From 18c7b936939359f0fa00e4b849d625415962f4cd Mon Sep 17 00:00:00 2001 From: github-actions Date: Sun, 4 Aug 2024 01:09:02 +0000 Subject: [PATCH] update catalog --- .../Daily_Chlorophyll_a/collection.json | 36 ++--- .../Daily_Chlorophyll_a/models/USGSHABs1.json | 12 +- .../models/cb_prophet.json | 40 ++--- .../models/climatology.json | 24 +-- .../Daily_Chlorophyll_a/models/fTSLM_lag.json | 2 +- .../models/persistenceRW.json | 2 +- .../models/procBlanchardMonod.json | 12 +- .../models/procCTMIMonod.json | 12 +- .../models/procEppleyNorbergMonod.json | 20 +-- .../models/procEppleyNorbergSteele.json | 2 +- .../models/procHinshelwoodMonod.json | 12 +- .../models/procHinshelwoodSteele.json | 12 +- .../Daily_Chlorophyll_a/models/tg_arima.json | 28 ++-- .../Daily_Chlorophyll_a/models/tg_ets.json | 16 +- .../models/tg_humidity_lm.json | 20 +-- .../models/tg_humidity_lm_all_sites.json | 16 +- .../Daily_Chlorophyll_a/models/tg_lasso.json | 24 +-- .../models/tg_precip_lm.json | 16 +- .../models/tg_precip_lm_all_sites.json | 2 +- .../models/tg_randfor.json | 2 +- .../Daily_Chlorophyll_a/models/tg_tbats.json | 28 ++-- .../models/tg_temp_lm.json | 24 +-- .../models/tg_temp_lm_all_sites.json | 12 +- .../models/xgboost_parallel.json | 20 +-- .../Daily_Dissolved_oxygen/collection.json | 22 +-- .../models/AquaticEcosystemsOxygen.json | 2 +- .../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 | 36 ++--- .../models/hotdeck.json | 2 +- .../models/persistenceRW.json | 72 ++++----- .../models/tg_arima.json | 16 +- .../Daily_Dissolved_oxygen/models/tg_ets.json | 60 +++---- .../models/tg_humidity_lm.json | 40 ++--- .../models/tg_humidity_lm_all_sites.json | 32 ++-- .../models/tg_lasso.json | 32 ++-- .../models/tg_precip_lm.json | 32 ++-- .../models/tg_precip_lm_all_sites.json | 72 ++++----- .../models/tg_randfor.json | 48 +++--- .../models/tg_tbats.json | 2 +- .../models/tg_temp_lm.json | 60 +++---- .../models/tg_temp_lm_all_sites.json | 16 +- .../models/wbears_gp.json | 2 +- .../models/wbears_rnn.json | 2 +- .../models/xgboost_parallel.json | 56 +++---- .../Daily_Water_temperature/collection.json | 42 ++--- .../Daily_Water_temperature/models/BBTW.json | 2 +- .../Daily_Water_temperature/models/BTW.json | 2 +- .../models/GAM_air_wind.json | 2 +- .../models/GLEON_JRabaey_temp_physics.json | 64 ++++---- .../models/GLEON_lm_lag_1day.json | 2 +- .../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 | 56 +++---- .../models/baseline_ensemble.json | 68 ++++---- .../models/bee_bake_RFModel_2024.json | 2 +- .../models/cb_prophet.json | 92 +++++------ .../models/climatology.json | 20 +-- .../models/fARIMA_clim_ensemble.json | 60 +++---- .../models/fTSLM_lag.json | 2 +- .../models/flareGLM.json | 12 +- .../models/flareGLM_noDA.json | 2 +- .../models/flareGOTM_noDA.json | 12 +- .../models/flareSimstrat_noDA.json | 14 +- .../models/flare_ler.json | 12 +- .../models/flare_ler_baselines.json | 8 +- .../models/hotdeck.json | 2 +- .../models/lm_AT_WTL_WS.json | 2 +- .../models/mkricheldorf_w_lag.json | 20 +-- .../models/mlp1_wtempforecast_LF.json | 20 +-- .../models/persistenceRW.json | 40 ++--- .../models/precip_mod.json | 2 +- .../models/tg_arima.json | 52 +++--- .../models/tg_ets.json | 60 +++---- .../models/tg_humidity_lm.json | 2 +- .../models/tg_humidity_lm_all_sites.json | 2 +- .../models/tg_lasso.json | 32 ++-- .../models/tg_precip_lm.json | 16 +- .../models/tg_precip_lm_all_sites.json | 52 +++--- .../models/tg_randfor.json | 16 +- .../models/tg_tbats.json | 36 ++--- .../models/tg_temp_lm.json | 44 +++--- .../models/tg_temp_lm_all_sites.json | 76 ++++----- .../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 | 2 +- .../collection.json | 18 +-- .../models/tg_arima.json | 44 +++--- .../models/tg_ets.json | 2 +- .../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 | 100 ++++++------ .../models/tg_temp_lm.json | 2 +- .../models/tg_temp_lm_all_sites.json | 2 +- .../collection.json | 22 +-- .../models/tg_arima.json | 80 +++++----- .../models/tg_ets.json | 72 ++++----- .../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 | 44 +++--- .../models/tg_tbats.json | 148 +++++++++--------- .../models/tg_temp_lm.json | 2 +- .../models/tg_temp_lm_all_sites.json | 88 +++++------ .../collection.json | 26 +-- .../models/CSP_Gwave.json | 2 +- .../models/CU_Pheno.json | 2 +- .../models/ChlorophyllCrusaders.json | 2 +- .../models/DALEC_SIP.json | 2 +- .../models/EFI_U_P.json | 2 +- .../models/Fourier.json | 2 +- .../models/NEFIpheno.json | 2 +- .../models/PEG.json | 72 ++++----- .../models/PEG_RFR.json | 2 +- .../models/PEG_RFR0.json | 2 +- .../models/UCSC_P_EDM.json | 16 +- .../models/VT_Ph_GDD.json | 2 +- .../models/cb_prophet.json | 60 +++---- .../models/climatology.json | 72 ++++----- .../models/greenbears.json | 2 +- .../models/persistenceRW.json | 72 ++++----- .../models/tg_arima.json | 44 +++--- .../models/tg_ets.json | 16 +- .../models/tg_humidity_lm.json | 44 +++--- .../models/tg_humidity_lm_all_sites.json | 32 ++-- .../models/tg_lasso.json | 40 ++--- .../models/tg_precip_lm.json | 60 +++---- .../models/tg_precip_lm_all_sites.json | 44 +++--- .../models/tg_randfor.json | 80 +++++----- .../models/tg_tbats.json | 92 +++++------ .../models/tg_temp_lm.json | 16 +- .../models/tg_temp_lm_all_sites.json | 40 ++--- .../models/xgboost_parallel.json | 80 +++++----- .../models/PEG.json | 36 ++--- .../models/baseline_ensemble.json | 72 ++++----- .../models/cb_prophet.json | 116 +++++++------- .../models/climatology.json | 100 ++++++------ .../models/persistenceRW.json | 36 ++--- .../models/tg_arima.json | 44 +++--- .../models/tg_ets.json | 76 ++++----- .../models/tg_humidity_lm.json | 2 +- .../models/tg_humidity_lm_all_sites.json | 16 +- .../models/tg_lasso.json | 20 +-- .../models/tg_precip_lm.json | 2 +- .../models/tg_precip_lm_all_sites.json | 44 +++--- .../models/tg_randfor.json | 60 +++---- .../models/tg_tbats.json | 52 +++--- .../models/tg_temp_lm.json | 12 +- .../models/tg_temp_lm_all_sites.json | 44 +++--- .../models/xgboost_parallel.json | 88 +++++------ .../collection.json | 14 +- .../models/BU_SIPNET.json | 2 +- .../models/IU_Eco2021.json | 2 +- .../models/UCB_XT.json | 2 +- .../models/VT_NEET.json | 2 +- .../models/cb_prophet.json | 72 ++++----- .../models/climatology.json | 52 +++--- .../models/hist30min.json | 2 +- .../30min_latent_heat_flux/collection.json | 10 +- .../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 | 44 +++--- .../models/climatology.json | 96 ++++++------ .../models/hist30min.json | 2 +- .../collection.json | 12 +- .../models/USUNEEDAILY.json | 2 +- .../models/bookcast_forest.json | 2 +- .../models/cb_prophet.json | 2 +- .../models/climatology.json | 44 +++--- .../models/persistenceRW.json | 60 +++---- .../models/tg_arima.json | 88 +++++------ .../models/tg_ets.json | 44 +++--- .../models/tg_humidity_lm.json | 68 ++++---- .../models/tg_humidity_lm_all_sites.json | 28 ++-- .../models/tg_precip_lm.json | 80 +++++----- .../models/tg_precip_lm_all_sites.json | 2 +- .../models/tg_randfor.json | 80 +++++----- .../models/tg_tbats.json | 88 +++++------ .../models/tg_temp_lm.json | 2 +- .../models/tg_temp_lm_all_sites.json | 2 +- .../Daily_latent_heat_flux/collection.json | 8 +- .../models/cb_prophet.json | 2 +- .../models/climatology.json | 2 +- .../models/tg_arima.json | 52 +++--- .../Daily_latent_heat_flux/models/tg_ets.json | 2 +- .../models/tg_humidity_lm.json | 2 +- .../models/tg_humidity_lm_all_sites.json | 44 +++--- .../models/tg_precip_lm.json | 44 +++--- .../models/tg_precip_lm_all_sites.json | 88 +++++------ .../models/tg_randfor.json | 60 +++---- .../models/tg_tbats.json | 28 ++-- .../models/tg_temp_lm.json | 100 ++++++------ .../models/tg_temp_lm_all_sites.json | 44 +++--- .../collection.json | 24 +-- .../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 | 16 +- .../models/tg_ets.json | 24 +-- .../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 | 2 +- .../models/tg_temp_lm.json | 2 +- .../models/tg_temp_lm_all_sites.json | 2 +- 231 files changed, 3034 insertions(+), 3034 deletions(-) diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/collection.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/collection.json index 2ddaa953fe..3b09e20c59 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/collection.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/collection.json @@ -11,12 +11,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/USGSHABs1.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/climatology.json" }, { "rel": "item", @@ -26,7 +26,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/USGSHABs1.json" }, { "rel": "item", @@ -36,32 +36,32 @@ { "rel": "item", "type": "application/json", - "href": "./models/procEppleyNorbergSteele.json" + "href": "./models/procBlanchardMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/procCTMIMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procCTMIMonod.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procHinshelwoodMonod.json" + "href": "./models/procEppleyNorbergMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procBlanchardMonod.json" + "href": "./models/procEppleyNorbergSteele.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procEppleyNorbergMonod.json" + "href": "./models/procHinshelwoodMonod.json" }, { "rel": "item", @@ -76,47 +76,47 @@ { "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_lasso.json" + "href": "./models/tg_precip_lm_all_sites.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_tbats.json" + "href": "./models/tg_temp_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_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json index e963d036cf..2a17ba55bb 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json @@ -9,15 +9,15 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-88.1589, 31.8534], [-87.7982, 32.5415], - [-84.4374, 31.1854], - [-88.1589, 31.8534] + [-84.4374, 31.1854] ] }, "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": "2024-08-03T00:00:00Z", + "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: TOMB, BLWA, 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-09-01T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ @@ -48,9 +48,9 @@ "chla", "Daily", "P1D", + "TOMB", "BLWA", - "FLNT", - "TOMB" + "FLNT" ], "table:columns": [ { 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 a891dbb592..8cb632c27d 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "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 5cb6881ede..743055ae4b 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json @@ -10,15 +10,15 @@ "type": "MultiPoint", "coordinates": [ [-82.0084, 29.676], - [-89.4737, 46.2097], [-84.4374, 31.1854], + [-82.0177, 29.6878], + [-88.1589, 31.8534], + [-87.7982, 32.5415], [-89.7048, 45.9983], - [-99.1139, 47.1591], + [-89.4737, 46.2097], [-99.2531, 47.1298], - [-82.0177, 29.6878], + [-99.1139, 47.1591], [-149.6106, 68.6307], - [-87.7982, 32.5415], - [-88.1589, 31.8534], [-105.5442, 40.035], [-96.443, 38.9459], [-78.1473, 38.8943] @@ -26,8 +26,8 @@ }, "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: BARC, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOOK, BLWA, TOMB, 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": "2024-08-03T00:00:00Z", + "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, FLNT, SUGG, TOMB, BLWA, LIRO, CRAM, PRPO, PRLA, 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-01-01T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -59,15 +59,15 @@ "Daily", "P1D", "BARC", - "CRAM", "FLNT", + "SUGG", + "TOMB", + "BLWA", "LIRO", - "PRLA", + "CRAM", "PRPO", - "SUGG", + "PRLA", "TOOK", - "BLWA", - "TOMB", "COMO", "MCDI", "POSE", 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 8740dae65d..34d6575c49 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 3c04f9c3dc..d5c38cec23 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json @@ -24,7 +24,7 @@ "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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2022-08-25T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json index 72967cc11e..972c6e6005 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": [ - [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "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" ], "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 0d92d8207e..c90c9a5f8e 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], - [-99.1139, 47.1591] + [-89.7048, 45.9983] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "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", - "PRLA" + "LIRO" ], "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 12b96fb6f7..4b386b33cc 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": [ + [-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": "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "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/procEppleyNorbergSteele.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json index 0312b7f128..1c0704d614 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json @@ -21,7 +21,7 @@ "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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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/procHinshelwoodMonod.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json index 32348da5c3..0bdfb03eff 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": [ - [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "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" ], "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 e682be76e3..369bb70eb3 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "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" ], "table:columns": [ { 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 d0c2a3edea..ad232851c7 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json @@ -9,22 +9,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-87.7982, 32.5415], + [-89.4737, 46.2097], + [-84.4374, 31.1854], [-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], - [-84.4374, 31.1854], [-82.0177, 29.6878], - [-88.1589, 31.8534] + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-82.0084, 29.676] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -55,16 +55,16 @@ "chla", "Daily", "P1D", + "BLWA", + "CRAM", + "FLNT", "LIRO", "PRLA", "PRPO", - "TOOK", - "CRAM", - "BARC", - "BLWA", - "FLNT", "SUGG", - "TOMB" + "TOMB", + "TOOK", + "BARC" ], "table:columns": [ { 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 c64be145f4..a0a68da9cc 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json @@ -9,8 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-149.6106, 68.6307], - [-82.0084, 29.676], [-87.7982, 32.5415], [-89.4737, 46.2097], [-84.4374, 31.1854], @@ -18,13 +16,15 @@ [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0177, 29.6878], - [-88.1589, 31.8534] + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-82.0084, 29.676] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -55,8 +55,6 @@ "chla", "Daily", "P1D", - "TOOK", - "BARC", "BLWA", "CRAM", "FLNT", @@ -64,7 +62,9 @@ "PRLA", "PRPO", "SUGG", - "TOMB" + "TOMB", + "TOOK", + "BARC" ], "table:columns": [ { 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 5d5fd48d16..b88e1beda8 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 @@ -9,22 +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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "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" + "TOOK", + "BARC", + "BLWA", + "CRAM" ], "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 0ae1578823..26ad656fd5 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 @@ -9,8 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-99.1139, 47.1591], - [-99.2531, 47.1298], [-82.0177, 29.6878], [-88.1589, 31.8534], [-149.6106, 68.6307], @@ -18,13 +16,15 @@ [-87.7982, 32.5415], [-89.4737, 46.2097], [-84.4374, 31.1854], - [-89.7048, 45.9983] + [-89.7048, 45.9983], + [-99.1139, 47.1591], + [-99.2531, 47.1298] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -55,8 +55,6 @@ "chla", "Daily", "P1D", - "PRLA", - "PRPO", "SUGG", "TOMB", "TOOK", @@ -64,7 +62,9 @@ "BLWA", "CRAM", "FLNT", - "LIRO" + "LIRO", + "PRLA", + "PRPO" ], "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 e2cc442de3..715d83ee75 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] ] }, "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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "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" ], "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 3505097621..bd3f0d96a9 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 @@ -9,6 +9,8 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-99.2531, 47.1298], + [-82.0177, 29.6878], [-88.1589, 31.8534], [-149.6106, 68.6307], [-82.0084, 29.676], @@ -16,15 +18,13 @@ [-89.4737, 46.2097], [-84.4374, 31.1854], [-89.7048, 45.9983], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-82.0177, 29.6878] + [-99.1139, 47.1591] ] }, "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: TOMB, TOOK, BARC, BLWA, CRAM, FLNT, 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": "2024-08-03T00:00:00Z", + "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: PRPO, SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT, 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -55,6 +55,8 @@ "chla", "Daily", "P1D", + "PRPO", + "SUGG", "TOMB", "TOOK", "BARC", @@ -62,9 +64,7 @@ "CRAM", "FLNT", "LIRO", - "PRLA", - "PRPO", - "SUGG" + "PRLA" ], "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 253fb96d2d..365e3d40d4 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 @@ -24,7 +24,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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/tg_randfor.json b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json index 528062bc1b..97c6a69c9c 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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 e9f9d96b95..b1a12df6ce 100644 --- a/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json +++ b/catalog/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json @@ -9,22 +9,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-82.0177, 29.6878], - [-88.1589, 31.8534], + [-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_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: 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -55,16 +55,16 @@ "chla", "Daily", "P1D", - "SUGG", - "TOMB", + "LIRO", + "PRLA", + "PRPO", "TOOK", + "CRAM", "BARC", "BLWA", - "CRAM", "FLNT", - "LIRO", - "PRLA", - "PRPO" + "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 6801656aa5..6a4d807f42 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 @@ -9,22 +9,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], - [-89.7048, 45.9983] + [-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: 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": "2024-08-03T00:00:00Z", + "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, 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -55,16 +55,16 @@ "chla", "Daily", "P1D", - "PRLA", - "PRPO", - "SUGG", - "TOMB", "TOOK", "BARC", "BLWA", "CRAM", "FLNT", - "LIRO" + "LIRO", + "PRLA", + "PRPO", + "SUGG", + "TOMB" ], "table:columns": [ { 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 206afa1d1c..5a4483b6a0 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 @@ -9,7 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-149.6106, 68.6307], [-82.0084, 29.676], [-87.7982, 32.5415], [-89.4737, 46.2097], @@ -18,13 +17,14 @@ [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0177, 29.6878], - [-88.1589, 31.8534] + [-88.1589, 31.8534], + [-149.6106, 68.6307] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -55,7 +55,6 @@ "chla", "Daily", "P1D", - "TOOK", "BARC", "BLWA", "CRAM", @@ -64,7 +63,8 @@ "PRLA", "PRPO", "SUGG", - "TOMB" + "TOMB", + "TOOK" ], "table:columns": [ { 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 387c37d546..280d2c5252 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": [ - [-99.2531, 47.1298], - [-82.0177, 29.6878], - [-88.1589, 31.8534], [-82.0084, 29.676], [-87.7982, 32.5415], - [-89.4737, 46.2097], [-84.4374, 31.1854], + [-82.0177, 29.6878], + [-88.1589, 31.8534], + [-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: PRPO, SUGG, TOMB, BARC, BLWA, CRAM, FLNT, LIRO, 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2023-12-08T00:00:00Z", "providers": [ @@ -55,14 +55,14 @@ "chla", "Daily", "P1D", - "PRPO", - "SUGG", - "TOMB", "BARC", "BLWA", - "CRAM", "FLNT", + "SUGG", + "TOMB", + "CRAM", "LIRO", + "PRPO", "PRLA", "TOOK" ], diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json index 0b328e51a3..014dbe08ce 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json @@ -11,42 +11,42 @@ { "rel": "item", "type": "application/json", - "href": "./models/air2waterSat_2.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/AquaticEcosystemsOxygen.json" + "href": "./models/LSAMP_AWPC.json" }, { "rel": "item", "type": "application/json", - "href": "./models/BBTW.json" + "href": "./models/MSU_ARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/BTW.json" + "href": "./models/NDWaterTempDO.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/air2waterSat_2.json" }, { "rel": "item", "type": "application/json", - "href": "./models/LSAMP_AWPC.json" + "href": "./models/AquaticEcosystemsOxygen.json" }, { "rel": "item", "type": "application/json", - "href": "./models/MSU_ARIMA.json" + "href": "./models/BBTW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/NDWaterTempDO.json" + "href": "./models/BTW.json" }, { "rel": "item", @@ -126,17 +126,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/xgboost_parallel.json" + "href": "./models/wbears_gp.json" }, { "rel": "item", "type": "application/json", - "href": "./models/wbears_gp.json" + "href": "./models/wbears_rnn.json" }, { "rel": "item", "type": "application/json", - "href": "./models/wbears_rnn.json" + "href": "./models/xgboost_parallel.json" }, { "rel": "parent", diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json index 453087d569..3642d4d0eb 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json @@ -17,7 +17,7 @@ "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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "start_datetime": "2024-04-03T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/BBTW.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/BBTW.json index f1bf64da1f..9c87897e37 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 2a2f6375fe..004c92833e 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 420da946b7..4800f4b3f8 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] + [-149.6106, 68.6307], + [-82.0084, 29.676], + [-89.4737, 46.2097] ] }, "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: 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": "2024-08-02T00:00:00Z", + "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: 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": "2024-08-03T00:00:00Z", "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" + "TOOK", + "BARC", + "CRAM" ], "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 ca34085e11..715498cf1b 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 1f6028b8fb..81d7eac64d 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 8ff8707f86..757831094c 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 273ac0e4e0..7d48527ed2 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json @@ -9,6 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], @@ -29,26 +42,13 @@ [-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": "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": "2024-08-02T00:00:00Z", + "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: 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, MAYF, MCDI, MCRA, 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": "2024-08-03T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,6 +79,19 @@ "oxygen", "Daily", "P1D", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -99,19 +112,6 @@ "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_Dissolved_oxygen/models/cb_prophet.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json index a3820da72c..6a4784d600 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json @@ -9,21 +9,23 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-97.7823, 33.3785], - [-111.7979, 40.7839], - [-82.0177, 29.6878], + [-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], - [-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], @@ -33,22 +35,20 @@ [-77.9832, 39.0956], [-121.9338, 45.7908], [-87.4077, 32.9604], - [-96.443, 38.9459], - [-78.1473, 38.8943], - [-89.7048, 45.9983], - [-99.2531, 47.1298], - [-99.1139, 47.1591], [-122.1655, 44.2596], - [-149.143, 68.6698], - [-149.6106, 68.6307], + [-97.7823, 33.3785], + [-111.7979, 40.7839], + [-82.0177, 29.6878], [-119.0274, 36.9559], - [-119.2575, 37.0597] + [-88.1589, 31.8534], + [-84.2793, 35.9574], + [-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: PRIN, REDB, SUGG, SYCA, TOMB, WALK, WLOU, ARIK, BARC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, POSE, LIRO, PRPO, PRLA, MCRA, OKSR, TOOK, TECR, BIGC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-08-02T00:00:00Z", + "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": "2024-08-03T00:00:00Z", "start_datetime": "2022-06-01T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ @@ -79,21 +79,23 @@ "oxygen", "Daily", "P1D", - "PRIN", - "REDB", - "SUGG", + "BARC", + "COMO", + "CRAM", + "MCDI", + "POSE", + "OKSR", + "TOOK", + "PRLA", + "LIRO", + "PRPO", "SYCA", - "TOMB", - "WALK", - "WLOU", "ARIK", - "BARC", + "BIGC", "BLDE", "BLUE", "BLWA", "CARI", - "COMO", - "CRAM", "CUPE", "FLNT", "GUIL", @@ -103,16 +105,14 @@ "LEWI", "MART", "MAYF", - "MCDI", - "POSE", - "LIRO", - "PRPO", - "PRLA", "MCRA", - "OKSR", - "TOOK", + "PRIN", + "REDB", + "SUGG", "TECR", - "BIGC" + "TOMB", + "WALK", + "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 7fee87d987..a837308c2d 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json @@ -9,6 +9,10 @@ "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], @@ -21,34 +25,30 @@ [-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], [-105.5442, 40.035], [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-96.6242, 34.4442], [-87.7982, 32.5415], - [-147.504, 65.1532], [-89.4737, 46.2097], + [-99.2531, 47.1298], + [-147.504, 65.1532], [-89.7048, 45.9983], [-149.143, 68.6698], [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-88.1589, 31.8534], [-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: LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, COMO, CUPE, FLNT, GUIL, HOPB, KING, BLUE, BLWA, CARI, CRAM, 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": "2024-08-02T00:00:00Z", + "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: FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, COMO, CUPE, BLWA, CRAM, PRPO, CARI, LIRO, OKSR, 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": "2024-08-03T00:00:00Z", "start_datetime": "2021-05-01T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -79,6 +79,10 @@ "oxygen", "Daily", "P1D", + "FLNT", + "GUIL", + "HOPB", + "KING", "LECO", "LEWI", "MART", @@ -91,27 +95,23 @@ "SUGG", "SYCA", "TECR", + "TOMB", "WALK", "WLOU", "ARIK", "BARC", "BIGC", "BLDE", + "BLUE", "COMO", "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "BLUE", "BLWA", - "CARI", "CRAM", + "PRPO", + "CARI", "LIRO", "OKSR", "PRLA", - "PRPO", - "TOMB", "TOOK" ], "table:columns": [ diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json index 3173b33d81..dbe5ecb5cf 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json @@ -28,7 +28,7 @@ "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: SYCA, BARC, BIGC, BLDE, CRAM, KING, LIRO, MCRA, REDB, SUGG, LEWI, MAYF, 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "start_datetime": "2024-04-05T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json index 7a9b7df1e3..8246df8c6c 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.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], @@ -42,13 +26,29 @@ [-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], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914] ] }, "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: 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": "2024-08-02T00:00:00Z", + "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: 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": "2024-08-03T00:00:00Z", "start_datetime": "2022-08-25T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -79,22 +79,6 @@ "oxygen", "Daily", "P1D", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -112,7 +96,23 @@ "LECO", "LEWI", "LIRO", - "MART" + "MART", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "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 e6ffe13e41..f32c9f7f3e 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json @@ -15,6 +15,8 @@ [-89.7048, 45.9983], [-99.2531, 47.1298], [-89.4737, 46.2097], + [-83.5038, 35.6904], + [-77.9832, 39.0956], [-121.9338, 45.7908], [-87.4077, 32.9604], [-96.443, 38.9459], @@ -40,15 +42,13 @@ [-84.4374, 31.1854], [-66.7987, 18.1741], [-72.3295, 42.4719], - [-96.6038, 39.1051], - [-83.5038, 35.6904], - [-77.9832, 39.0956] + [-96.6038, 39.1051] ] }, "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: OKSR, TOOK, PRLA, LIRO, PRPO, CRAM, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, 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": "2024-08-02T00:00:00Z", + "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: OKSR, TOOK, PRLA, LIRO, PRPO, CRAM, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, 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": "2024-08-03T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -85,6 +85,8 @@ "LIRO", "PRPO", "CRAM", + "LECO", + "LEWI", "MART", "MAYF", "MCDI", @@ -110,9 +112,7 @@ "FLNT", "GUIL", "HOPB", - "KING", - "LECO", - "LEWI" + "KING" ], "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 3060280e66..13a1001327 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json @@ -9,20 +9,7 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-149.6106, 68.6307], - [-149.143, 68.6698], [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-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], - [-147.504, 65.1532], - [-105.5442, 40.035], [-66.9868, 18.1135], [-84.4374, 31.1854], [-66.7987, 18.1741], @@ -30,25 +17,38 @@ [-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], + [-119.2575, 37.0597], + [-110.5871, 44.9501], + [-96.6242, 34.4442], + [-87.7982, 32.5415], + [-147.504, 65.1532], + [-105.5442, 40.035] ] }, "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: TOOK, OKSR, CRAM, LIRO, PRLA, PRPO, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, 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": "2024-08-02T00:00:00Z", + "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: 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": "2024-08-03T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -79,20 +79,7 @@ "oxygen", "Daily", "P1D", - "TOOK", - "OKSR", "CRAM", - "LIRO", - "PRLA", - "PRPO", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", "CUPE", "FLNT", "GUIL", @@ -100,19 +87,32 @@ "KING", "LECO", "LEWI", + "LIRO", "MART", "MAYF", "MCDI", "MCRA", + "OKSR", "POSE", "PRIN", + "PRLA", + "PRPO", "REDB", "SUGG", "SYCA", "TECR", "TOMB", + "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO" ], "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 a5b6835821..c9d4d563fe 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 @@ -9,14 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -42,13 +34,21 @@ [-72.3295, 42.4719], [-96.6038, 39.1051], [-83.5038, 35.6904], - [-77.9832, 39.0956] + [-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] ] }, "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: 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": "2024-08-02T00:00:00Z", + "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: 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": "2024-08-03T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,14 +79,6 @@ "oxygen", "Daily", "P1D", - "LIRO", - "MART", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", "PRLA", "PRPO", "REDB", @@ -112,7 +104,15 @@ "HOPB", "KING", "LECO", - "LEWI" + "LEWI", + "LIRO", + "MART", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN" ], "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 ba434d7c0c..b361b307e7 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 @@ -9,12 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -42,13 +36,19 @@ [-111.7979, 40.7839], [-82.0177, 29.6878], [-111.5081, 33.751], - [-119.0274, 36.9559] + [-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] ] }, "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: 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, TECR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-08-02T00:00:00Z", + "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: 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": "2024-08-03T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,12 +79,6 @@ "oxygen", "Daily", "P1D", - "TOMB", - "TOOK", - "WALK", - "WLOU", - "ARIK", - "BARC", "BIGC", "BLDE", "BLUE", @@ -112,7 +106,13 @@ "REDB", "SUGG", "SYCA", - "TECR" + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", + "ARIK", + "BARC" ], "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 1e17af9fe4..49e10c43a3 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json @@ -9,12 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -42,13 +36,19 @@ [-72.3295, 42.4719], [-96.6038, 39.1051], [-83.5038, 35.6904], - [-77.9832, 39.0956] + [-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] ] }, "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: 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": "2024-08-02T00:00:00Z", + "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: 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, MAYF, MCDI, MCRA, OKSR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-08-03T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -79,12 +79,6 @@ "oxygen", "Daily", "P1D", - "LIRO", - "MART", - "MAYF", - "MCDI", - "MCRA", - "OKSR", "POSE", "PRIN", "PRLA", @@ -112,7 +106,13 @@ "HOPB", "KING", "LECO", - "LEWI" + "LEWI", + "LIRO", + "MART", + "MAYF", + "MCDI", + "MCRA", + "OKSR" ], "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 76bb36317d..04c756fe0a 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 +9,12 @@ "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], @@ -36,19 +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], - [-84.2793, 35.9574], - [-105.9154, 39.8914] + [-82.0177, 29.6878] ] }, "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: 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": "2024-08-02T00:00:00Z", + "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: 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": "2024-08-03T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,6 +79,12 @@ "oxygen", "Daily", "P1D", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -106,13 +112,7 @@ "PRLA", "PRPO", "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "SUGG" ], "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 f10e859561..70a6ba0381 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 @@ -9,22 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], [-102.4471, 39.7582], @@ -42,13 +26,29 @@ [-72.3295, 42.4719], [-96.6038, 39.1051], [-83.5038, 35.6904], - [-77.9832, 39.0956] + [-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] ] }, "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: 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": "2024-08-02T00:00:00Z", + "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: 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": "2024-08-03T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,22 +79,6 @@ "oxygen", "Daily", "P1D", - "LIRO", - "MART", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", "WALK", "WLOU", "ARIK", @@ -112,7 +96,23 @@ "HOPB", "KING", "LECO", - "LEWI" + "LEWI", + "LIRO", + "MART", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK" ], "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 824a3c7175..c105865d9b 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json @@ -9,16 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -42,13 +32,23 @@ [-122.1655, 44.2596], [-149.143, 68.6698], [-78.1473, 38.8943], - [-97.7823, 33.3785] + [-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] ] }, "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: 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": "2024-08-02T00:00:00Z", + "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": "2024-08-03T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -79,16 +79,6 @@ "oxygen", "Daily", "P1D", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -112,7 +102,17 @@ "MCRA", "OKSR", "POSE", - "PRIN" + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "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 6dbae00768..4193106fb7 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json @@ -48,7 +48,7 @@ "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: OKSR, PRLA, PRPO, TOOK, CRAM, LIRO, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ 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 b900350dba..965b6b2427 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 @@ -9,12 +9,20 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-122.1655, 44.2596], [-149.143, 68.6698], - [-149.6106, 68.6307], + [-78.1473, 38.8943], + [-97.7823, 33.3785], [-99.1139, 47.1591], - [-89.7048, 45.9983], [-99.2531, 47.1298], - [-89.4737, 46.2097], + [-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], @@ -23,6 +31,7 @@ [-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 +39,16 @@ [-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], - [-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] + [-96.443, 38.9459] ] }, "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: OKSR, TOOK, PRLA, LIRO, PRPO, CRAM, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, 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": "2024-08-02T00:00:00Z", + "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: 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, MAYF, MCDI.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-08-03T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,12 +79,20 @@ "oxygen", "Daily", "P1D", + "MCRA", "OKSR", - "TOOK", + "POSE", + "PRIN", "PRLA", - "LIRO", "PRPO", - "CRAM", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -93,6 +101,7 @@ "BLWA", "CARI", "COMO", + "CRAM", "CUPE", "FLNT", "GUIL", @@ -100,19 +109,10 @@ "KING", "LECO", "LEWI", + "LIRO", "MART", "MAYF", - "MCDI", - "MCRA", - "POSE", - "PRIN", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "WALK", - "WLOU" + "MCDI" ], "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 20c0170581..b7fac36cc3 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 +9,8 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-97.7823, 33.3785], + [-99.1139, 47.1591], [-99.2531, 47.1298], [-111.7979, 40.7839], [-82.0177, 29.6878], @@ -40,15 +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] + [-78.1473, 38.8943] ] }, "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: 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, 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": "2024-08-02T00:00:00Z", + "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: 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": "2024-08-03T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,6 +79,8 @@ "oxygen", "Daily", "P1D", + "PRIN", + "PRLA", "PRPO", "REDB", "SUGG", @@ -110,9 +112,7 @@ "MCDI", "MCRA", "OKSR", - "POSE", - "PRIN", - "PRLA" + "POSE" ], "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 b75a3af621..1d30fd8c80 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 7c1d46a2cf..020b76320e 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 255cc3dbcc..5a00e523c9 100644 --- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/xgboost_parallel.json +++ b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/models/xgboost_parallel.json @@ -9,17 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -36,19 +25,30 @@ [-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], + [-89.4737, 46.2097], [-89.7048, 45.9983], [-149.143, 68.6698], [-99.1139, 47.1591], [-99.2531, 47.1298], - [-149.6106, 68.6307], [-147.504, 65.1532], - [-89.4737, 46.2097] + [-149.6106, 68.6307] ] }, "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: MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, LIRO, OKSR, PRLA, PRPO, TOOK, CARI, 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": "2024-08-02T00:00:00Z", + "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: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, CRAM, LIRO, OKSR, PRLA, PRPO, CARI, 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": "2024-08-03T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2023-12-08T00:00:00Z", "providers": [ @@ -79,17 +79,6 @@ "oxygen", "Daily", "P1D", - "MCDI", - "MCRA", - "POSE", - "PRIN", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -106,13 +95,24 @@ "LEWI", "MART", "MAYF", + "MCDI", + "MCRA", + "POSE", + "PRIN", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "WALK", + "WLOU", + "CRAM", "LIRO", "OKSR", "PRLA", "PRPO", - "TOOK", "CARI", - "CRAM" + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/collection.json b/catalog/scores/Aquatics/Daily_Water_temperature/collection.json index a4d3f371f1..b448c87063 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/collection.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/collection.json @@ -11,72 +11,72 @@ { "rel": "item", "type": "application/json", - "href": "./models/GAM_air_wind.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/BBTW.json" + "href": "./models/GAM_air_wind.json" }, { "rel": "item", "type": "application/json", - "href": "./models/BTW.json" + "href": "./models/GLEON_JRabaey_temp_physics.json" }, { "rel": "item", "type": "application/json", - "href": "./models/air2waterSat_2.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/GLEON_physics.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_physics.json" + "href": "./models/air2waterSat_2.json" }, { "rel": "item", "type": "application/json", - "href": "./models/JorritsCrystalBall.json" + "href": "./models/TSLM_seasonal_JM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/LSAMP_AWPC.json" + "href": "./models/acp_fableLM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.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/acp_fableLM.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/bee_bake_RFModel_2024.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/bee_bake_RFModel_2024.json" }, { "rel": "item", @@ -96,17 +96,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/flareGLM_noDA.json" + "href": "./models/flareGLM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGOTM_noDA.json" + "href": "./models/flareSimstrat_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGLM.json" + "href": "./models/flareGLM_noDA.json" }, { "rel": "item", @@ -116,12 +116,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/lm_AT_WTL_WS.json" + "href": "./models/flareGOTM_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/mkricheldorf_w_lag.json" + "href": "./models/flare_ler.json" }, { "rel": "item", @@ -131,7 +131,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/flare_ler.json" + "href": "./models/mkricheldorf_w_lag.json" }, { "rel": "item", @@ -141,7 +141,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/flareSimstrat_noDA.json" + "href": "./models/lm_AT_WTL_WS.json" }, { "rel": "item", diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/BBTW.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/BBTW.json index a52185e0c2..66e00dc64b 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 1f913e9715..3c31343dc0 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 7b4deb6fde..959130cb61 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 @@ -21,7 +21,7 @@ "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: 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ 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 f55569ef3e..5559beb907 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 @@ -9,21 +9,6 @@ "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], - [-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], @@ -42,13 +27,28 @@ [-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], + [-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: 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": "2024-08-02T00:00:00Z", + "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: 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, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, 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": "2024-08-03T00:00:00Z", "start_datetime": "2022-11-15T00:00:00Z", "end_datetime": "2024-03-12T00:00:00Z", "providers": [ @@ -79,21 +79,6 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "LECO", - "LEWI", "LIRO", "MART", "MAYF", @@ -112,6 +97,21 @@ "TOOK", "WALK", "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "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 622cda13c5..ea42ca7daa 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 @@ -21,7 +21,7 @@ "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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "start_datetime": "2022-10-30T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ 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 9de08748ab..ff95d7de39 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 28d066742d..a6cd444c26 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 4e3be23c7d..95915b070e 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 72288ab474..9a2e29b381 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 99ede899f4..acc3fbb8d1 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "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 df0aa1b91f..cbddb7881d 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json @@ -9,7 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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 @@ [-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], - [-96.6242, 34.4442] + [-99.2531, 47.1298] ] }, "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: 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": "2024-08-02T00:00:00Z", + "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: 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, 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": "2024-08-03T00:00:00Z", "start_datetime": "2022-09-21T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,7 +79,19 @@ "temperature", "Daily", "P1D", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", + "ARIK", + "BARC", + "BIGC", "BLDE", + "BLUE", "BLWA", "CARI", "COMO", @@ -100,19 +112,7 @@ "POSE", "PRIN", "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLUE" + "PRPO" ], "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 50095e865e..3072b4a68a 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json @@ -9,46 +9,46 @@ "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], - [-105.5442, 40.035], - [-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], - [-89.4737, 46.2097], - [-89.7048, 45.9983], - [-99.2531, 47.1298], - [-99.1139, 47.1591], + [-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], - [-149.143, 68.6698], - [-149.6106, 68.6307] + [-105.5442, 40.035], + [-89.4737, 46.2097], + [-66.9868, 18.1135], + [-84.4374, 31.1854] ] }, "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: WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, CRAM, LIRO, PRPO, PRLA, CARI, 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": "2024-08-02T00:00:00Z", + "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: 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": "2024-08-03T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -79,40 +79,40 @@ "temperature", "Daily", "P1D", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "COMO", - "CUPE", - "FLNT", "GUIL", "HOPB", "KING", "LECO", "LEWI", + "LIRO", "MART", "MAYF", "MCDI", "MCRA", + "OKSR", "POSE", "PRIN", + "PRLA", + "PRPO", "REDB", "SUGG", "SYCA", "TECR", "TOMB", + "TOOK", "WALK", - "CRAM", - "LIRO", - "PRPO", - "PRLA", + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", "CARI", - "OKSR", - "TOOK" + "COMO", + "CRAM", + "CUPE", + "FLNT" ], "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 fe881f292f..f255f30c86 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 @@ -21,7 +21,7 @@ "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: 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ 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 3e50505469..cd2267696b 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": [ - [-119.2575, 37.0597], - [-82.0084, 29.676], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-96.443, 38.9459], - [-78.1473, 38.8943], - [-111.5081, 33.751], - [-149.143, 68.6698], - [-149.6106, 68.6307], - [-147.504, 65.1532], - [-99.1139, 47.1591], - [-89.7048, 45.9983], - [-99.2531, 47.1298], - [-66.7987, 18.1741], - [-83.5038, 35.6904], - [-119.0274, 36.9559], + [-82.0177, 29.6878], + [-88.1589, 31.8534], + [-84.2793, 35.9574], + [-105.9154, 39.8914], [-102.4471, 39.7582], + [-82.0084, 29.676], [-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], + [-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], - [-88.1589, 31.8534], - [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-96.6242, 34.4442] + [-119.0274, 36.9559], + [-87.4077, 32.9604], + [-119.2575, 37.0597], + [-96.6242, 34.4442], + [-111.5081, 33.751], + [-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: BIGC, BARC, COMO, CRAM, MCDI, POSE, SYCA, OKSR, TOOK, CARI, PRLA, LIRO, PRPO, GUIL, LECO, TECR, ARIK, BLDE, BLWA, CUPE, FLNT, HOPB, KING, LEWI, MART, MAYF, MCRA, PRIN, REDB, SUGG, TOMB, 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": "2024-08-02T00:00:00Z", + "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: SUGG, TOMB, WALK, WLOU, ARIK, BARC, BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, TECR, MAYF, BIGC, BLUE, SYCA, 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": "2024-08-03T00:00:00Z", "start_datetime": "2021-12-18T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ @@ -79,40 +79,40 @@ "temperature", "Daily", "P1D", - "BIGC", - "BARC", - "COMO", - "CRAM", - "MCDI", - "POSE", - "SYCA", - "OKSR", - "TOOK", - "CARI", - "PRLA", - "LIRO", - "PRPO", - "GUIL", - "LECO", - "TECR", + "SUGG", + "TOMB", + "WALK", + "WLOU", "ARIK", + "BARC", "BLDE", "BLWA", + "CARI", + "COMO", + "CRAM", "CUPE", "FLNT", + "GUIL", "HOPB", "KING", + "LECO", "LEWI", + "LIRO", "MART", - "MAYF", + "MCDI", "MCRA", + "POSE", "PRIN", + "PRLA", + "PRPO", "REDB", - "SUGG", - "TOMB", - "WALK", - "WLOU", - "BLUE" + "TECR", + "MAYF", + "BIGC", + "BLUE", + "SYCA", + "OKSR", + "TOOK" ], "table:columns": [ { diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/climatology.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/climatology.json index c752ecd570..a318e759b7 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/climatology.