9,15 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], [-110.8355, 31.9107], [-89.5864, 45.5089], [-103.0293, 40.4619], @@ -46,24 +55,15 @@ [-109.3883, 38.2483], [-105.5824, 40.0543], [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334] + [-99.0588, 35.4106] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", - "end_datetime": "2024-07-21T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,6 +92,15 @@ "rcc_90", "Daily", "P1D", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", "SRER", "STEI", "STER", @@ -129,16 +138,7 @@ "MOAB", "NIWO", "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP" + "OAES" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json index c51e4c4bf8..40e109b93c 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json @@ -9,6 +9,13 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], [-155.3173, 19.5531], [-105.546, 40.2759], [-78.1395, 38.8929], @@ -48,22 +55,15 @@ [-96.5631, 39.1008], [-67.0769, 18.0213], [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893] + [-80.5248, 37.3783] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", - "end_datetime": "2024-07-21T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,6 +92,13 @@ "rcc_90", "Daily", "P1D", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", "PUUM", "RMNP", "SCBI", @@ -131,14 +138,7 @@ "KONZ", "LAJA", "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS" + "MLBS" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json index 9d31e79950..e19444e423 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json @@ -9,8 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-122.3303, 45.7624], - [-156.6194, 71.2824], [-71.2874, 44.0639], [-78.0418, 39.0337], [-147.5026, 65.154], @@ -55,13 +53,15 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,8 +92,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", "BART", "BLAN", "BONA", @@ -138,7 +136,9 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 69405c7b72..5e70ad50ac 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -9,6 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], [-84.4686, 31.1948], [-106.8425, 32.5907], [-96.6129, 39.1104], @@ -40,28 +55,13 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758] + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_humidity_lm_all_sites", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,6 +92,21 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -123,22 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json index d21f42fd90..3a1530862e 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json @@ -9,6 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], [-84.4686, 31.1948], [-106.8425, 32.5907], [-96.6129, 39.1104], @@ -42,26 +55,13 @@ [-121.9519, 45.8205], [-110.5391, 44.9535], [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758] + [-156.6194, 71.2824] ] }, "properties": { "title": "tg_lasso", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,6 +92,19 @@ "rcc_90", "Daily", "P1D", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -125,20 +138,7 @@ "WREF", "YELL", "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "BARR" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json index 539adfd1a8..58ecb3506a 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json @@ -9,22 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -55,13 +39,29 @@ [-84.2826, 35.9641], [-81.9934, 29.6893], [-155.3173, 19.5531], - [-105.546, 40.2759] + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_precip_lm", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,22 +92,6 @@ "rcc_90", "Daily", "P1D", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +122,23 @@ "ORNL", "OSBS", "PUUM", - "RMNP" + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json index f7dcc50e64..835b7e35a2 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -9,8 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-156.6194, 71.2824], - [-71.2874, 44.0639], [-78.0418, 39.0337], [-147.5026, 65.154], [-97.57, 33.4012], @@ -55,13 +53,15 @@ [-99.2413, 47.1282], [-121.9519, 45.8205], [-110.5391, 44.9535], - [-122.3303, 45.7624] + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,8 +92,6 @@ "rcc_90", "Daily", "P1D", - "BARR", - "BART", "BLAN", "BONA", "CLBJ", @@ -138,7 +136,9 @@ "WOOD", "WREF", "YELL", - "ABBY" + "ABBY", + "BARR", + "BART" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json index 88aa66c64e..b33ea721ab 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json @@ -9,24 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], [-110.8355, 31.9107], [-89.5864, 45.5089], [-103.0293, 40.4619], @@ -55,13 +37,31 @@ [-72.1727, 42.5369], [-149.2133, 63.8758], [-84.4686, 31.1948], - [-106.8425, 32.5907] + [-106.8425, 32.5907], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-67.0769, 18.0213], + [-88.1612, 31.8539], + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334] ] }, "properties": { "title": "tg_randfor", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,24 +92,6 @@ "rcc_90", "Daily", "P1D", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", "SRER", "STEI", "STER", @@ -138,7 +120,25 @@ "HARV", "HEAL", "JERC", - "JORN" + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json index d870e8bba1..3f3c4f2f39 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json @@ -9,6 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], [-72.1727, 42.5369], [-149.2133, 63.8758], [-84.4686, 31.1948], @@ -42,28 +55,15 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696] + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", - "end_datetime": "2024-07-21T00:00:00Z", + "end_datetime": "2024-07-24T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,6 +92,19 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", "HARV", "HEAL", "JERC", @@ -125,20 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN" + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json index f5e340072d..afb321d1d7 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json @@ -9,6 +9,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-106.8425, 32.5907], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-67.0769, 18.0213], [-88.1612, 31.8539], [-80.5248, 37.3783], [-109.3883, 38.2483], @@ -51,17 +55,13 @@ [-66.8687, 17.9696], [-72.1727, 42.5369], [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213] + [-84.4686, 31.1948] ] }, "properties": { "title": "tg_temp_lm", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,6 +92,10 @@ "rcc_90", "Daily", "P1D", + "JORN", + "KONA", + "KONZ", + "LAJA", "LENO", "MLBS", "MOAB", @@ -134,11 +138,7 @@ "GUAN", "HARV", "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA" + "JERC" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json index 4570ae792e..50d61ec5ef 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/xgboost_parallel.