diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/collection.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/collection.json index 3302c7e036..cd0b2681b1 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/collection.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/collection.json @@ -8,6 +8,21 @@ ], "type": "Collection", "links": [ + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_tbats.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_temp_lm.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_temp_lm_all_sites.json" + }, { "rel": "item", "type": "application/json", @@ -98,21 +113,6 @@ "type": "application/json", "href": "./models/tg_randfor.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_temp_lm.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" - }, { "rel": "parent", "type": "application/json", diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json index efaa5430fb..a539300b63 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json @@ -17,7 +17,7 @@ "properties": { "title": "USGSHABs1", "description": "All summaries 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, TOMB, FLNT.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-12T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json index a6efe88fa1..fe819a5358 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json @@ -23,7 +23,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries 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, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json index 9ca4ea42b9..6255962930 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json @@ -24,7 +24,7 @@ "properties": { "title": "climatology", "description": "All summaries for the Daily_Chlorophyll_a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BARC, BLWA, FLNT, SUGG, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, USGS-01427510, USGS-01463500, USGS-05543010, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json index c70aac5bcc..26337af7df 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json @@ -24,7 +24,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries 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: LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json index 9f08e6b30c..1c22d0e446 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json @@ -21,7 +21,7 @@ "properties": { "title": "procBlanchardMonod", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json index 4801ab66a8..30c87d6477 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json @@ -21,7 +21,7 @@ "properties": { "title": "procCTMIMonod", "description": "All summaries 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: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json index e892e39c0f..34ca11a804 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json @@ -21,7 +21,7 @@ "properties": { "title": "procEppleyNorbergMonod", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json index 3518342fc2..97099451cf 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json @@ -21,7 +21,7 @@ "properties": { "title": "procEppleyNorbergSteele", "description": "All summaries 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: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json index 5e72278e02..2d440bbe72 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json @@ -21,7 +21,7 @@ "properties": { "title": "procHinshelwoodMonod", "description": "All summaries for the Daily_Chlorophyll_a variable for the procHinshelwoodMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json index d763133795..880aab675f 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json @@ -21,7 +21,7 @@ "properties": { "title": "procHinshelwoodSteele", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json index a9410703b9..984fed5222 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_arima", "description": "All summaries 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: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json index 18985eb3d3..e722c22bb6 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_ets", "description": "All summaries 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: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json index 9721c8d329..22e58d4578 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json index 2204f54970..aac7f5f194 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json index 564159f69f..ce4b6da8cd 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json index c501493a20..08072705dd 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json index 1508e7a48e..aeec490641 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json index d3706d749a..4b8f6e1e39 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json index 00ee983196..7add233463 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json @@ -9,7 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-82.0084, 29.676], [-87.7982, 32.5415], [-89.4737, 46.2097], [-84.4374, 31.1854], @@ -18,13 +17,14 @@ [-99.2531, 47.1298], [-82.0177, 29.6878], [-88.1589, 31.8534], - [-149.6106, 68.6307] + [-149.6106, 68.6307], + [-82.0084, 29.676] ] }, "properties": { "title": "tg_tbats", - "description": "All summaries 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: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ @@ -55,7 +55,6 @@ "chla", "Daily", "P1D", - "BARC", "BLWA", "CRAM", "FLNT", @@ -64,7 +63,8 @@ "PRPO", "SUGG", "TOMB", - "TOOK" + "TOOK", + "BARC" ], "table:columns": [ { diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json index fd5a1a37f8..c65be41545 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries 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: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json index a2cced65de..183aab2333 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json index f336885505..1583085f45 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json @@ -21,72 +21,72 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_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_precip_lm.json" + "href": "./models/tg_randfor.