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/climatology.json @@ -35,20 +35,20 @@ [-97.7823, 33.3785], [-111.7979, 40.7839], [-82.0177, 29.6878], - [-89.4737, 46.2097], + [-96.6242, 34.4442], [-147.504, 65.1532], + [-89.4737, 46.2097], [-89.7048, 45.9983], - [-149.143, 68.6698], [-99.1139, 47.1591], [-99.2531, 47.1298], - [-149.6106, 68.6307], - [-96.6242, 34.4442] + [-149.143, 68.6698], + [-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: SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, CRAM, CARI, LIRO, OKSR, PRLA, PRPO, 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": "2024-08-02T00:00:00Z", + "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: SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, BLUE, 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": "2024-08-03T00:00:00Z", "start_datetime": "2021-05-01T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -105,14 +105,14 @@ "PRIN", "REDB", "SUGG", - "CRAM", + "BLUE", "CARI", + "CRAM", "LIRO", - "OKSR", "PRLA", "PRPO", - "TOOK", - "BLUE" + "OKSR", + "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 eedb3c02db..e7e9dd816d 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 @@ -9,46 +9,46 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-87.7982, 32.5415], - [-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], [-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], [-84.2793, 35.9574], [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], - [-119.2575, 37.0597], - [-96.6242, 34.4442], [-110.5871, 44.9501], - [-96.6038, 39.1051], - [-111.5081, 33.751], - [-89.7048, 45.9983], - [-149.143, 68.6698], - [-99.1139, 47.1591], - [-99.2531, 47.1298], + [-96.6242, 34.4442], + [-87.7982, 32.5415], + [-105.5442, 40.035], [-147.504, 65.1532], - [-89.4737, 46.2097], - [-149.6106, 68.6307] + [-99.1139, 47.1591], + [-119.2575, 37.0597], + [-122.1655, 44.2596], + [-149.143, 68.6698], + [-149.6106, 68.6307], + [-88.1589, 31.8534] ] }, "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: BLWA, COMO, CUPE, FLNT, GUIL, HOPB, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLUE, BLDE, KING, SYCA, LIRO, OKSR, PRLA, PRPO, CARI, 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": "2024-08-02T00:00:00Z", + "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: CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, POSE, PRIN, PRPO, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BLDE, BLUE, BLWA, COMO, CARI, PRLA, BIGC, MCRA, 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": "2024-08-03T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -79,40 +79,40 @@ "temperature", "Daily", "P1D", - "BLWA", - "COMO", + "CRAM", "CUPE", "FLNT", "GUIL", "HOPB", + "KING", "LECO", "LEWI", + "LIRO", "MART", "MAYF", "MCDI", - "MCRA", "POSE", "PRIN", + "PRPO", "REDB", "SUGG", + "SYCA", "TECR", - "TOMB", "WALK", "WLOU", "ARIK", "BARC", - "BIGC", - "BLUE", "BLDE", - "KING", - "SYCA", - "LIRO", - "OKSR", - "PRLA", - "PRPO", + "BLUE", + "BLWA", + "COMO", "CARI", - "CRAM", - "TOOK" + "PRLA", + "BIGC", + "MCRA", + "OKSR", + "TOOK", + "TOMB" ], "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 88638864d0..7dfc268fd1 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json @@ -48,7 +48,7 @@ "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: 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-03T00:00:00Z", "start_datetime": "2022-10-21T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json index 87f426fcfa..59ae74fc17 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM.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] + [-149.6106, 68.6307] ] }, "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: 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": "2024-08-02T00:00:00Z", + "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": "2024-08-03T00:00:00Z", "start_datetime": "2022-12-08T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -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/flareGLM_noDA.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json index 8553797338..86525788b8 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json @@ -21,7 +21,7 @@ "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: 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": "2024-08-02T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-03-02T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ 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 ea04d1acb2..a03fc7f160 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-89.4737, 46.2097], - [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], [-82.0177, 29.6878], [-82.0084, 29.676], + [-89.4737, 46.2097], + [-89.7048, 45.9983], [-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: 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": "2024-08-02T00:00:00Z", + "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, PRPO, SUGG, BARC, CRAM, LIRO, 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-20T00:00:00Z", "providers": [ @@ -52,12 +52,12 @@ "temperature", "Daily", "P1D", - "CRAM", - "LIRO", "PRLA", "PRPO", "SUGG", "BARC", + "CRAM", + "LIRO", "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 bad8fde049..ca1d2dfa66 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json @@ -9,17 +9,17 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-89.4737, 46.2097], - [-99.1139, 47.1591], [-99.2531, 47.1298], - [-82.0177, 29.6878], [-82.0084, 29.676], + [-82.0177, 29.6878], + [-89.4737, 46.2097], + [-99.1139, 47.1591], [-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: CRAM, 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)", + "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": "2024-08-03T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", @@ -51,11 +51,11 @@ "temperature", "Daily", "P1D", - "CRAM", - "PRLA", "PRPO", - "SUGG", "BARC", + "SUGG", + "CRAM", + "PRLA", "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 13d359ccaa..d8ad0ec088 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/flare_ler.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/flare_ler.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-82.0177, 29.6878], - [-82.0084, 29.676], [-89.4737, 46.2097], [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], + [-82.0177, 29.6878], + [-82.0084, 29.676], [-149.6106, 68.6307] ] }, "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: SUGG, BARC, 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ @@ -52,12 +52,12 @@ "temperature", "Daily", "P1D", - "SUGG", - "BARC", "CRAM", "LIRO", "PRLA", "PRPO", + "SUGG", + "BARC", "TOOK" ], "table:columns": [ 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 bb8f7f1fde..0643ec2ec9 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 @@ -9,8 +9,8 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-82.0177, 29.6878], [-82.0084, 29.676], + [-82.0177, 29.6878], [-89.4737, 46.2097], [-89.7048, 45.9983], [-99.1139, 47.1591], @@ -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: SUGG, BARC, 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "start_datetime": "2023-03-17T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ @@ -78,8 +78,8 @@ "temperature", "Daily", "P1D", - "SUGG", "BARC", + "SUGG", "CRAM", "LIRO", "PRLA", diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json index 58a1710693..e992eecaa0 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json @@ -44,7 +44,7 @@ "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: PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BLDE, BLWA, COMO, FLNT, HOPB, KING, LECO, LEWI, MAYF, MCRA, POSE, PRIN, CRAM, CARI, BIGC, BLUE, CUPE, GUIL, 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ 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 550d31f7ba..401b2cdef0 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 @@ -21,7 +21,7 @@ "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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ 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 90e7ca4b55..546122fe4a 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 @@ -9,19 +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] + [-149.6106, 68.6307], + [-82.0084, 29.676], + [-89.4737, 46.2097], + [-89.7048, 45.9983] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2024-03-06T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -52,13 +52,13 @@ "temperature", "Daily", "P1D", - "BARC", - "CRAM", - "LIRO", "PRLA", "PRPO", "SUGG", - "TOOK" + "TOOK", + "BARC", + "CRAM", + "LIRO" ], "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 d838c7ed60..e242e2b73d 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 @@ -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": "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: 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "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/persistenceRW.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json index 64c206da38..843aba4e43 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json @@ -9,15 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -42,13 +33,22 @@ [-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": "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: 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, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "start_datetime": "2022-08-25T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -79,15 +79,6 @@ "temperature", "Daily", "P1D", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -112,6 +103,15 @@ "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/precip_mod.json b/catalog/scores/Aquatics/Daily_Water_temperature/models/precip_mod.json index a85d813c56..718da521af 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/precip_mod.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/precip_mod.json @@ -21,7 +21,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2022-12-24T00:00:00Z", "end_datetime": "2024-01-24T00:00:00Z", "providers": [ 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 6e44ae483e..eeff873ddf 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json @@ -9,21 +9,13 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-149.143, 68.6698], - [-99.1139, 47.1591], - [-99.2531, 47.1298], - [-149.6106, 68.6307], [-119.2575, 37.0597], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-102.4471, 39.7582], - [-82.0084, 29.676], [-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], @@ -31,24 +23,32 @@ [-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: LIRO, MART, OKSR, PRLA, PRPO, TOOK, BIGC, CARI, COMO, CRAM, ARIK, BARC, BLDE, BLUE, BLWA, CUPE, FLNT, GUIL, HOPB, KING, 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "start_datetime": "2021-12-18T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -79,21 +79,13 @@ "temperature", "Daily", "P1D", - "LIRO", - "MART", - "OKSR", - "PRLA", - "PRPO", - "TOOK", "BIGC", - "CARI", - "COMO", - "CRAM", - "ARIK", - "BARC", "BLDE", "BLUE", "BLWA", + "CARI", + "COMO", + "CRAM", "CUPE", "FLNT", "GUIL", @@ -101,18 +93,26 @@ "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 f36f5f17c9..0a37e8153f 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json @@ -9,21 +9,21 @@ "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], + [-147.504, 65.1532], + [-105.5442, 40.035], + [-89.4737, 46.2097], + [-89.7048, 45.9983], + [-121.9338, 45.7908], + [-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], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], [-66.9868, 18.1135], [-84.4374, 31.1854], [-66.7987, 18.1741], @@ -31,24 +31,24 @@ [-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] + [-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: 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": "2024-08-03T00:00:00Z", + "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, CARI, COMO, CRAM, LIRO, MART, OKSR, PRLA, PRPO, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CUPE, FLNT, GUIL, HOPB, KING, 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-08-06T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -79,21 +79,21 @@ "temperature", "Daily", "P1D", - "SYCA", - "TECR", - "TOMB", "TOOK", - "WALK", - "WLOU", + "CARI", + "COMO", + "CRAM", + "LIRO", + "MART", + "OKSR", + "PRLA", + "PRPO", "ARIK", "BARC", "BIGC", "BLDE", "BLUE", "BLWA", - "CARI", - "COMO", - "CRAM", "CUPE", "FLNT", "GUIL", @@ -101,18 +101,18 @@ "KING", "LECO", "LEWI", - "LIRO", - "MART", "MAYF", "MCDI", "MCRA", - "OKSR", "POSE", "PRIN", - "PRLA", - "PRPO", "REDB", - "SUGG" + "SUGG", + "SYCA", + "TECR", + "TOMB", + "WALK", + "WLOU" ], "table:columns": [ { 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 ef7b398ce3..d4f9ffafb0 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 @@ -48,7 +48,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ 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 ad845b6af7..04fb98cce1 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 @@ -48,7 +48,7 @@ "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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ 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 3528339352..d7c81575e0 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_lasso.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_lasso.json @@ -9,12 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -42,13 +36,19 @@ [-97.7823, 33.3785], [-99.1139, 47.1591], [-99.2531, 47.1298], - [-111.7979, 40.7839] + [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -79,12 +79,6 @@ "temperature", "Daily", "P1D", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", "WLOU", "ARIK", "BARC", @@ -112,7 +106,13 @@ "PRIN", "PRLA", "PRPO", - "REDB" + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK" ], "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 929d5ccfd8..91568910fd 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 +9,8 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-110.5871, 44.9501], + [-96.6242, 34.4442], [-87.7982, 32.5415], [-147.504, 65.1532], [-105.5442, 40.035], @@ -40,15 +42,13 @@ [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442] + [-119.2575, 37.0597] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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, BIGC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,6 +79,8 @@ "temperature", "Daily", "P1D", + "BLDE", + "BLUE", "BLWA", "CARI", "COMO", @@ -110,9 +112,7 @@ "WLOU", "ARIK", "BARC", - "BIGC", - "BLDE", - "BLUE" + "BIGC" ], "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 57fb6b3907..35078800ce 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 @@ -9,6 +9,17 @@ "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], @@ -31,24 +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] + [-105.9154, 39.8914] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,6 +79,17 @@ "temperature", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", "GUIL", "HOPB", "KING", @@ -101,18 +112,7 @@ "TOMB", "TOOK", "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT" + "WLOU" ], "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 2b863f11b6..3056b0d668 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 +9,8 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-96.443, 38.9459], + [-122.1655, 44.2596], [-149.143, 68.6698], [-78.1473, 38.8943], [-97.7823, 33.3785], @@ -40,15 +42,13 @@ [-77.9832, 39.0956], [-89.7048, 45.9983], [-121.9338, 45.7908], - [-87.4077, 32.9604], - [-96.443, 38.9459], - [-122.1655, 44.2596] + [-87.4077, 32.9604] ] }, "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: 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, MAYF, MCDI, MCRA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-08-03T00:00:00Z", + "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: 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, MAYF.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -79,6 +79,8 @@ "temperature", "Daily", "P1D", + "MCDI", + "MCRA", "OKSR", "POSE", "PRIN", @@ -110,9 +112,7 @@ "LEWI", "LIRO", "MART", - "MAYF", - "MCDI", - "MCRA" + "MAYF" ], "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 80edfd3baa..69e95cfc1d 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json @@ -9,37 +9,37 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-111.5081, 33.751], + [-105.5442, 40.035], + [-149.143, 68.6698], [-149.6106, 68.6307], + [-96.6038, 39.1051], + [-147.504, 65.1532], + [-99.1139, 47.1591], + [-89.7048, 45.9983], + [-99.2531, 47.1298], [-119.2575, 37.0597], - [-149.143, 68.6698], [-119.0274, 36.9559], [-102.4471, 39.7582], [-82.0084, 29.676], [-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], [-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], [-88.1589, 31.8534], [-84.2793, 35.9574], [-105.9154, 39.8914] @@ -47,8 +47,8 @@ }, "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: TOOK, BIGC, OKSR, TECR, ARIK, BARC, 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, 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": "2024-08-03T00:00:00Z", + "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: SYCA, COMO, OKSR, TOOK, KING, CARI, PRLA, LIRO, PRPO, BIGC, TECR, ARIK, BARC, BLDE, BLUE, BLWA, CRAM, CUPE, FLNT, GUIL, HOPB, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-08-06T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ @@ -79,37 +79,37 @@ "temperature", "Daily", "P1D", + "SYCA", + "COMO", + "OKSR", "TOOK", + "KING", + "CARI", + "PRLA", + "LIRO", + "PRPO", "BIGC", - "OKSR", "TECR", "ARIK", "BARC", "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", "TOMB", "WALK", "WLOU" 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 6616285da3..ce2949af12 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 +9,14 @@ "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], - [-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 +24,31 @@ [-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] ] }, "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": "2024-08-03T00:00:00Z", + "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: 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, 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": "2024-08-04T00:00:00Z", "start_datetime": "2021-12-18T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,20 +79,14 @@ "temperature", "Daily", "P1D", - "BIGC", - "OKSR", - "TOOK", - "CARI", - "PRLA", - "LIRO", - "PRPO", - "CRAM", - "ARIK", "BARC", + "BIGC", "BLDE", "BLUE", "BLWA", + "CARI", "COMO", + "CRAM", "CUPE", "FLNT", "GUIL", @@ -100,19 +94,25 @@ "KING", "LECO", "LEWI", + "LIRO", "MART", "MAYF", "MCDI", "MCRA", + "OKSR", "POSE", "PRIN", + "PRLA", + "PRPO", "REDB", "SUGG", "SYCA", "TECR", "TOMB", + "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK" ], "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 d976375c7c..9de66d59d7 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 @@ -9,23 +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], [-87.4077, 32.9604], [-96.443, 38.9459], @@ -42,13 +25,30 @@ [-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], + [-77.9832, 39.0956], + [-89.7048, 45.9983] ] }, "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": "2024-08-03T00:00:00Z", + "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: 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, 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,23 +79,6 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "LECO", - "LEWI", - "LIRO", "MART", "MAYF", "MCDI", @@ -112,7 +95,24 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING", + "LECO", + "LEWI", + "LIRO" ], "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 f24c08bdb2..91dc4b0fef 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 6713c66b17..2e8dae41fb 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 88ae0307bb..c956314a13 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 25dfe9bc16..b6cccedb3a 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/xgboost_parallel.