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/xgboost_parallel.json index f82e53425b..8cd8762851 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/xgboost_parallel.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/xgboost_parallel.json @@ -9,29 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -55,13 +32,36 @@ [-88.1612, 31.8539], [-80.5248, 37.3783], [-109.3883, 38.2483], - [-105.5824, 40.0543] + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "xgboost_parallel", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the xgboost_parallel model. Information for the model is provided as follows: The XGBoost model is an extreme gradient boosted random forest (XGBoost) machine learning\nmodel that uses predicted atmospheric conditions and day of year as covariates. This model utilises the\nxgboost R package (Chen & Guestrin 2016; Chen et al., 2023)..\n The model predicts this variable at the following sites: NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the xgboost_parallel model. Information for the model is provided as follows: The XGBoost model is an extreme gradient boosted random forest (XGBoost) machine learning\nmodel that uses predicted atmospheric conditions and day of year as covariates. This model utilises the\nxgboost R package (Chen & Guestrin 2016; Chen et al., 2023)..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-03-28T00:00:00Z", "end_datetime": "2023-12-08T00:00:00Z", "providers": [ @@ -92,29 +92,6 @@ "rcc_90", "Daily", "P1D", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +115,30 @@ "LENO", "MLBS", "MOAB", - "NIWO" + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Phenology/collection.json b/catalog/scores/Phenology/collection.json index 4d9f6019f7..4c70340385 100644 --- a/catalog/scores/Phenology/collection.json +++ b/catalog/scores/Phenology/collection.json @@ -73,7 +73,7 @@ "interval": [ [ "2017-02-01T00:00:00Z", - "2024-07-21T00:00:00Z" + "2024-07-24T00:00:00Z" ] ] } diff --git a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/collection.json b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/collection.json index c5f26b7c39..e76ac2a304 100644 --- a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/collection.json +++ b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/collection.json @@ -11,12 +11,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/climatology.json" }, { "rel": "item", diff --git a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/BU_SIPNET.json b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/BU_SIPNET.json index 83a53bd008..aab86b0182 100644 --- a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/BU_SIPNET.json +++ b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/BU_SIPNET.json @@ -18,7 +18,7 @@ "properties": { "title": "BU_SIPNET", "description": "All scores for the 30min_Net_ecosystem_exchange variable for the BU_SIPNET model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, KONZ, OSBS, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-01-01T00:00:00Z", "end_datetime": "2021-10-11T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/IU_Eco2021.json b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/IU_Eco2021.json index c5df6e5000..2b5073f263 100644 --- a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/IU_Eco2021.json +++ b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/IU_Eco2021.json @@ -18,7 +18,7 @@ "properties": { "title": "IU_Eco2021", "description": "All scores for the 30min_Net_ecosystem_exchange variable for the IU_Eco2021 model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, KONZ, OSBS, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-04-25T00:00:00Z", "end_datetime": "2021-05-30T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/UCB_XT.json b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/UCB_XT.json index bcb28bd9ed..06493ba263 100644 --- a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/UCB_XT.json +++ b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/UCB_XT.json @@ -18,7 +18,7 @@ "properties": { "title": "UCB_XT", "description": "All scores for the 30min_Net_ecosystem_exchange variable for the UCB_XT model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, KONZ, OSBS, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2017-02-01T00:00:00Z", "end_datetime": "2020-12-31T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/VT_NEET.json b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/VT_NEET.json index 2e8519b7a9..1dd0787cbc 100644 --- a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/VT_NEET.json +++ b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/VT_NEET.json @@ -18,7 +18,7 @@ "properties": { "title": "VT_NEET", "description": "All scores for the 30min_Net_ecosystem_exchange variable for the VT_NEET model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, KONZ, OSBS, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-03-01T00:00:00Z", "end_datetime": "2021-05-06T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/cb_prophet.json b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/cb_prophet.json index 906c02697d..ee76372cb2 100644 --- a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/cb_prophet.json +++ b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/cb_prophet.json @@ -9,27 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], [-99.1066, 47.1617], [-145.7514, 63.8811], [-87.8039, 32.5417], @@ -55,13 +34,34 @@ [-155.3173, 19.5531], [-105.546, 40.2759], [-78.1395, 38.8929], - [-76.56, 38.8901] + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155] ] }, "properties": { "title": "cb_prophet", - "description": "All scores for the 30min_Net_ecosystem_exchange variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the 30min_Net_ecosystem_exchange variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-06-08T00:00:00Z", "end_datetime": "2023-06-27T00:00:00Z", "providers": [ @@ -92,27 +92,6 @@ "nee", "30min", "PT30M", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", "DCFS", "DEJU", "DELA", @@ -138,7 +117,28 @@ "PUUM", "RMNP", "SCBI", - "SERC" + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json index