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_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json index b82b55bbec..3fba5385f6 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json @@ -17,7 +17,7 @@ "properties": { "title": "AquaticEcosystemsOxygen", "description": "All summaries 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, WLOU, ARIK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2024-04-03T00:00:00Z", "end_datetime": "2024-08-04T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json index cc7fe8b67d..144f5899f6 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json @@ -21,7 +21,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json index 29c47ed22b..dea3bbfc06 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json @@ -48,7 +48,7 @@ "properties": { "title": "air2waterSat_2", "description": "All summaries 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, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOMB, TOOK, WALK, WLOU, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json index 9ac2616e33..d6c5ec33db 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json @@ -46,7 +46,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries 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: BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json index 439420611b..ab178ecd4b 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json @@ -9,11 +9,8 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], + [-87.7982, 32.5415], + [-147.504, 65.1532], [-105.5442, 40.035], [-66.9868, 18.1135], [-84.4374, 31.1854], @@ -34,10 +31,13 @@ [-119.0274, 36.9559], [-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], [-88.1589, 31.8534], - [-87.7982, 32.5415], [-89.4737, 46.2097], - [-147.504, 65.1532], [-89.7048, 45.9983], [-99.2531, 47.1298], [-99.1139, 47.1591], @@ -47,8 +47,8 @@ }, "properties": { "title": "climatology", - "description": "All summaries 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: ARIK, BARC, BIGC, BLDE, BLUE, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, BLWA, CRAM, CARI, LIRO, PRPO, PRLA, TOOK, OKSR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: BLWA, CARI, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, OKSR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ @@ -79,11 +79,8 @@ "oxygen", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", + "BLWA", + "CARI", "COMO", "CUPE", "FLNT", @@ -104,10 +101,13 @@ "TECR", "WALK", "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", "TOMB", - "BLWA", "CRAM", - "CARI", "LIRO", "PRPO", "PRLA", diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json index 97983773b7..d36daefcdc 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json @@ -12,11 +12,11 @@ [-82.0084, 29.676], [-82.0177, 29.6878], [-96.6038, 39.1051], + [-111.5081, 33.751], [-110.5871, 44.9501], [-119.2575, 37.0597], [-122.1655, 44.2596], [-111.7979, 40.7839], - [-111.5081, 33.751], [-89.4737, 46.2097], [-89.7048, 45.9983], [-97.7823, 33.3785], @@ -27,8 +27,8 @@ }, "properties": { "title": "hotdeck", - "description": "All summaries 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: BARC, SUGG, KING, BLDE, BIGC, MCRA, REDB, SYCA, CRAM, LIRO, PRIN, POSE, MAYF, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: BARC, SUGG, KING, SYCA, BLDE, BIGC, MCRA, REDB, CRAM, LIRO, PRIN, POSE, MAYF, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2024-04-05T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ @@ -62,11 +62,11 @@ "BARC", "SUGG", "KING", + "SYCA", "BLDE", "BIGC", "MCRA", "REDB", - "SYCA", "CRAM", "LIRO", "PRIN", diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json index 881715c06e..4e9ff1d534 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json @@ -48,7 +48,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries 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: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, BLUE, BLWA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json index f1fb87d25e..4ecbd2cead 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_arima", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json index 93cae13f32..09947adecc 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_ets", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json index fb7eb095ce..9697bae93e 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json @@ -9,12 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-111.5081, 33.751], - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], @@ -42,13 +36,19 @@ [-99.1139, 47.1591], [-99.2531, 47.1298], [-111.7979, 40.7839], - [-82.0177, 29.6878] + [-82.0177, 29.6878], + [-111.5081, 33.751], + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -79,12 +79,6 @@ "oxygen", "Daily", "P1D", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -112,7 +106,13 @@ "PRLA", "PRPO", "REDB", - "SUGG" + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json index 8b0850fba9..6380324ffe 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json @@ -9,6 +9,16 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], @@ -32,23 +42,13 @@ [-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] + [-96.443, 38.9459] ] }, "properties": { "title": "tg_humidity_lm_all_sites", - "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,6 +79,16 @@ "oxygen", "Daily", "P1D", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", "TOMB", "TOOK", "WALK", @@ -102,17 +112,7 @@ "LIRO", "MART", "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR" + "MCDI" ], "table:columns": [ { diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json index 7578e179ee..9e0480e4bd 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json @@ -9,6 +9,15 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-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], @@ -33,22 +42,13 @@ [-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] + [-96.443, 38.9459] ] }, "properties": { "title": "tg_lasso", - "description": "All summaries 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: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -79,6 +79,15 @@ "oxygen", "Daily", "P1D", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", "TECR", "TOMB", "TOOK", @@ -103,16 +112,7 @@ "LIRO", "MART", "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA" + "MCDI" ], "table:columns": [ { diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json index 581d3238e4..ff6a74c50a 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json index 3c8f38e841..07f1656a08 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json @@ -9,10 +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], @@ -42,13 +38,17 @@ [-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] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ @@ -79,10 +79,6 @@ "oxygen", "Daily", "P1D", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -112,7 +108,11 @@ "REDB", "SUGG", "SYCA", - "TECR" + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json index 59bf7543d2..07fcb4ffdb 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json @@ -9,11 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], @@ -42,13 +37,18 @@ [-99.2531, 47.1298], [-111.7979, 40.7839], [-82.0177, 29.6878], - [-111.5081, 33.751] + [-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 summaries 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: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ @@ -79,11 +79,6 @@ "oxygen", "Daily", "P1D", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -112,7 +107,12 @@ "PRPO", "REDB", "SUGG", - "SYCA" + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json index 2e11c99d7b..27a4ba237a 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries 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: FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json index d3b86852f1..9d5cc89fa1 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json index 65c35689ee..3159165fcd 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json b/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json index e460c29dd0..70a1e4dfa6 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json @@ -8,6 +8,16 @@ ], "type": "Collection", "links": [ + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_arima.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_ets.json" + }, { "rel": "item", "type": "application/json", @@ -51,47 +61,47 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_humidity_lm.json" }, { "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_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", @@ -106,43 +116,33 @@ { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/GLEON_JRabaey_temp_physics.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fARIMA_clim_ensemble.json" + "href": "./models/air2waterSat_2.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_JRabaey_temp_physics.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/fARIMA_clim_ensemble.json" }, { "rel": "item", "type": "application/json", "href": "./models/GLEON_physics.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/air2waterSat_2.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "./models/baseline_ensemble.json" - }, { "rel": "item", "type": "application/json", @@ -151,17 +151,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/lm_AT_WTL_WS.json" + "href": "./models/GAM_air_wind.json" }, { "rel": "item", "type": "application/json", - "href": "./models/mlp1_wtempforecast_LF.json" + "href": "./models/TSLM_seasonal_JM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/zimmerman_proj1.json" + "href": "./models/bee_bake_RFModel_2024.json" }, { "rel": "item", @@ -171,22 +171,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/GAM_air_wind.json" + "href": "./models/lm_AT_WTL_WS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/TSLM_seasonal_JM.json" + "href": "./models/mkricheldorf_w_lag.json" }, { "rel": "item", "type": "application/json", - "href": "./models/bee_bake_RFModel_2024.json" + "href": "./models/mlp1_wtempforecast_LF.json" }, { "rel": "item", "type": "application/json", - "href": "./models/mkricheldorf_w_lag.json" + "href": "./models/zimmerman_proj1.json" }, { "rel": "item", diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json index 2051191524..f8af577ed8 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json @@ -21,7 +21,7 @@ "properties": { "title": "GAM_air_wind", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json index 633dfafe1e..1ef6aced5e 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json @@ -48,7 +48,7 @@ "properties": { "title": "GLEON_JRabaey_temp_physics", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-12T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json index 17a6ab2eb1..f988838571 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json @@ -21,7 +21,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All summaries 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: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json index b273cbdaf1..686eaf95c3 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json @@ -20,7 +20,7 @@ "properties": { "title": "GLEON_physics", "description": "All summaries 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.