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/xgboost_parallel.json @@ -9,8 +9,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], - [-149.6106, 68.6307], [-84.2793, 35.9574], [-105.9154, 39.8914], [-102.4471, 39.7582], @@ -18,37 +28,27 @@ [-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], + [-87.7982, 32.5415], + [-89.4737, 46.2097], [-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] + [-149.6106, 68.6307], + [-147.504, 65.1532], + [-149.143, 68.6698], + [-99.1139, 47.1591] ] }, "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: 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, TECR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-08-03T00:00:00Z", + "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: LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, BLWA, CRAM, LIRO, PRPO, TOOK, CARI, OKSR, 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2023-12-08T00:00:00Z", "providers": [ @@ -79,8 +79,18 @@ "temperature", "Daily", "P1D", + "LEWI", + "MART", + "MAYF", + "MCDI", + "MCRA", + "POSE", + "PRIN", + "REDB", + "SUGG", + "SYCA", + "TECR", "TOMB", - "TOOK", "WALK", "WLOU", "ARIK", @@ -88,31 +98,21 @@ "BIGC", "BLDE", "BLUE", - "BLWA", - "CARI", "COMO", - "CRAM", "CUPE", "FLNT", "GUIL", "HOPB", "KING", "LECO", - "LEWI", + "BLWA", + "CRAM", "LIRO", - "MART", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR" + "TOOK", + "CARI", + "OKSR", + "PRLA" ], "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 6be59d7170..fbfab7c642 100644 --- a/catalog/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json +++ b/catalog/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json @@ -21,7 +21,7 @@ "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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-07-27T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/collection.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/collection.json index 2057567abd..e25ba496f9 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/collection.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_abundance/collection.json @@ -21,47 +21,47 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_humidity_lm.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_tbats.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "parent", 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 87bbf44ef4..4cd7e8026c 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 @@ -9,15 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -55,13 +46,22 @@ [-78.0418, 39.0337], [-147.5026, 65.154], [-97.57, 33.4012], - [-104.7456, 40.8155] + [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", "end_datetime": "2024-07-15T00:00:00Z", "providers": [ @@ -92,15 +92,6 @@ "abundance", "Weekly", "P1W", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -138,7 +129,16 @@ "BLAN", "BONA", "CLBJ", - "CPER" + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "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 39124662ef..2373451e59 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 @@ -61,7 +61,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", "end_datetime": "2024-07-15T00:00:00Z", "providers": [ 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 272645246a..11b8baa0b5 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 611eb66d94..ab2faf9b9c 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 827891ef48..226a366aa3 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 @@ -59,7 +59,7 @@ "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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2022-12-26T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ 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 c901948062..5478bf5fb0 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 c91f4db9bc..037768356d 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 6a6d63e750..d229ff81ee 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 @@ -61,7 +61,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2022-12-26T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json index 04d4328f09..b96834a853 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 @@ -9,6 +9,29 @@ "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], @@ -32,36 +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], - [-110.5391, 44.9535] + [-105.5824, 40.0543] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", "end_datetime": "2024-07-15T00:00:00Z", "providers": [ @@ -92,6 +92,29 @@ "abundance", "Weekly", "P1W", + "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", @@ -115,30 +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", - "YELL" + "NIWO" ], "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 178ddda2a6..3306cd1d8f 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 @@ -61,7 +61,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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_temp_lm_all_sites.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json index 5bae936264..67596f5764 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 3aede3b75c..ec0ef53274 100644 --- a/catalog/scores/Beetles/Weekly_beetle_community_richness/collection.json +++ b/catalog/scores/Beetles/Weekly_beetle_community_richness/collection.json @@ -11,57 +11,57 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_precip_lm_all_sites.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", 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 89a3bfad1e..007d4f2a61 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 @@ -9,22 +9,6 @@ "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], [-110.8355, 31.9107], [-89.5864, 45.5089], @@ -38,11 +22,11 @@ [-99.2413, 47.1282], [-121.9519, 45.8205], [-110.5391, 44.9535], - [-156.6194, 71.2824], - [-147.5026, 65.154], [-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], @@ -55,13 +39,29 @@ [-149.2133, 63.8758], [-84.4686, 31.1948], [-106.8425, 32.5907], - [-96.6129, 39.1104] + [-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] ] }, "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, BARR, BONA, ABBY, BART, BLAN, 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": "2024-08-03T00:00:00Z", + "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: 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, 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": "2024-08-04T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", "end_datetime": "2024-07-15T00:00:00Z", "providers": [ @@ -92,22 +92,6 @@ "richness", "Weekly", "P1W", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", "SOAP", "SRER", "STEI", @@ -121,11 +105,11 @@ "WOOD", "WREF", "YELL", - "BARR", - "BONA", "ABBY", + "BARR", "BART", "BLAN", + "BONA", "CLBJ", "CPER", "DCFS", @@ -138,7 +122,23 @@ "HEAL", "JERC", "JORN", - "KONA" + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER" ], "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 a557de5317..12f054cc61 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 @@ -9,22 +9,6 @@ "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], @@ -55,13 +39,29 @@ [-99.0588, 35.4106], [-112.4524, 40.1776], [-84.2826, 35.9641], - [-81.9934, 29.6893] + [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", "end_datetime": "2024-07-15T00:00:00Z", "providers": [ @@ -92,22 +92,6 @@ "richness", "Weekly", "P1W", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", "WREF", "YELL", "ABBY", @@ -138,7 +122,23 @@ "OAES", "ONAQ", "ORNL", - "OSBS" + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD" ], "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 db105fb413..d8db7755d3 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 7ab4065ead..4804451a28 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 39787a4caa..d60e7319a0 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 @@ -59,7 +59,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2022-12-26T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ 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 ac350f49d6..85a3cf46f2 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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 a0e8305f4a..2e731856d9 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 fbb8c7f72b..4c977f6f2c 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 @@ -9,15 +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], [-81.4362, 28.1251], [-83.5019, 35.689], [-66.8687, 17.9696], @@ -48,20 +39,29 @@ [-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], - [-149.3705, 68.6611] + [-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] ] }, "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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-12-26T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ @@ -92,15 +92,6 @@ "richness", "Weekly", "P1W", - "ABBY", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", "DSNY", "GRSM", "GUAN", @@ -131,14 +122,23 @@ "STER", "TALL", "TEAK", + "TOOL", "TREE", "UKFS", "UNDE", "WOOD", "WREF", "YELL", + "ABBY", "BARR", - "TOOL" + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA" ], "table:columns": [ { 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 46c89ab94b..7741ad3a21 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 @@ -9,59 +9,59 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-84.4686, 31.1948], + [-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], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], + [-149.3705, 68.6611], + [-145.7514, 63.8811], + [-149.2133, 63.8758], + [-103.0293, 40.4619], + [-122.3303, 45.7624], + [-99.1066, 47.1617], + [-84.4686, 31.1948], + [-119.2622, 37.0334], + [-119.006, 37.0058], + [-99.2413, 47.1282], + [-81.4362, 28.1251], [-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], + [-89.5373, 46.2339], + [-121.9519, 45.8205], + [-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], + [-87.8039, 32.5417], + [-80.5248, 37.3783], + [-81.9934, 29.6893], [-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], + [-76.56, 38.8901], [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], + [-88.1612, 31.8539], + [-109.3883, 38.2483], [-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] + [-67.0769, 18.0213], + [-99.0588, 35.4106] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2021-05-17T00:00:00Z", "end_datetime": "2024-07-15T00:00:00Z", "providers": [ @@ -92,53 +92,53 @@ "richness", "Weekly", "P1W", - "JERC", + "SJER", + "GUAN", + "ONAQ", + "BARR", + "SRER", + "YELL", + "BONA", "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", + "TOOL", + "DEJU", + "HEAL", + "STER", + "ABBY", + "DCFS", + "JERC", + "SOAP", + "TEAK", + "WOOD", + "DSNY", "NIWO", "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", "PUUM", + "UNDE", + "WREF", + "GRSM", + "HARV", + "KONA", + "KONZ", + "ORNL", "RMNP", + "TREE", + "BART", + "BLAN", + "DELA", + "MLBS", + "OSBS", "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", "STEI", - "STER", + "SERC", "TALL", - "TEAK", - "TOOL", - "TREE", + "LENO", + "MOAB", "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", "CLBJ", "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "LAJA", + "OAES" ], "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 9cba18fdab..6c9bb9cec6 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 f131a42dc6..ae072c92d1 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 @@ -9,26 +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], @@ -55,13 +35,33 @@ [-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": "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -92,26 +92,6 @@ "richness", "Weekly", "P1W", - "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_Green_chromatic_coordinate/collection.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json index 0e30d576b5..d2baa9ea44 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json @@ -16,47 +16,47 @@ { "rel": "item", "type": "application/json", - "href": "./models/CSP_Gwave.json" + "href": "./models/DALEC_SIP.json" }, { "rel": "item", "type": "application/json", - "href": "./models/CU_Pheno.json" + "href": "./models/EFI_U_P.json" }, { "rel": "item", "type": "application/json", - "href": "./models/ChlorophyllCrusaders.json" + "href": "./models/CSP_Gwave.json" }, { "rel": "item", "type": "application/json", - "href": "./models/DALEC_SIP.json" + "href": "./models/CU_Pheno.json" }, { "rel": "item", "type": "application/json", - "href": "./models/PEG_RFR.json" + "href": "./models/ChlorophyllCrusaders.json" }, { "rel": "item", "type": "application/json", - "href": "./models/PEG_RFR0.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/Fourier.json" }, { "rel": "item", "type": "application/json", - "href": "./models/EFI_U_P.json" + "href": "./models/NEFIpheno.json" }, { "rel": "item", @@ -71,12 +71,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/VT_Ph_GDD.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/VT_Ph_GDD.json" }, { "rel": "item", @@ -86,12 +86,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/greenbears.json" }, { "rel": "item", "type": "application/json", - "href": "./models/greenbears.json" + "href": "./models/tg_arima.json" }, { "rel": "item", 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 a6c5d26d29..43e55fca49 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 @@ -22,7 +22,7 @@ "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: UKFS, BART, CLBJ, DELA, GRSM, HARV, SCBI, 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2021-04-08T00:00:00Z", "end_datetime": "2021-11-21T00:00:00Z", "providers": [ 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 f6069202f9..d8c932627e 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 b455c0b98c..89f6c6315e 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 1a059829cc..cc8478c6ed 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 @@ -21,7 +21,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2021-03-01T00:00:00Z", "end_datetime": "2021-08-19T00:00:00Z", "providers": [ 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 9755df234d..14655c25f5 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 55ac9197e0..357c6addd7 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 d74eb5a782..bf148c1012 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 80afb14234..e617fedd4d 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 +17,22 @@ [-95.1921, 39.0404], [-71.2874, 44.0639], [-97.57, 33.4012], + [-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], + [-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], @@ -39,29 +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], - [-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] + [-105.5824, 40.0543] ] }, "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: DELA, GRSM, HARV, SCBI, STEI, UKFS, BART, CLBJ, 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, 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": "2024-08-03T00:00:00Z", + "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: DELA, GRSM, HARV, SCBI, STEI, UKFS, BART, CLBJ, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SERC, SJER, SOAP, SRER, 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.