cc444c2ca0..c5f7608fc1 100644 --- a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json +++ b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json @@ -9,6 +9,14 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907], + [-96.6129, 39.1104], [-96.5631, 39.1008], [-67.0769, 18.0213], [-88.1612, 31.8539], @@ -47,21 +55,13 @@ [-104.7456, 40.8155], [-99.1066, 47.1617], [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104] + [-87.8039, 32.5417] ] }, "properties": { "title": "climatology", - "description": "All scores for the 30min_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the 30min_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-02-01T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ @@ -92,6 +92,14 @@ "nee", "30min", "PT30M", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", "KONZ", "LAJA", "LENO", @@ -130,15 +138,7 @@ "CPER", "DCFS", "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA" + "DELA" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/hist30min.json b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/hist30min.json index 41ca39daae..2946d8be01 100644 --- a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/hist30min.json +++ b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/hist30min.json @@ -18,7 +18,7 @@ "properties": { "title": "hist30min", "description": "All scores for the 30min_Net_ecosystem_exchange variable for the hist30min model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, KONZ, OSBS, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-01-01T00:00:00Z", "end_datetime": "2022-06-05T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/30min_latent_heat_flux/collection.json b/catalog/scores/Terrestrial/30min_latent_heat_flux/collection.json index 150e1c79c2..35c06fe70d 100644 --- a/catalog/scores/Terrestrial/30min_latent_heat_flux/collection.json +++ b/catalog/scores/Terrestrial/30min_latent_heat_flux/collection.json @@ -11,12 +11,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/climatology.json" }, { "rel": "item", diff --git a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/BU_SIPNET.json b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/BU_SIPNET.json index 40623349ed..a5e493de3f 100644 --- a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/BU_SIPNET.json +++ b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/BU_SIPNET.json @@ -18,7 +18,7 @@ "properties": { "title": "BU_SIPNET", "description": "All scores for the 30min_latent_heat_flux variable for the BU_SIPNET model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, KONZ, OSBS, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-01-01T00:00:00Z", "end_datetime": "2021-10-11T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/IU_Eco2021.json b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/IU_Eco2021.json index 771379a184..95e5971af8 100644 --- a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/IU_Eco2021.json +++ b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/IU_Eco2021.json @@ -18,7 +18,7 @@ "properties": { "title": "IU_Eco2021", "description": "All scores for the 30min_latent_heat_flux variable for the IU_Eco2021 model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, KONZ, OSBS, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-04-25T00:00:00Z", "end_datetime": "2021-05-30T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/UCB_XT.json b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/UCB_XT.json index 356c55ee37..5d7c167571 100644 --- a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/UCB_XT.json +++ b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/UCB_XT.json @@ -18,7 +18,7 @@ "properties": { "title": "UCB_XT", "description": "All scores for the 30min_latent_heat_flux variable for the UCB_XT model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, KONZ, OSBS, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2017-02-01T00:00:00Z", "end_datetime": "2020-12-31T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/VT_NEET.json b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/VT_NEET.json index ad0ba4a7fa..b4edbc753a 100644 --- a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/VT_NEET.json +++ b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/VT_NEET.json @@ -18,7 +18,7 @@ "properties": { "title": "VT_NEET", "description": "All scores for the 30min_latent_heat_flux variable for the VT_NEET model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, KONZ, OSBS, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-03-01T00:00:00Z", "end_datetime": "2021-05-06T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/cb_prophet.json b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/cb_prophet.json index db197be060..0c7f87bda0 100644 --- a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/cb_prophet.json +++ b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/cb_prophet.json @@ -9,7 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-99.1066, 47.1617], [-145.7514, 63.8811], [-87.8039, 32.5417], [-81.4362, 28.1251], @@ -55,13 +54,14 @@ [-78.0418, 39.0337], [-147.5026, 65.154], [-97.57, 33.4012], - [-104.7456, 40.8155] + [-104.7456, 40.8155], + [-99.1066, 47.1617] ] }, "properties": { "title": "cb_prophet", - "description": "All scores for the 30min_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the 30min_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-06-08T00:00:00Z", "end_datetime": "2023-06-27T00:00:00Z", "providers": [ @@ -92,7 +92,6 @@ "le", "30min", "PT30M", - "DCFS", "DEJU", "DELA", "DSNY", @@ -138,7 +137,8 @@ "BLAN", "BONA", "CLBJ", - "CPER" + "CPER", + "DCFS" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/climatology.json b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/climatology.json index 322d93553a..352bc7378f 100644 --- a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/climatology.json +++ b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/climatology.json @@ -9,6 +9,24 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-97.57, 33.4012], + [-96.5631, 39.1008], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-119.7323, 37.1088], + [-110.8355, 31.9107], + [-87.3933, 32.9505], + [-89.5373, 46.2339], + [-121.9519, 45.8205], + [-71.2874, 44.0639], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], [-81.4362, 28.1251], [-83.5019, 35.689], [-66.8687, 17.9696], @@ -17,7 +35,6 @@ [-84.4686, 31.1948], [-106.8425, 32.5907], [-96.6129, 39.1104], - [-96.5631, 39.1008], [-67.0769, 18.0213], [-88.1612, 