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2023-12-22T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json index 280a2fa32a..88d2e958d5 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json @@ -21,7 +21,7 @@ "properties": { "title": "TSLM_seasonal_JM", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", "end_datetime": "2024-06-02T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json index 4757576c1b..10a00a9d26 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json @@ -21,7 +21,7 @@ "properties": { "title": "acp_fableLM", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2024-03-11T00:00:00Z", "end_datetime": "2024-04-13T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json index 98326424d5..a968096082 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json @@ -9,7 +9,9 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-77.9832, 39.0956], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914], [-89.7048, 45.9983], [-121.9338, 45.7908], [-87.4077, 32.9604], @@ -40,15 +42,13 @@ [-72.3295, 42.4719], [-96.6038, 39.1051], [-83.5038, 35.6904], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914] + [-77.9832, 39.0956] ] }, "properties": { "title": "air2waterSat_2", - "description": "All summaries 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: LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: TOOK, WALK, WLOU, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -79,7 +79,9 @@ "temperature", "Daily", "P1D", - "LEWI", + "TOOK", + "WALK", + "WLOU", "LIRO", "MART", "MAYF", @@ -110,9 +112,7 @@ "HOPB", "KING", "LECO", - "TOOK", - "WALK", - "WLOU" + "LEWI" ], "table:columns": [ { diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json index 6608b9c03f..6d91fc1431 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json @@ -9,9 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-78.1473, 38.8943], [-87.7982, 32.5415], [-105.5442, 40.035], [-66.9868, 18.1135], @@ -29,6 +26,9 @@ [-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], [-102.4471, 39.7582], @@ -47,8 +47,8 @@ }, "properties": { "title": "baseline_ensemble", - "description": "All summaries 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: MCDI, MCRA, POSE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, KING, LECO, LEWI, MART, MAYF, PRIN, REDB, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: BLWA, COMO, CUPE, FLNT, GUIL, HOPB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -79,9 +79,6 @@ "temperature", "Daily", "P1D", - "MCDI", - "MCRA", - "POSE", "BLWA", "COMO", "CUPE", @@ -99,6 +96,9 @@ "LEWI", "MART", "MAYF", + "MCDI", + "MCRA", + "POSE", "PRIN", "REDB", "ARIK", diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json index 9dc8c516f6..51b85800c4 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json @@ -9,19 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-99.1139, 47.1591], - [-99.2531, 47.1298], [-89.7048, 45.9983], - [-82.0084, 29.676], + [-99.2531, 47.1298], [-89.4737, 46.2097], + [-99.1139, 47.1591], + [-82.0084, 29.676], [-82.0177, 29.6878], [-149.6106, 68.6307] ] }, "properties": { "title": "bee_bake_RFModel_2024", - "description": "All summaries 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: PRLA, PRPO, LIRO, BARC, CRAM, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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, PRPO, CRAM, PRLA, BARC, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", "end_datetime": "2024-09-24T00:00:00Z", "providers": [ @@ -52,11 +52,11 @@ "temperature", "Daily", "P1D", - "PRLA", - "PRPO", "LIRO", - "BARC", + "PRPO", "CRAM", + "PRLA", + "BARC", "SUGG", "TOOK" ], diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json index 0417788c41..a711dfbf4c 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json @@ -46,7 +46,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries 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: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, TECR, TOMB, WALK, WLOU, SYCA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/climatology.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/climatology.json index 95473f537a..e1ea8b03be 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/climatology.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/climatology.json @@ -48,7 +48,7 @@ "properties": { "title": "climatology", "description": "All summaries 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: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, LIRO, PRPO, CRAM, PRLA, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json index ff3c6742c7..6a23a81323 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json @@ -34,8 +34,8 @@ [-88.1589, 31.8534], [-119.2575, 37.0597], [-110.5871, 44.9501], - [-89.4737, 46.2097], [-84.4374, 31.1854], + [-89.4737, 46.2097], [-111.5081, 33.751], [-89.7048, 45.9983], [-99.1139, 47.1591], @@ -47,8 +47,8 @@ }, "properties": { "title": "fARIMA_clim_ensemble", - "description": "All summaries 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: LECO, LEWI, MART, MAYF, MCDI, MCRA, COMO, CUPE, GUIL, HOPB, KING, ARIK, BARC, BLUE, BLWA, WALK, WLOU, POSE, PRIN, REDB, SUGG, TECR, TOMB, BIGC, BLDE, CRAM, FLNT, SYCA, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: LECO, LEWI, MART, MAYF, MCDI, MCRA, COMO, CUPE, GUIL, HOPB, KING, ARIK, BARC, BLUE, BLWA, WALK, WLOU, POSE, PRIN, REDB, SUGG, TECR, TOMB, BIGC, BLDE, FLNT, CRAM, SYCA, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-10T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -104,8 +104,8 @@ "TOMB", "BIGC", "BLDE", - "CRAM", "FLNT", + "CRAM", "SYCA", "LIRO", "PRLA", diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json index 64b7b0c172..c3f6fd7e92 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json @@ -48,7 +48,7 @@ "properties": { "title": "fTSLM_lag", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-08T00:00:00Z", "end_datetime": "2024-09-14T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json index de24ffbbff..a7b1bd0228 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json @@ -21,7 +21,7 @@ "properties": { "title": "flareGLM", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json index e1be7ba253..f4dd99007b 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json @@ -21,7 +21,7 @@ "properties": { "title": "flareGLM_noDA", "description": "All summaries 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: TOOK, BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-03-02T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json index 0d2e820252..625032bc6c 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json @@ -21,7 +21,7 @@ "properties": { "title": "flareGOTM_noDA", "description": "All summaries 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: BARC, CRAM, SUGG, LIRO, PRLA, PRPO, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-20T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json index 77b5fc5abd..7c4e899d43 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json @@ -20,7 +20,7 @@ "properties": { "title": "flareSimstrat_noDA", "description": "All summaries 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: BARC, SUGG, TOOK, CRAM, PRLA, PRPO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json index a898c9d282..010fec8b01 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json @@ -20,7 +20,7 @@ "properties": { "title": "flare_ler", "description": "All summaries 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, CRAM, LIRO, PRLA, PRPO, BARC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json index 9da125ab6b..355a90636a 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json @@ -16,7 +16,7 @@ "properties": { "title": "flare_ler_baselines", "description": "All summaries 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.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json index 2a146c6843..51aef5a1b8 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json @@ -44,7 +44,7 @@ "properties": { "title": "hotdeck", "description": "All summaries for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, TOMB, BLWA, FLNT, MCRA, KING, SYCA, POSE, PRIN, MAYF, LEWI, LECO, ARIK, HOPB, REDB, TECR, BLDE, COMO, WLOU, CRAM, CARI, BIGC, BLUE, CUPE, GUIL, WALK, LIRO, PRLA, PRPO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json index b1e98f7b18..93fa62cf34 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json @@ -21,7 +21,7 @@ "properties": { "title": "lm_AT_WTL_WS", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json index b6ecfdec9f..ab59ee2536 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json @@ -21,7 +21,7 @@ "properties": { "title": "mkricheldorf_w_lag", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2024-03-06T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json index 7edf16f106..b41356aa93 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json @@ -21,7 +21,7 @@ "properties": { "title": "mlp1_wtempforecast_LF", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-24T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json index 2268197e03..6a7ad0d6fd 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json @@ -48,7 +48,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries 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: KING, LECO, LEWI, LIRO, MART, MAYF, ARIK, BARC, BIGC, BLDE, BLUE, MCDI, MCRA, OKSR, POSE, PRIN, WLOU, CUPE, FLNT, GUIL, HOPB, PRLA, PRPO, REDB, SUGG, SYCA, BLWA, CARI, COMO, CRAM, TECR, TOMB, TOOK, WALK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json index c9930e1753..281c970c6e 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json @@ -21,7 +21,7 @@ "properties": { "title": "precip_mod", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-12-21T00:00:00Z", "end_datetime": "2024-01-24T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json index 0f0640e469..29e68c0a7a 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json @@ -9,6 +9,22 @@ "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], @@ -26,29 +42,13 @@ [-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] + [-121.9338, 45.7908] ] }, "properties": { "title": "tg_arima", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ @@ -79,6 +79,22 @@ "temperature", "Daily", "P1D", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -96,23 +112,7 @@ "LECO", "LEWI", "LIRO", - "MART", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "MART" ], "table:columns": [ { diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json index 77fdf2e833..aa4544c425 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json @@ -9,6 +9,19 @@ "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], @@ -29,26 +42,13 @@ [-88.1589, 31.8534], [-149.6106, 68.6307], [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582], - [-82.0084, 29.676], - [-119.2575, 37.0597], - [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-87.7982, 32.5415], - [-147.504, 65.1532], - [-105.5442, 40.035], - [-89.4737, 46.2097], - [-66.9868, 18.1135], - [-84.4374, 31.1854], - [-66.7987, 18.1741], - [-72.3295, 42.4719] + [-105.9154, 