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2021-02-24T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ @@ -100,6 +100,22 @@ "UKFS", "BART", "CLBJ", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SERC", + "SJER", + "SOAP", + "SRER", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", "UNDE", "WOOD", "WREF", @@ -122,23 +138,7 @@ "LENO", "MLBS", "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SERC", - "SJER", - "SOAP", - "SRER", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE" + "NIWO" ], "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 49f07df388..d9d31453e4 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 @@ -22,7 +22,7 @@ "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: HARV, SCBI, STEI, UKFS, BART, CLBJ, DELA, GRSM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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/PEG_RFR0.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/PEG_RFR0.json index 0782ab3444..8a15f9f74a 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 9e001a76e5..1671586d64 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 @@ -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": "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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2020-04-27T00:00:00Z", "end_datetime": "2021-11-22T00:00:00Z", "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/VT_Ph_GDD.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/VT_Ph_GDD.json index ce041ec547..2e22e00d09 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 5d295ec2c1..fd8ee7cb81 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 @@ -9,6 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], @@ -42,26 +55,13 @@ [-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] + [-119.7323, 37.1088] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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, 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-05-29T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ @@ -92,6 +92,19 @@ "gcc_90", "Daily", "P1D", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -125,20 +138,7 @@ "RMNP", "SCBI", "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "SJER" ], "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 652e057692..f64b1e7d64 100644 --- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json +++ b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json @@ -17,51 +17,51 @@ [-72.1727, 42.5369], [-78.1395, 38.8929], [-89.5864, 45.5089], - [-104.7456, 40.8155], - [-81.4362, 28.1251], - [-106.8425, 32.5907], - [-96.5631, 39.1008], - [-80.5248, 37.3783], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-76.56, 38.8901], - [-110.8355, 31.9107], - [-99.2413, 47.1282], - [-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], + [-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], + [-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], + [-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], - [-119.7323, 37.1088] + [-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] ] }, "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: UKFS, BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, CPER, DSNY, JORN, KONZ, MLBS, OAES, ONAQ, SERC, SRER, WOOD, 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, 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": "2024-08-03T00:00:00Z", + "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: UKFS, BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, 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, 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": "2024-08-04T00:00:00Z", "start_datetime": "2021-01-01T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -100,45 +100,45 @@ "HARV", "SCBI", "STEI", - "CPER", - "DSNY", - "JORN", - "KONZ", - "MLBS", - "OAES", - "ONAQ", - "SERC", - "SRER", - "WOOD", - "SOAP", - "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", - "SJER" + "SERC", + "SJER", + "SOAP", + "SRER", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE" ], "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 0122b82f36..30e616b716 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 5d6a523079..0c245a7126 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 +9,22 @@ "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], @@ -39,29 +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] + [-155.3173, 19.5531] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-08-24T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -92,6 +92,22 @@ "gcc_90", "Daily", "P1D", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", "YELL", "ABBY", "BARR", @@ -122,23 +138,7 @@ "ONAQ", "ORNL", "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF" + "PUUM" ], "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 4705d3c3ff..46bfa5a705 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 +9,15 @@ "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], [-78.0418, 39.0337], [-147.5026, 65.154], @@ -46,22 +55,13 @@ [-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] + [-119.006, 37.0058] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -92,6 +92,15 @@ "gcc_90", "Daily", "P1D", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR", "BART", "BLAN", "BONA", @@ -129,16 +138,7 @@ "STEI", "STER", "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR" + "TEAK" ], "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 a3355039e4..b1c9371a6a 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 @@ -9,8 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-97.57, 33.4012], - [-104.7456, 40.8155], [-99.1066, 47.1617], [-145.7514, 63.8811], [-87.8039, 32.5417], @@ -55,13 +53,15 @@ [-156.6194, 71.2824], [-71.2874, 44.0639], [-78.0418, 39.0337], - [-147.5026, 65.154] + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155] ] }, "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: 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, 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -92,8 +92,6 @@ "gcc_90", "Daily", "P1D", - "CLBJ", - "CPER", "DCFS", "DEJU", "DELA", @@ -138,7 +136,9 @@ "BARR", "BART", "BLAN", - "BONA" + "BONA", + "CLBJ", + "CPER" ], "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 fcf0cb43a0..bb704f36df 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 @@ -9,6 +9,15 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], @@ -46,22 +55,13 @@ [-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] + [-119.7323, 37.1088] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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, 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,6 +92,15 @@ "gcc_90", "Daily", "P1D", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", "UNDE", "WOOD", "WREF", @@ -129,16 +138,7 @@ "RMNP", "SCBI", "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS" + "SJER" ], "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 534c777fac..b281d98f73 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 @@ -9,6 +9,12 @@ "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], @@ -49,19 +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] + [-110.5391, 44.9535] ] }, "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: 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, BONA, 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,6 +92,12 @@ "gcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", "CPER", "DCFS", "DEJU", @@ -132,13 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ" + "YELL" ], "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 146d823630..d7b4098b3a 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 @@ -9,6 +9,14 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], @@ -47,21 +55,13 @@ [-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] + [-97.57, 33.4012] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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, BONA, 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,6 +92,14 @@ "gcc_90", "Daily", "P1D", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", "HEAL", "JERC", "JORN", @@ -130,15 +138,7 @@ "BART", "BLAN", "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV" + "CLBJ" ], "table:columns": [ { 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 e4fc26fafd..55de799b80 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 @@ -9,19 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -55,13 +42,26 @@ [-106.8425, 32.5907], [-96.6129, 39.1104], [-96.5631, 39.1008], - [-67.0769, 18.0213] + [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,19 +92,6 @@ "gcc_90", "Daily", "P1D", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", "SJER", "SOAP", "SRER", @@ -138,7 +125,20 @@ "JORN", "KONA", "KONZ", - "LAJA" + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC" ], "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 061043d78c..9152b3549f 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 @@ -9,6 +9,15 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], @@ -46,22 +55,13 @@ [-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] + [-147.5026, 65.154] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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, 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,6 +92,15 @@ "gcc_90", "Daily", "P1D", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", "HEAL", "JERC", "JORN", @@ -129,16 +138,7 @@ "BARR", "BART", "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV" + "BONA" ], "table:columns": [ { 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 042c02b146..5326ab2162 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 @@ -9,6 +9,24 @@ "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], [-88.1612, 31.8539], @@ -37,31 +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], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104] + [-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: 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,6 +92,24 @@ "gcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", "KONZ", "LAJA", "LENO", @@ -120,25 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA" + "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 77da7ebf3b..d26bc715a8 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 @@ -9,27 +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], - [-88.1612, 31.8539], [-80.5248, 37.3783], [-109.3883, 38.2483], [-105.5824, 40.0543], @@ -55,13 +34,34 @@ [-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], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-67.0769, 18.0213], + [-88.1612, 31.8539] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -92,27 +92,6 @@ "gcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", "MLBS", "MOAB", "NIWO", @@ -138,7 +117,28 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO" ], "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 71634ae26b..829ae17ca7 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 @@ -9,8 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-97.57, 33.4012], - [-104.7456, 40.8155], [-99.1066, 47.1617], [-145.7514, 63.8811], [-87.8039, 32.5417], @@ -55,13 +53,15 @@ [-156.6194, 71.2824], [-71.2874, 44.0639], [-78.0418, 39.0337], - [-147.5026, 65.154] + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155] ] }, "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: 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, 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,8 +92,6 @@ "gcc_90", "Daily", "P1D", - "CLBJ", - "CPER", "DCFS", "DEJU", "DELA", @@ -138,7 +136,9 @@ "BARR", "BART", "BLAN", - "BONA" + "BONA", + "CLBJ", + "CPER" ], "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 7b23e86f1c..d92876028c 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 @@ -9,6 +9,14 @@ "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], @@ -47,21 +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] + [-110.5391, 44.9535] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,6 +92,14 @@ "gcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", "DEJU", "DELA", "DSNY", @@ -130,15 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS" + "YELL" ], "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 fc04f981b5..20ea2cad43 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 @@ -9,6 +9,24 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], @@ -37,31 +55,13 @@ [-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], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-156.6194, 71.2824] + [-110.8355, 31.9107] ] }, "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: 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, BART, BLAN, BONA, CLBJ, CPER, 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-03-28T00:00:00Z", "end_datetime": "2023-12-08T00:00:00Z", "providers": [ @@ -92,6 +92,24 @@ "gcc_90", "Daily", "P1D", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", "DCFS", "DEJU", "DELA", @@ -120,25 +138,7 @@ "SERC", "SJER", "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "BARR" + "SRER" ], "table:columns": [ { 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 2f75fcb49f..95c1f672fc 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json @@ -9,26 +9,23 @@ "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], [-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], @@ -46,22 +43,25 @@ [-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] ] }, "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: BART, CLBJ, DELA, GRSM, HARV, SCBI, STEI, UKFS, 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, STER, TALL, TEAK, TOOL, TREE, 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2021-09-12T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ @@ -92,26 +92,23 @@ "rcc_90", "Daily", "P1D", - "BART", - "CLBJ", - "DELA", - "GRSM", - "HARV", - "SCBI", - "STEI", - "UKFS", "WOOD", "WREF", "YELL", "ABBY", "BARR", + "BART", "BLAN", "BONA", + "CLBJ", "CPER", "DCFS", "DEJU", + "DELA", "DSNY", + "GRSM", "GUAN", + "HARV", "HEAL", "JERC", "JORN", @@ -129,15 +126,18 @@ "OSBS", "PUUM", "RMNP", + "SCBI", "SERC", "SJER", "SOAP", "SRER", + "STEI", "STER", "TALL", "TEAK", "TOOL", "TREE", + "UKFS", "UNDE" ], "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 068c9c65ca..ffa7141a02 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 @@ -9,21 +9,9 @@ "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], - [-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], + [-149.2133, 63.8758], [-84.4686, 31.1948], [-106.8425, 32.5907], [-96.6129, 39.1104], @@ -49,19 +37,31 @@ [-103.0293, 40.4619], [-87.3933, 32.9505], [-119.006, 37.0058], + [-149.3705, 68.6611], [-89.5857, 45.4937], [-95.1921, 39.0404], - [-145.7514, 63.8811], - [-149.2133, 63.8758], + [-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], - [-149.3705, 68.6611], - [-156.6194, 71.2824] + [-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] ] }, "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: UNDE, WOOD, WREF, YELL, 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, 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": "2024-08-03T00:00:00Z", + "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: 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, GRSM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-07-26T00:00:00Z", "providers": [ @@ -92,21 +92,9 @@ "rcc_90", "Daily", "P1D", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BART", - "BLAN", - "CLBJ", - "CPER", - "DCFS", - "DELA", - "DSNY", - "GRSM", "GUAN", "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -132,13 +120,25 @@ "STER", "TALL", "TEAK", + "TOOL", "TREE", "UKFS", - "DEJU", - "HEAL", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", "BONA", - "TOOL", - "BARR" + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM" ], "table:columns": [ { 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 020061a5b3..98ff52d0ee 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 @@ -9,59 +9,59 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-71.2874, 44.0639], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-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], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-66.8687, 17.9696], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-96.6129, 39.1104], [-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], [-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] + [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-05-29T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ @@ -92,53 +92,53 @@ "rcc_90", "Daily", "P1D", - "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", + "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" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA" ], "table:columns": [ { 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 a3b6fa674a..7726a69f65 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json @@ -9,59 +9,59 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-119.2622, 37.0334], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-78.1395, 38.8929], + [-76.56, 38.8901], [-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], + [-106.8425, 32.5907], + [-96.5631, 39.1008], + [-80.5248, 37.3783], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-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], - [-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] + [-119.7323, 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_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: 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, 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": "2024-08-03T00:00:00Z", + "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: OAES, ONAQ, SCBI, SERC, SRER, STEI, UKFS, WOOD, BART, CLBJ, CPER, DELA, DSNY, GRSM, HARV, JORN, KONZ, MLBS, 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": "2024-08-04T00:00:00Z", "start_datetime": "2021-01-01T00:00:00Z", "end_datetime": "2024-07-26T00:00:00Z", "providers": [ @@ -92,53 +92,53 @@ "rcc_90", "Daily", "P1D", - "SOAP", + "OAES", + "ONAQ", + "SCBI", + "SERC", "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", + "JORN", + "KONZ", + "MLBS", + "ABBY", + "BARR", + "BLAN", + "BONA", + "DCFS", + "DEJU", + "GUAN", "HEAL", "JERC", - "JORN", "KONA", - "KONZ", "LAJA", "LENO", - "MLBS", "MOAB", "NIWO", "NOGP", - "OAES", - "ONAQ", "ORNL", "OSBS", "PUUM", "RMNP", - "SCBI", - "SERC", - "SJER" + "SJER", + "SOAP", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UNDE", + "WREF", + "YELL" ], "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 1df1565230..fbc09352e6 100644 --- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json +++ b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.