31.8539], [-80.5248, 37.3783], @@ -26,42 +43,25 @@ [-100.9154, 46.7697], [-99.0588, 35.4106], [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], [-155.3173, 19.5531], [-105.546, 40.2759], [-78.1395, 38.8929], [-76.56, 38.8901], - [-119.7323, 37.1088], [-119.2622, 37.0334], - [-110.8355, 31.9107], [-89.5864, 45.5089], [-103.0293, 40.4619], - [-87.3933, 32.9505], [-119.006, 37.0058], [-149.3705, 68.6611], [-89.5857, 45.4937], [-95.1921, 39.0404], - [-89.5373, 46.2339], [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417] + [-110.5391, 44.9535] ] }, "properties": { "title": "climatology", - "description": "All scores for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: CLBJ, KONZ, ORNL, OSBS, SJER, SRER, TALL, UNDE, WREF, BART, ABBY, BARR, BLAN, BONA, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, PUUM, RMNP, SCBI, SERC, SOAP, STEI, STER, TEAK, TOOL, TREE, UKFS, WOOD, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-02-01T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ @@ -92,6 +92,24 @@ "le", "30min", "PT30M", + "CLBJ", + "KONZ", + "ORNL", + "OSBS", + "SJER", + "SRER", + "TALL", + "UNDE", + "WREF", + "BART", + "ABBY", + "BARR", + "BLAN", + "BONA", + "CPER", + "DCFS", + "DEJU", + "DELA", "DSNY", "GRSM", "GUAN", @@ -100,7 +118,6 @@ "JERC", "JORN", "KONA", - "KONZ", "LAJA", "LENO", "MLBS", @@ -109,36 +126,19 @@ "NOGP", "OAES", "ONAQ", - "ORNL", - "OSBS", "PUUM", "RMNP", "SCBI", "SERC", - "SJER", "SOAP", - "SRER", "STEI", "STER", - "TALL", "TEAK", "TOOL", "TREE", "UKFS", - "UNDE", "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA" + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/hist30min.json b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/hist30min.json index cff80f2335..04e6bda5a5 100644 --- a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/hist30min.json +++ b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/hist30min.json @@ -18,7 +18,7 @@ "properties": { "title": "hist30min", "description": "All scores for the 30min_latent_heat_flux variable for the hist30min model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BART, KONZ, OSBS, SRER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-01-01T00:00:00Z", "end_datetime": "2022-06-05T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json index 96b5715733..8bdcd2ebb4 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json @@ -11,22 +11,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/USUNEEDAILY.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/bookcast_forest.json" }, { "rel": "item", "type": "application/json", - "href": "./models/USUNEEDAILY.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/bookcast_forest.json" + "href": "./models/climatology.json" }, { "rel": "item", @@ -51,37 +51,37 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "parent", @@ -126,7 +126,7 @@ "interval": [ [ "2023-01-01T00:00:00Z", - "2024-07-14T00:00:00Z" + "2024-07-17T00:00:00Z" ] ] } diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json index 8d868afd4f..566a949ac7 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json @@ -15,7 +15,7 @@ "properties": { "title": "USUNEEDAILY", "description": "All scores for the Daily_Net_ecosystem_exchange variable for the USUNEEDAILY model. Information for the model is provided as follows: \"Home brew ARIMA.\" We didn't use a formal time series framework because of all the missing values in both our response variable and the weather covariates. So we used a GAM to fit a seasonal component based on day of year, and we included NEE the previous day as as an AR 1 term. We did some model selection, using cross validation, to identify temperature and relative humidity as weather covariates..\n The model predicts this variable at the following sites: PUUM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-12-12T00:00:00Z", "end_datetime": "2024-01-16T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json index 7df90a4d7e..ac668b3946 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json @@ -16,7 +16,7 @@ "properties": { "title": "bookcast_forest", "description": "All scores for the Daily_Net_ecosystem_exchange variable for the bookcast_forest model. Information for the model is provided as follows: A simple daily timestep process-based model of a terrestrial carbon cycle. It includes leaves, wood, and soil pools. It uses a light-use efficiency GPP model to convert PAR to carbon. The model is derived from https://github.com/mdietze/FluxCourseForecast..\n The model predicts this variable at the following sites: TALL, OSBS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2024-01-10T00:00:00Z", "end_datetime": "2024-07-12T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json index 6272aebf2d..4556aa70e7 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json @@ -9,10 +9,26 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948], [-106.8425, 32.5907], [-96.6129, 39.1104], [-96.5631, 39.1008], [-67.0769, 18.0213], + [-88.1612, 31.8539], [-80.5248, 37.3783], [-109.3883, 38.2483], [-105.5824, 40.0543], @@ -38,29 +54,13 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-147.5026, 65.154], - [-88.1612, 31.8539] + [-147.5026, 65.154] ] }, "properties": { "title": "cb_prophet", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, ABBY, BARR, BART, BLAN, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, BONA, LENO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, BONA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -91,10 +91,26 @@ "nee", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", "LAJA", + "LENO", "MLBS", "MOAB", "NIWO", @@ -120,23 +136,7 @@ "UNDE", "WOOD", "WREF", - "ABBY", - "BARR", - "BART", - "BLAN", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "BONA", - "LENO" + "BONA" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json index 0a65d2c3a1..64e0e23efc 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json @@ -9,6 +9,28 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-67.0769, 18.0213], + [-88.1612, 31.8539], + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], [-105.546, 40.2759], [-78.1395, 38.8929], [-76.56, 38.8901], @@ -19,6 +41,7 @@ [-103.0293, 40.4619], [-87.3933, 32.9505], [-119.006, 37.0058], + [-149.3705, 68.6611], [-89.5857, 45.4937], [-95.1921, 39.0404], [-89.5373, 46.2339], @@ -32,38 +55,15 @@ [-147.5026, 65.154], [-97.57, 33.4012], [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-149.3705, 68.6611] + [-99.1066, 