39.8914] ] }, "properties": { "title": "tg_ets", - "description": "All summaries 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: KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ @@ -79,6 +79,19 @@ "temperature", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", "KING", "LECO", "LEWI", @@ -99,20 +112,7 @@ "TOMB", "TOOK", "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB" + "WLOU" ], "table:columns": [ { diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json index d970474779..b330b8a340 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries 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: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json index 00ae577739..cd17da1749 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json index 249af7a686..8a2b00f34b 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Daily_Water_temperature variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json index c3deab3b31..6c987837b6 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries 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: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json index 30a94d06b0..8ac1b30101 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json index 7533dd04de..087cca73c9 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json index ce9546a34f..f8a8a8bebb 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json index 3a9b62c51b..7f570d03f0 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json index 6551cbc8d0..a11b82fbdc 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json @@ -48,7 +48,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json index 1b809925b2..58493f2cd0 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json @@ -21,7 +21,7 @@ "properties": { "title": "zimmerman_proj1", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json index 82ef8b026f..d5caaad663 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json @@ -8,6 +8,11 @@ ], "type": "Collection", "links": [ + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_arima.json" + }, { "rel": "item", "type": "application/json", @@ -43,11 +48,6 @@ "type": "application/json", "href": "./models/tg_temp_lm_all_sites.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_arima.json" - }, { "rel": "item", "type": "application/json", diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json index 35a8effd8a..d9b79af33f 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_arima", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json index d9f0f094e7..b36d0c3812 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json @@ -9,6 +9,20 @@ "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], @@ -41,27 +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] + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_ets", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -92,6 +92,20 @@ "abundance", "Weekly", "P1W", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", "HEAL", "JERC", "JORN", @@ -124,21 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV" + "YELL" ], "table:columns": [ { diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json index 362b752b6a..2992cf136c 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json index 7e23f817ae..5e786dc0cf 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/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 summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json index 284fe3f569..9bf476e64e 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json index 3468345d9a..bb787214d9 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json index 02b2257a5d..905c2d984c 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/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 summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json index c182cb255d..0c6036d96b 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json index 1ebbd44a4c..6b5c6ea341 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json index 3b16e5af51..87ade3ef13 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json index 3691ff6b1a..7d72f72b79 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/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 summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json index e3ef7c16fa..8390ac615a 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_arima", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json index 7b38956c87..92fd1e71fc 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_ets", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json index ce7e6d3a1c..6dc4963c63 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json index bf0e5fd85c..2144b6266e 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/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 summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json index e75006d8b7..6a2316b157 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json index b47d8951b1..52f25e01b0 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json index 9c5a517723..755f11f9db 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/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 summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json index 64a1528811..efde860c26 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries 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, 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json index bfb6c2dfd2..abf695ff58 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json index 1daae22345..99f1c881cf 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json index e5f0e928c1..c88035866b 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json