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": "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-08-24T00:00:00Z", "end_datetime": "2024-07-26T00:00:00Z", "providers": [ @@ -92,13 +92,6 @@ "rcc_90", "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/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json index 4d9e5ded87..a86c91744b 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 @@ -9,15 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -55,13 +46,22 @@ [-105.546, 40.2759], [-78.1395, 38.8929], [-76.56, 38.8901], - [-119.7323, 37.1088] + [-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] ] }, "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: 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, 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-07-26T00:00:00Z", "providers": [ @@ -92,15 +92,6 @@ "rcc_90", "Daily", "P1D", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", "UNDE", "WOOD", "WREF", @@ -138,7 +129,16 @@ "RMNP", "SCBI", "SERC", - "SJER" + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS" ], "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 d6f74a6b95..f97878a1bc 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 +9,23 @@ "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_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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-07-26T00:00:00Z", "providers": [ @@ -92,6 +92,23 @@ "rcc_90", "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/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json index 9b3e27d787..def0860674 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 @@ -61,7 +61,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ 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 d56dd2c80f..da4c7404ac 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 +9,8 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-121.9519, 45.8205], + [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -53,15 +55,13 @@ [-89.5857, 45.4937], [-95.1921, 39.0404], [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-99.2413, 47.1282] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,6 +92,8 @@ "rcc_90", "Daily", "P1D", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -136,9 +138,7 @@ "TREE", "UKFS", "UNDE", - "WOOD", - "WREF", - "YELL" + "WOOD" ], "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 33d94013ab..369664949f 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 @@ -9,9 +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], @@ -55,13 +52,16 @@ [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,9 +92,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", "BLAN", "BONA", "CLBJ", @@ -138,7 +135,10 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART" ], "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 7f51c54c9a..8455478bb6 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 @@ -61,7 +61,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ 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 c8cb31aab5..a7b5c0d282 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 @@ -9,6 +9,15 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], @@ -46,22 +55,13 @@ [-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": "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: 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": "2024-08-03T00:00:00Z", + "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: 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, 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,6 +92,15 @@ "rcc_90", "Daily", "P1D", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -129,16 +138,7 @@ "SOAP", "SRER", "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "STER" ], "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 6a968c1ff3..2171c4f1a0 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 @@ -9,19 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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 +42,26 @@ [-105.546, 40.2759], [-78.1395, 38.8929], [-76.56, 38.8901], - [-119.7323, 37.1088] + [-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_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: 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, 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,19 +92,6 @@ "rcc_90", "Daily", "P1D", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +125,20 @@ "RMNP", "SCBI", "SERC", - "SJER" + "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_tbats.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json index 015e6fab13..1ddde8c6cd 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 +9,17 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], @@ -44,24 +55,13 @@ [-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] + [-83.5019, 35.689] ] }, "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: 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, MOAB.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-08-03T00:00:00Z", + "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: 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, GRSM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-07-26T00:00:00Z", "providers": [ @@ -92,6 +92,17 @@ "rcc_90", "Daily", "P1D", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", "NIWO", "NOGP", "OAES", @@ -127,18 +138,7 @@ "DEJU", "DELA", "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB" + "GRSM" ], "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 220189f037..ec9d565cc9 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 @@ -9,7 +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_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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,7 +92,6 @@ "rcc_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_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 47cd0cd270..7c644945ac 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 @@ -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_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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,15 +92,6 @@ "rcc_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_Red_chromatic_coordinate/models/xgboost_parallel.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/models/xgboost_parallel.json index f6c2734da6..ea91bdf09e 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 @@ -9,6 +9,26 @@ "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], @@ -35,33 +55,13 @@ [-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] + [-99.1066, 47.1617] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-03-28T00:00:00Z", "end_datetime": "2023-12-08T00:00:00Z", "providers": [ @@ -92,6 +92,26 @@ "rcc_90", "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", @@ -118,27 +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" + "DCFS" ], "table:columns": [ { diff --git a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/collection.json b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/collection.json index f28a8d2785..aa40e47002 100644 --- a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/collection.json +++ b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/collection.json @@ -11,37 +11,37 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/BU_SIPNET.json" }, { "rel": "item", "type": "application/json", - "href": "./models/hist30min.json" + "href": "./models/IU_Eco2021.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/UCB_XT.json" }, { "rel": "item", "type": "application/json", - "href": "./models/BU_SIPNET.json" + "href": "./models/VT_NEET.json" }, { "rel": "item", "type": "application/json", - "href": "./models/IU_Eco2021.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/UCB_XT.json" + "href": "./models/hist30min.json" }, { "rel": "item", "type": "application/json", - "href": "./models/VT_NEET.json" + "href": "./models/climatology.json" }, { "rel": "parent", 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 6237b59bb8..dc0817d744 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 16d37bb3d7..859bc071c3 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 943614c63a..876414b379 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 9f40a745bc..7292f8d6c8 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 6f1fcc5ec6..c9bb83ea39 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 @@ -9,6 +9,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-71.2874, 44.0639], + [-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], + [-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], @@ -19,7 +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], @@ -28,40 +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] + [-110.5391, 44.9535] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: BART, CLBJ, KONZ, ORNL, OSBS, SJER, SRER, TALL, UNDE, WREF, 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-06-08T00:00:00Z", "end_datetime": "2023-06-27T00:00:00Z", "providers": [ @@ -92,6 +92,22 @@ "nee", "30min", "PT30M", + "BART", + "CLBJ", + "KONZ", + "ORNL", + "OSBS", + "SJER", + "SRER", + "TALL", + "UNDE", + "WREF", + "ABBY", + "BARR", + "BLAN", + "BONA", + "CPER", + "DCFS", "DEJU", "DELA", "DSNY", @@ -102,7 +118,6 @@ "JERC", "JORN", "KONA", - "KONZ", "LAJA", "LENO", "MLBS", @@ -111,34 +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" + "YELL" ], "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 b3594be01c..df77f8e009 100644 --- a/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json +++ b/catalog/scores/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json @@ -9,20 +9,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-89.5373, 46.2339], - [-121.9519, 45.8205], [-71.2874, 44.0639], - [-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], - [-122.3303, 45.7624], - [-156.6194, 71.2824], [-78.0418, 39.0337], [-147.5026, 65.154], + [-97.57, 33.4012], [-104.7456, 40.8155], [-99.1066, 47.1617], [-145.7514, 63.8811], @@ -35,6 +25,7 @@ [-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], @@ -43,25 +34,34 @@ [-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], - [-110.5391, 44.9535] + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824] ] }, "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: UNDE, WREF, BART, CLBJ, KONZ, ORNL, OSBS, SJER, SRER, TALL, 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-02-01T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ @@ -92,20 +92,10 @@ "nee", "30min", "PT30M", - "UNDE", - "WREF", "BART", - "CLBJ", - "KONZ", - "ORNL", - "OSBS", - "SJER", - "SRER", - "TALL", - "ABBY", - "BARR", "BLAN", "BONA", + "CLBJ", "CPER", "DCFS", "DEJU", @@ -118,6 +108,7 @@ "JERC", "JORN", "KONA", + "KONZ", "LAJA", "LENO", "MLBS", @@ -126,19 +117,28 @@ "NOGP", "OAES", "ONAQ", + "ORNL", + "OSBS", "PUUM", "RMNP", "SCBI", "SERC", + "SJER", "SOAP", + "SRER", "STEI", "STER", + "TALL", "TEAK", "TOOL", "TREE", "UKFS", + "UNDE", "WOOD", - "YELL" + "WREF", + "YELL", + "ABBY", + "BARR" ], "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 8ab1659d8f..e09d628fee 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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..e419f5c0cf 100644 --- a/catalog/scores/Terrestrial/30min_latent_heat_flux/collection.json +++ b/catalog/scores/Terrestrial/30min_latent_heat_flux/collection.json @@ -13,6 +13,11 @@ "type": "application/json", "href": "./models/climatology.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/hist30min.json" + }, { "rel": "item", "type": "application/json", @@ -38,11 +43,6 @@ "type": "application/json", "href": "./models/VT_NEET.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/hist30min.json" - }, { "rel": "parent", "type": "application/json", 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 da0e896317..4d707f066c 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 2fffdd5eb4..c726bc4f21 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 9723217c2c..91d43303a5 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 c12100b53a..2ba6477d15 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 d4ab593e82..2598b356dc 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 @@ -9,15 +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], @@ -55,13 +46,22 @@ [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-06-08T00:00:00Z", "end_datetime": "2023-06-27T00:00:00Z", "providers": [ @@ -92,15 +92,6 @@ "le", "30min", "PT30M", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", "STEI", "STER", "TALL", @@ -138,7 +129,16 @@ "NIWO", "NOGP", "OAES", - "ONAQ" + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER" ], "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 2d4b91d990..96a171d5db 100644 --- a/catalog/scores/Terrestrial/30min_latent_heat_flux/models/climatology.json +++ b/catalog/scores/Terrestrial/30min_latent_heat_flux/models/climatology.json @@ -9,20 +9,38 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-97.57, 33.4012], - [-96.5631, 39.1008], + [-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], - [-71.2874, 44.0639], + [-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], @@ -35,33 +53,15 @@ [-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], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.2622, 37.0334], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-99.2413, 47.1282], - [-110.5391, 44.9535] + [-88.1612, 31.8539] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2022-02-01T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ @@ -92,20 +92,38 @@ "le", "30min", "PT30M", - "CLBJ", - "KONZ", + "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", - "BART", + "YELL", "ABBY", "BARR", + "BART", "BLAN", "BONA", + "CLBJ", "CPER", "DCFS", "DEJU", @@ -118,27 +136,9 @@ "JERC", "JORN", "KONA", + "KONZ", "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SOAP", - "STEI", - "STER", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "WOOD", - "YELL" + "LENO" ], "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 064b4e1dcb..2c27c7fa3b 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 b283120406..d612e07b82 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json @@ -51,17 +51,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.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_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", @@ -71,17 +71,17 @@ { "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_temp_lm.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "parent", 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 31b15c9254..8692934b48 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 932af2173e..eb79ffc038 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: OSBS, 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 9456d2edd3..ae56d60c1e 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 @@ -60,7 +60,7 @@ "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: DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, MLBS, MOAB, NIWO, NOGP, ONAQ, ORNL, OSBS, PUUM, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, OAES, RMNP, 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ 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 3e755b41f6..20cba1c94b 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 +9,15 @@ "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], [-80.5248, 37.3783], @@ -46,22 +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], - [-96.5631, 39.1008] + [-87.8039, 32.5417] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-07-21T00:00:00Z", "providers": [ @@ -92,6 +92,15 @@ "nee", "Daily", "P1D", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", "LAJA", "LENO", "MLBS", @@ -129,16 +138,7 @@ "CPER", "DCFS", "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ" + "DELA" ], "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 22820144a4..e3e4df4aa8 100644 --- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json +++ b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.