47.1617] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, TOOL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", - "end_datetime": "2024-07-14T00:00:00Z", + "end_datetime": "2024-07-17T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", @@ -92,6 +92,28 @@ "nee", "Daily", "P1D", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", "RMNP", "SCBI", "SERC", @@ -102,6 +124,7 @@ "STER", "TALL", "TEAK", + "TOOL", "TREE", "UKFS", "UNDE", @@ -115,30 +138,7 @@ "BONA", "CLBJ", "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "TOOL" + "DCFS" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json index f153aade50..8cd37ab84d 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json @@ -9,14 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], [-145.7514, 63.8811], [-87.8039, 32.5417], [-81.4362, 28.1251], @@ -55,15 +47,23 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617] ] }, "properties": { "title": "persistenceRW", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", - "end_datetime": "2024-07-14T00:00:00Z", + "end_datetime": "2024-07-17T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", @@ -92,14 +92,6 @@ "nee", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", "DEJU", "DELA", "DSNY", @@ -138,7 +130,15 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json index f3e326a6e9..41df30a002 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json @@ -9,9 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], [-149.2133, 63.8758], [-84.4686, 31.1948], [-106.8425, 32.5907], @@ -55,15 +52,18 @@ [-99.1066, 47.1617], [-145.7514, 63.8811], [-87.8039, 32.5417], - [-81.4362, 28.1251] + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", - "end_datetime": "2024-07-14T00:00:00Z", + "end_datetime": "2024-07-17T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,9 +92,6 @@ "nee", "Daily", "P1D", - "GRSM", - "GUAN", - "HARV", "HEAL", "JERC", "JORN", @@ -138,7 +135,10 @@ "DCFS", "DEJU", "DELA", - "DSNY" + "DSNY", + "GRSM", + "GUAN", + "HARV" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json index e28805a398..d367add158 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json @@ -9,16 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], [-83.5019, 35.689], [-66.8687, 17.9696], [-72.1727, 42.5369], @@ -55,15 +45,25 @@ [-99.2413, 47.1282], [-121.9519, 45.8205], [-110.5391, 44.9535], - [-122.3303, 45.7624] + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", - "end_datetime": "2024-07-14T00:00:00Z", + "end_datetime": "2024-07-17T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,16 +92,6 @@ "nee", "Daily", "P1D", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", "GRSM", "GUAN", "HARV", @@ -138,7 +128,17 @@ "WOOD", "WREF", "YELL", - "ABBY" + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json index 6ad710ef27..a6ab6fd28b 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json @@ -9,6 +9,23 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -38,30 +55,13 @@ [-112.4524, 40.1776], [-84.2826, 35.9641], [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-155.3173, 19.5531] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,6 +92,23 @@ "nee", "Daily", "P1D", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -121,24 +138,7 @@ "ONAQ", "ORNL", "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "PUUM" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json index b16e3b7961..8aaf3dba82 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json index 7c750ee704..bbdcb6ae89 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json @@ -9,7 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-121.9519, 45.8205], [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], @@ -55,13 +54,14 @@ [-89.5857, 45.4937], [-95.1921, 39.0404], [-89.5373, 46.2339], - [-99.2413, 47.1282] + [-99.2413, 47.1282], + [-121.9519, 45.8205] ] }, "properties": { "title": "tg_precip_lm", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,7 +92,6 @@ "nee", "Daily", "P1D", - "WREF", "YELL", "ABBY", "BARR", @@ -138,7 +137,8 @@ "TREE", "UKFS", "UNDE", - "WOOD" + "WOOD", + "WREF" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json index 5e8cfaaf65..8ca9797d17 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json @@ -9,6 +9,9 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -52,16 +55,13 @@ [-149.3705, 68.6611], [-89.5857, 45.4937], [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-89.5373, 46.2339] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,6 +92,9 @@ "nee", "Daily", "P1D", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -135,10 +138,7 @@ "TOOL", "TREE", "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "UNDE" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json index 48e259f2b8..de00e5dc1a 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json @@ -9,7 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-76.56, 38.8901], [-119.7323, 37.1088], [-119.2622, 37.0334], [-110.8355, 31.9107], @@ -55,13 +54,14 @@ [-81.9934, 29.6893], [-155.3173, 19.5531], [-105.546, 40.2759], - [-78.1395, 38.8929] + [-78.1395, 38.8929], + [-76.56, 38.8901] ] }, "properties": { "title": "tg_randfor", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,7 +92,6 @@ "nee", "Daily", "P1D", - "SERC", "SJER", "SOAP", "SRER", @@ -138,7 +137,8 @@ "OSBS", "PUUM", "RMNP", - "SCBI" + "SCBI", + "SERC" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json index 6b49668d02..668fe8ce3a 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json @@ -9,6 +9,25 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], [-78.0418, 39.0337], @@ -36,34 +55,15 @@ [-99.0588, 35.4106], [-112.4524, 40.1776], [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624] + [-81.9934, 29.6893] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", - "end_datetime": "2024-07-14T00:00:00Z", + "end_datetime": "2024-07-17T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,6 +92,25 @@ "nee", "Daily", "P1D", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", "BARR", "BART", "BLAN", @@ -119,26 +138,7 @@ "OAES", "ONAQ", "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY" + "OSBS" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json index 457f04dab2..dadebddb1a 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json @@ -9,6 +9,14 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-67.0769, 18.0213], + [-88.1612, 31.8539], + [-80.5248, 37.3783], [-109.3883, 38.2483], [-105.5824, 40.0543], [-100.9154, 46.7697], @@ -47,21 +55,13 @@ [-81.4362, 28.1251], [-83.5019, 