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], @@ -42,26 +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] + [-110.5391, 44.9535] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-07-21T00:00:00Z", "providers": [ @@ -92,6 +92,19 @@ "nee", "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/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json index 8f9823dcb9..ebe9856e8c 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 @@ -9,6 +9,26 @@ "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], [-87.3933, 32.9505], [-119.006, 37.0058], @@ -35,33 +55,13 @@ [-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], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089] + [-106.8425, 32.5907] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-07-21T00:00:00Z", "providers": [ @@ -92,6 +92,26 @@ "nee", "Daily", "P1D", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", "STER", "TALL", "TEAK", @@ -118,27 +138,7 @@ "HARV", "HEAL", "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI" + "JORN" ], "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 97a0ef0116..088f4d2796 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 @@ -9,6 +9,15 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], @@ -46,22 +55,13 @@ [-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] + [-100.9154, 46.7697] ] }, "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: 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, 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": "2024-08-03T00:00:00Z", + "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: 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, 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-07-21T00:00:00Z", "providers": [ @@ -92,6 +92,15 @@ "nee", "Daily", "P1D", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", "SOAP", "SRER", "STEI", @@ -129,16 +138,7 @@ "MLBS", "MOAB", "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER" + "NOGP" ], "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 7973e4a72d..fbd0e421ce 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 +9,21 @@ "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", - "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,6 +92,21 @@ "nee", "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/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 e75aec19af..9e2ad96ae3 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 @@ -9,11 +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], @@ -55,13 +50,18 @@ [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,11 +92,6 @@ "nee", "Daily", "P1D", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +133,12 @@ "TALL", "TEAK", "TOOL", - "TREE" + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { 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 df4c6511b0..dbff47af23 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 @@ -9,24 +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], [-78.0418, 39.0337], [-147.5026, 65.154], @@ -55,13 +37,31 @@ [-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], + [-122.3303, 45.7624], + [-156.6194, 71.2824] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,24 +92,6 @@ "nee", "Daily", "P1D", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", "BART", "BLAN", "BONA", @@ -138,7 +120,25 @@ "ORNL", "OSBS", "PUUM", - "RMNP" + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR" ], "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 fdc9cf624f..6f4c9e0193 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 @@ -61,7 +61,7 @@ "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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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_randfor.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json index 692efb759f..6a40934292 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 @@ -9,6 +9,24 @@ "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], [-88.1612, 31.8539], @@ -37,31 +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], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104] + [-110.5391, 44.9535] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,6 +92,24 @@ "nee", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", "KONZ", "LAJA", "LENO", @@ -120,25 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA" + "YELL" ], "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 23c38997a3..7528738464 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 +9,26 @@ "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], @@ -35,33 +55,13 @@ [-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] + [-112.4524, 40.1776] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-07-21T00:00:00Z", "providers": [ @@ -92,6 +92,26 @@ "nee", "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", @@ -118,27 +138,7 @@ "NIWO", "NOGP", "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "ONAQ" ], "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 c2ebe75b5a..e913474bfd 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 @@ -61,7 +61,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ 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 e5d993f810..24487ad40a 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 @@ -61,7 +61,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/scores/Terrestrial/Daily_latent_heat_flux/collection.json b/catalog/scores/Terrestrial/Daily_latent_heat_flux/collection.json index 7e4a7a205f..def2964604 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/collection.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/collection.json @@ -31,12 +31,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", @@ -46,12 +46,12 @@ { "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_precip_lm_all_sites.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", 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 6f1d60e6d1..38ee10021e 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: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TEAK, TOOL, UKFS, UNDE, WOOD, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DELA, GRSM, GUAN, HARV, HEAL, JORN, KONA, KONZ, LAJA, MOAB, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, TREE, WREF, JERC, MLBS, NIWO, TALL, DEJU, DSNY, 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 a67843c241..925cefcf39 100644 --- a/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json +++ b/catalog/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json @@ -61,7 +61,7 @@ "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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-07-21T00:00:00Z", "providers": [ 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 4e4ee597f2..31d5a77d3e 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 @@ -9,17 +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], - [-97.57, 33.4012], [-104.7456, 40.8155], [-99.1066, 47.1617], [-145.7514, 63.8811], @@ -55,13 +44,24 @@ [-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], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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, BONA, 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-07-21T00:00:00Z", "providers": [ @@ -92,17 +92,6 @@ "le", "Daily", "P1D", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", "CPER", "DCFS", "DEJU", @@ -138,7 +127,18 @@ "TALL", "TEAK", "TOOL", - "TREE" + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ" ], "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 43d18f7909..881926f06d 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 @@ -61,7 +61,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-07-21T00:00:00Z", "providers": [ 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 22952a1478..9c2cd2faad 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 @@ -61,7 +61,7 @@ "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: 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ 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 23ca1f0a04..89ee41e539 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 @@ -9,15 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -55,13 +46,22 @@ [-106.8425, 32.5907], [-96.6129, 39.1104], [-96.5631, 39.1008], - [-67.0769, 18.0213] + [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,15 +92,6 @@ "le", "Daily", "P1D", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", "PUUM", "RMNP", "SCBI", @@ -138,7 +129,16 @@ "JORN", "KONA", "KONZ", - "LAJA" + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS" ], "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 541758aea4..4dfa093a28 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 @@ -9,15 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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 +46,22 @@ [-119.2622, 37.0334], [-110.8355, 31.9107], [-89.5864, 45.5089], - [-103.0293, 40.4619] + [-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_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: 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, 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,15 +92,6 @@ "le", "Daily", "P1D", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +129,16 @@ "SOAP", "SRER", "STEI", - "STER" + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "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 979827c6a5..160c705eb3 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 @@ -9,26 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-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], @@ -55,13 +35,33 @@ [-78.0418, 39.0337], [-147.5026, 65.154], [-97.57, 33.4012], - [-104.7456, 40.8155] + [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,26 +92,6 @@ "le", "Daily", "P1D", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", "ORNL", "OSBS", "PUUM", @@ -138,7 +118,27 @@ "BLAN", "BONA", "CLBJ", - "CPER" + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ" ], "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 95a612523e..a8fc431fca 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 @@ -9,15 +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], - [-105.5824, 40.0543], - [-100.9154, 46.7697], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], [-99.0588, 35.4106], [-112.4524, 40.1776], [-84.2826, 35.9641], @@ -36,10 +31,6 @@ [-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 +46,22 @@ [-66.8687, 17.9696], [-72.1727, 42.5369], [-149.2133, 63.8758], - [-84.4686, 31.1948] + [-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] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: UNDE, WOOD, WREF, YELL, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, 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.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,15 +92,10 @@ "le", "Daily", "P1D", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", + "UNDE", + "WOOD", + "WREF", + "YELL", "OAES", "ONAQ", "ORNL", @@ -119,10 +114,6 @@ "TOOL", "TREE", "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +129,16 @@ "GUAN", "HARV", "HEAL", - "JERC" + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP" ], "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 e073104890..a5f548e044 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 @@ -9,6 +9,11 @@ "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], @@ -50,18 +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] + [-110.5391, 44.9535] ] }, "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: 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, 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": "2024-08-03T00:00:00Z", + "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": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-07-21T00:00:00Z", "providers": [ @@ -92,6 +92,11 @@ "le", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", "CLBJ", "CPER", "DCFS", @@ -133,12 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA" + "YELL" ], "table:columns": [ { 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 548627bb03..bc8c194cd9 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 +9,29 @@ "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], @@ -32,36 +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], - [-110.5391, 44.9535] + [-105.5824, 40.0543] ] }, "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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,6 +92,29 @@ "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", "BART", @@ -115,30 +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", - "YELL" + "NIWO" ], "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 83d6409696..28ff24fcd5 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 @@ -9,6 +9,15 @@ "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], [-97.57, 33.4012], [-104.7456, 40.8155], @@ -46,22 +55,13 @@ [-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] + [-89.5857, 45.4937] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,6 +92,15 @@ "le", "Daily", "P1D", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", "BONA", "CLBJ", "CPER", @@ -129,16 +138,7 @@ "TALL", "TEAK", "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN" + "TREE" ], "table:columns": [ { diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/collection.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/collection.json index 2bf93aa182..7a0ab3c712 100644 --- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/collection.json +++ b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/collection.json @@ -11,62 +11,62 @@ { "rel": "item", "type": "application/json", - "href": "./models/BU_Dem.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/NJC_ETS_PF.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/NJC_Ticks.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/TickBench.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/Ticks_288.json" + "href": "./models/BU_Dem.json" }, { "rel": "item", "type": "application/json", - "href": "./models/UCLA_2022.json" + "href": "./models/NJC_ETS_PF.json" }, { "rel": "item", "type": "application/json", - "href": "./models/VTicks.json" + "href": "./models/NJC_Ticks.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/TickBench.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/Ticks_288.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/UCLA_2022.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/VTicks.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_ets.json" }, { "rel": "item", 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 6f3f70f170..0678cd8282 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 c64834dd77..80166cb414 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 a571303187..9fa29a4515 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 3733d5d6a1..b5f4cf0eee 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 96fc31512b..75f5ee3aa4 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 6edc1c4faf..fc0328abd3 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 db6d5b33fc..2a40dd121b 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 2fef95e92d..157b813b01 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 @@ -10,20 +10,20 @@ "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], - [-78.0418, 39.0337] + [-95.1921, 39.0404] ] }, "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, KONZ, ORNL, OSBS, 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": "2024-08-03T00:00:00Z", + "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: 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": "2024-08-04T00:00:00Z", "start_datetime": "2021-06-07T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ @@ -55,14 +55,14 @@ "Weekly", "P1W", "LENO", + "BLAN", "KONZ", - "ORNL", "OSBS", "SCBI", + "ORNL", "SERC", "TALL", - "UKFS", - "BLAN" + "UKFS" ], "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 1e03a01625..9dfad2fde5 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": [ - [-88.1612, 31.8539], - [-78.0418, 39.0337], - [-96.5631, 39.1008], [-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], + [-84.2826, 35.9641] ] }, "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: 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": "2024-08-03T00:00:00Z", + "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: OSBS, SCBI, SERC, TALL, UKFS, BLAN, KONZ, LENO, 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": "2024-08-04T00:00:00Z", "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", "OSBS", "SCBI", - "ORNL", "SERC", "TALL", - "UKFS" + "UKFS", + "BLAN", + "KONZ", + "LENO", + "ORNL" ], "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 493cee222b..83d2459d10 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 827cadc060..738904b7da 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 bb69100d0b..0da00df968 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 d182bd11d6..b3e3a91d00 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 ff5b10450b..48c5e8527e 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 84b6ee4335..01e08e2759 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 1b06eb14ca..dbfdc178a8 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 @@ -23,7 +23,7 @@ "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: LENO, OSBS, BLAN, 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2021-06-07T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [ 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 653f0574f0..426494e912 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "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 07581bd29b..700ab8c5a4 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": "2024-08-03T00:00:00Z", + "datetime": "2024-08-04T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2023-12-25T00:00:00Z", "providers": [