35.689], [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783] + [-72.1727, 42.5369] ] }, "properties": { "title": "tg_temp_lm", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,6 +92,14 @@ "nee", "Daily", "P1D", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", "MOAB", "NIWO", "NOGP", @@ -130,15 +138,7 @@ "DSNY", "GRSM", "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS" + "HARV" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json index 3aa474da05..f97f707384 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json @@ -9,6 +9,8 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-122.3303, 45.7624], + [-156.6194, 71.2824], [-71.2874, 44.0639], [-78.0418, 39.0337], [-147.5026, 65.154], @@ -53,15 +55,13 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824] + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_temp_lm_all_sites", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,6 +92,8 @@ "nee", "Daily", "P1D", + "ABBY", + "BARR", "BART", "BLAN", "BONA", @@ -136,9 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR" + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/collection.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/collection.json index 708d9309e4..51b446bc4d 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/collection.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/collection.json @@ -11,12 +11,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", @@ -36,27 +36,27 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", @@ -111,7 +111,7 @@ "interval": [ [ "2023-01-01T00:00:00Z", - "2024-07-14T00:00:00Z" + "2024-07-17T00:00:00Z" ] ] } diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json index 36271f80c7..7e1a69ee4e 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json @@ -60,7 +60,7 @@ "properties": { "title": "cb_prophet", "description": "All scores for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, BONA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json index 0435ea2cbb..75a3f1790b 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json @@ -9,6 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -40,30 +55,15 @@ [-81.9934, 29.6893], [-155.3173, 19.5531], [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-103.0293, 40.4619] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, STER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", - "end_datetime": "2024-07-14T00:00:00Z", + "end_datetime": "2024-07-17T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", @@ -92,6 +92,21 @@ "le", "Daily", "P1D", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -123,22 +138,7 @@ "OSBS", "PUUM", "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "STER" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json index cce4577d83..90f1da8bfc 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json @@ -9,6 +9,17 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-67.0769, 18.0213], + [-88.1612, 31.8539], + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], [-105.546, 40.2759], [-78.1395, 38.8929], [-76.56, 38.8901], @@ -44,26 +55,15 @@ [-84.4686, 31.1948], [-106.8425, 32.5907], [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531] + [-96.5631, 39.1008] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", - "end_datetime": "2024-07-14T00:00:00Z", + "end_datetime": "2024-07-17T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,6 +92,17 @@ "le", "Daily", "P1D", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", "RMNP", "SCBI", "SERC", @@ -127,18 +138,7 @@ "JERC", "JORN", "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM" + "KONZ" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json index 45599ba02a..65f21be989 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json @@ -9,8 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-121.9519, 45.8205], - [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -55,15 +53,17 @@ [-89.5857, 45.4937], [-95.1921, 39.0404], [-89.5373, 46.2339], - [-99.2413, 47.1282] + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", - "end_datetime": "2024-07-14T00:00:00Z", + "end_datetime": "2024-07-17T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", @@ -92,8 +92,6 @@ "le", "Daily", "P1D", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +136,9 @@ "TREE", "UKFS", "UNDE", - "WOOD" + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json index 8b6bd53bfb..1900c996cd 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json @@ -9,22 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], [-96.6129, 39.1104], [-96.5631, 39.1008], [-67.0769, 18.0213], @@ -55,13 +39,29 @@ [-99.2413, 47.1282], [-121.9519, 45.8205], [-110.5391, 44.9535], - [-122.3303, 45.7624] + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All scores for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,22 +92,6 @@ "le", "Daily", "P1D", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", "KONA", "KONZ", "LAJA", @@ -138,7 +122,23 @@ "WOOD", "WREF", "YELL", - "ABBY" + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json index 1c01742316..f9e9e991be 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json @@ -9,6 +9,28 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], @@ -33,35 +55,13 @@ [-88.1612, 31.8539], [-80.5248, 37.3783], [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205] + [-105.5824, 40.0543] ] }, "properties": { "title": "tg_humidity_lm_all_sites", - "description": "All scores for the Daily_latent_heat_flux variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_latent_heat_flux variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,6 +92,28 @@ "le", "Daily", "P1D", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", "YELL", "ABBY", "BARR", @@ -116,29 +138,7 @@ "LENO", "MLBS", "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF" + "NIWO" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json index 04fb1e1204..9bba5872b4 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json @@ -9,13 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], [-147.5026, 65.154], [-97.57, 33.4012], [-104.7456, 40.8155], @@ -55,13 +48,20 @@ [-149.3705, 68.6611], [-89.5857, 45.4937], [-95.1921, 39.0404], - [-89.5373, 46.2339] + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337] ] }, "properties": { "title": "tg_precip_lm", - "description": "All scores for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,13 +92,6 @@ "le", "Daily", "P1D", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", "BONA", "CLBJ", "CPER", @@ -138,7 +131,14 @@ "TOOL", "TREE", "UKFS", - "UNDE" + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json index c0d6b84f51..d019d16274 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json @@ -9,13 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], [-71.2874, 44.0639], [-78.0418, 39.0337], [-147.5026, 65.154], @@ -55,13 +48,20 @@ [-87.3933, 32.9505], [-119.006, 37.0058], [-149.3705, 68.6611], - [-89.5857, 45.4937] + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All scores for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,13 +92,6 @@ "le", "Daily", "P1D", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", "BART", "BLAN", "BONA", @@ -138,7 +131,14 @@ "TALL", "TEAK", "TOOL", - "TREE" + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json index 71456f4d0f..48d0c7d6ba 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json @@ -9,22 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -55,13 +39,29 @@ [-84.2826, 35.9641], [-81.9934, 29.6893], [-155.3173, 19.5531], - [-105.546, 40.2759] + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_randfor", - "description": "All scores for the Daily_latent_heat_flux variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_latent_heat_flux variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,22 +92,6 @@ "le", "Daily", "P1D", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +122,23 @@ "ORNL", "OSBS", "PUUM", - "RMNP" + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json index a39e1243ad..3722fbafc4 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json @@ -61,9 +61,9 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Daily_latent_heat_flux variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", - "end_datetime": "2024-07-14T00:00:00Z", + "end_datetime": "2024-07-17T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json index 5e8544b7ce..c32b696044 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json @@ -9,6 +9,7 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -54,14 +55,13 @@ [-95.1921, 39.0404], [-89.5373, 46.2339], [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-121.9519, 45.8205] ] }, "properties": { "title": "tg_temp_lm", - "description": "All scores for the Daily_latent_heat_flux variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Daily_latent_heat_flux variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,6 +92,7 @@ "le", "Daily", "P1D", + "YELL", "ABBY", "BARR", "BART", @@ -137,8 +138,7 @@ "UKFS", "UNDE", "WOOD", - "WREF", - "YELL" + "WREF" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json index 33b1a72eac..1bdef88190 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All scores for the Daily_latent_heat_flux variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/collection.json b/catalog/scores/Terrestrial/collection.json index 3d079af7a6..0d2bb881ca 100644 --- a/catalog/scores/Terrestrial/collection.json +++ b/catalog/scores/Terrestrial/collection.json @@ -74,7 +74,7 @@ "interval": [ [ "2017-02-01T00:00:00Z", - "2024-07-21T00:00:00Z" + "2024-07-24T00:00:00Z" ] ] } diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/collection.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/collection.json index 46ed63765c..d17521025f 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/collection.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/collection.json @@ -18,6 +18,11 @@ "type": "application/json", "href": "./models/tg_temp_lm.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_temp_lm_all_sites.json" + }, { "rel": "item", "type": "application/json", @@ -93,11 +98,6 @@ "type": "application/json", "href": "./models/tg_randfor.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" - }, { "rel": "parent", "type": "application/json", diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/BU_Dem.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/BU_Dem.json index 36f7dda506..0cc6027a35 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/BU_Dem.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/BU_Dem.json @@ -21,7 +21,7 @@ "properties": { "title": "BU_Dem", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the BU_Dem model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: KONZ, ORNL, SCBI, SERC, TALL, UKFS, BLAN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2019-03-04T00:00:00Z", "end_datetime": "2019-10-28T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/NJC_ETS_PF.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/NJC_ETS_PF.json index cba412f124..06e15f6d13 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/NJC_ETS_PF.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/NJC_ETS_PF.json @@ -20,7 +20,7 @@ "properties": { "title": "NJC_ETS_PF", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the NJC_ETS_PF model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: KONZ, ORNL, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2019-05-27T00:00:00Z", "end_datetime": "2019-10-21T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/NJC_Ticks.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/NJC_Ticks.json index 089798646f..314307db4e 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/NJC_Ticks.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/NJC_Ticks.json @@ -20,7 +20,7 @@ "properties": { "title": "NJC_Ticks", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the NJC_Ticks model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: KONZ, ORNL, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2019-02-25T00:00:00Z", "end_datetime": "2019-10-28T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/TickBench.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/TickBench.json index bfef92464a..7ab81b7794 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/TickBench.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/TickBench.json @@ -23,7 +23,7 @@ "properties": { "title": "TickBench", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the TickBench model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-03-01T00:00:00Z", "end_datetime": "2021-03-22T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/Ticks_288.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/Ticks_288.json index 01f0b450ca..a48ab53ee5 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/Ticks_288.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/Ticks_288.json @@ -20,7 +20,7 @@ "properties": { "title": "Ticks_288", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the Ticks_288 model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: SERC, KONZ, UKFS, ORNL, SCBI, TALL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2018-09-03T00:00:00Z", "end_datetime": "2020-02-03T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/UCLA_2022.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/UCLA_2022.json index 9f54b33d2d..7055fd9228 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/UCLA_2022.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/UCLA_2022.json @@ -23,7 +23,7 @@ "properties": { "title": "UCLA_2022", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the UCLA_2022 model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS, NA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-03-01T00:00:00Z", "end_datetime": "2021-05-24T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/VTicks.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/VTicks.json index d48d6a160d..101d8ea262 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/VTicks.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/VTicks.json @@ -20,7 +20,7 @@ "properties": { "title": "VTicks", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the VTicks model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: KONZ, ORNL, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2019-03-04T00:00:00Z", "end_datetime": "2019-10-28T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json index f2a01c8c35..21c218d2ba 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json @@ -9,21 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-88.1612, 31.8539], - [-78.0418, 39.0337], - [-96.5631, 39.1008], + [-84.2826, 35.9641], [-81.9934, 29.6893], [-78.1395, 38.8929], - [-84.2826, 35.9641], [-76.56, 38.8901], [-87.3933, 32.9505], - [-95.1921, 39.0404] + [-95.1921, 39.0404], + [-78.0418, 39.0337], + [-96.5631, 39.1008], + [-88.1612, 31.8539] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: LENO, BLAN, KONZ, OSBS, SCBI, ORNL, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ORNL, OSBS, SCBI, SERC, TALL, UKFS, BLAN, KONZ, LENO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-06-07T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ @@ -54,15 +54,15 @@ "amblyomma_americanum", "Weekly", "P1W", - "LENO", - "BLAN", - "KONZ", + "ORNL", "OSBS", "SCBI", - "ORNL", "SERC", "TALL", - "UKFS" + "UKFS", + "BLAN", + "KONZ", + "LENO" ], "table:columns": [ { diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json index ae5c9e8041..053d12d356 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json @@ -9,21 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-87.3933, 32.9505], - [-95.1921, 39.0404], + [-88.1612, 31.8539], [-78.0418, 39.0337], [-96.5631, 39.1008], - [-88.1612, 31.8539], + [-81.9934, 29.6893], + [-78.1395, 38.8929], [-84.2826, 35.9641], - [-81.9934, 29.6893] + [-76.56, 38.8901], + [-87.3933, 32.9505], + [-95.1921, 39.0404] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: SCBI, SERC, TALL, UKFS, BLAN, KONZ, LENO, ORNL, OSBS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: LENO, BLAN, KONZ, OSBS, SCBI, ORNL, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-06-07T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ @@ -54,15 +54,15 @@ "amblyomma_americanum", "Weekly", "P1W", - "SCBI", - "SERC", - "TALL", - "UKFS", + "LENO", "BLAN", "KONZ", - "LENO", + "OSBS", + "SCBI", "ORNL", - "OSBS" + "SERC", + "TALL", + "UKFS" ], "table:columns": [ { diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json index df079fea77..8927ce0fc5 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json index cc9fde0baf..cd382c8954 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json index 7558e07aa4..680b3e14ff 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json @@ -22,7 +22,7 @@ "properties": { "title": "tg_lasso", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-12-26T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json index 39ba7bfd14..a82a8a701c 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_precip_lm", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json index 2fe42223d5..2496729902 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json index 9fe17c0690..87ffde0e8f 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json @@ -22,7 +22,7 @@ "properties": { "title": "tg_randfor", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2022-12-26T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json index 8e774c2e2a..ae07c9f667 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json @@ -9,21 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-96.5631, 39.1008], [-88.1612, 31.8539], + [-78.0418, 39.0337], + [-96.5631, 39.1008], [-81.9934, 29.6893], [-78.1395, 38.8929], - [-76.56, 38.8901], - [-95.1921, 39.0404], - [-78.0418, 39.0337], [-84.2826, 35.9641], - [-87.3933, 32.9505] + [-76.56, 38.8901], + [-87.3933, 32.9505], + [-95.1921, 39.0404] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: KONZ, LENO, OSBS, SCBI, SERC, UKFS, BLAN, ORNL, TALL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: LENO, BLAN, KONZ, OSBS, SCBI, ORNL, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2021-06-07T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ @@ -54,15 +54,15 @@ "amblyomma_americanum", "Weekly", "P1W", - "KONZ", "LENO", + "BLAN", + "KONZ", "OSBS", "SCBI", - "SERC", - "UKFS", - "BLAN", "ORNL", - "TALL" + "SERC", + "TALL", + "UKFS" ], "table:columns": [ { diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json index bbce080b74..43702b0e63 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_temp_lm", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json index f5e651c715..52ad957a55 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All scores for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-07-27T00:00:00Z", + "datetime": "2024-07-28T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ diff --git a/catalog/scores/Ticks/collection.json b/catalog/scores/Ticks/collection.json index 6da5d6a538..a54d2abdf9 100644 --- a/catalog/scores/Ticks/collection.json +++ b/catalog/scores/Ticks/collection.json @@ -59,7 +59,7 @@ "interval": [ [ "2017-02-01T00:00:00Z", - "2024-07-21T00:00:00Z" + "2024-07-24T00:00:00Z" ] ] } diff --git a/catalog/scores/collection.json b/catalog/scores/collection.json index c9167656fa..4d7a2ee5c1 100644 --- a/catalog/scores/collection.json +++ b/catalog/scores/collection.json @@ -81,7 +81,7 @@ "interval": [ [ "2017-02-01T00:00:00Z", - "2024-07-21T00:00:00Z" + "2024-07-24T00:00:00Z" ] ] }