index ba422a36f5..097eaeb50b 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json @@ -16,7 +16,7 @@ "properties": { "title": "ChlorophyllCrusaders", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-20T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json index 2fa779d4bb..0b6088a020 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json @@ -61,7 +61,7 @@ "properties": { "title": "PEG", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json index 0ee588e496..c3c017ffc2 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json @@ -9,21 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -55,13 +40,28 @@ [-81.9934, 29.6893], [-155.3173, 19.5531], [-105.546, 40.2759], - [-78.1395, 38.8929] + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535] ] }, "properties": { "title": "cb_prophet", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ @@ -92,21 +92,6 @@ "gcc_90", "Daily", "P1D", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -138,7 +123,22 @@ "OSBS", "PUUM", "RMNP", - "SCBI" + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json index a920e8d87c..2a670b4461 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json @@ -54,14 +54,14 @@ [-147.5026, 65.154], [-145.7514, 63.8811], [-149.2133, 63.8758], - [-156.6194, 71.2824], - [-149.3705, 68.6611] + [-149.3705, 68.6611], + [-156.6194, 71.2824] ] }, "properties": { "title": "climatology", - "description": "All summaries 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: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -137,8 +137,8 @@ "BONA", "DEJU", "HEAL", - "BARR", - "TOOL" + "TOOL", + "BARR" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json index 2f4c53af1c..f0e91e58f3 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json @@ -20,38 +20,38 @@ [-104.7456, 40.8155], [-99.1066, 47.1617], [-145.7514, 63.8811], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], [-119.7323, 37.1088], + [-67.0769, 18.0213], + [-88.1612, 31.8539], + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-100.9154, 46.7697], [-119.2622, 37.0334], [-110.8355, 31.9107], [-89.5864, 45.5089], [-103.0293, 40.4619], [-87.3933, 32.9505], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], [-119.006, 37.0058], [-149.3705, 68.6611], [-89.5857, 45.4937], [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-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], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], [-84.4686, 31.1948], [-106.8425, 32.5907], [-96.6129, 39.1104], @@ -60,8 +60,8 @@ }, "properties": { "title": "persistenceRW", - "description": "All summaries 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: DELA, DSNY, GRSM, GUAN, HARV, HEAL, BONA, CLBJ, CPER, DCFS, DEJU, SJER, SOAP, SRER, STEI, STER, TALL, NOGP, OAES, ONAQ, ORNL, OSBS, TEAK, TOOL, TREE, UKFS, UNDE, LAJA, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, JERC, JORN, KONA, KONZ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: DELA, DSNY, GRSM, GUAN, HARV, HEAL, BONA, CLBJ, CPER, DCFS, DEJU, OSBS, PUUM, RMNP, SCBI, SERC, SJER, LAJA, LENO, MLBS, MOAB, NIWO, UNDE, WOOD, WREF, YELL, NOGP, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, ABBY, BARR, BART, BLAN, OAES, ONAQ, ORNL, JERC, JORN, KONA, KONZ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -103,38 +103,38 @@ "CPER", "DCFS", "DEJU", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", "SJER", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "UNDE", + "WOOD", + "WREF", + "YELL", + "NOGP", "SOAP", "SRER", "STEI", "STER", "TALL", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", "TEAK", "TOOL", "TREE", "UKFS", - "UNDE", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", "BLAN", + "OAES", + "ONAQ", + "ORNL", "JERC", "JORN", "KONA", diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json index d377ec0d0d..2ff467a7e6 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_arima", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json index f1b1645e89..57ff401954 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_ets", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json index 56f9dceff2..ce88f71cc5 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index ea00af4076..b8061c85a6 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json index 2a80369ee5..e31e4905f2 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json index e18caf6c8e..3ef7df50d5 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json index 3f5328671c..fc453e6f6b 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json index 21b49e88ca..334b9c2658 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json index 24801e21b6..e64512d6a3 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json @@ -9,19 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], [-121.9519, 45.8205], [-110.5391, 44.9535], [-122.3303, 45.7624], @@ -55,13 +42,26 @@ [-81.9934, 29.6893], [-155.3173, 19.5531], [-105.546, 40.2759], - [-78.1395, 38.8929] + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282] ] }, "properties": { "title": "tg_tbats", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -92,19 +92,6 @@ "gcc_90", "Daily", "P1D", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", "WREF", "YELL", "ABBY", @@ -138,7 +125,20 @@ "OSBS", "PUUM", "RMNP", - "SCBI" + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json index 0994fd8473..376501f8f5 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json @@ -9,6 +9,22 @@ "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], @@ -39,29 +55,13 @@ [-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] + [-105.546, 40.2759] ] }, "properties": { "title": "tg_temp_lm", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,6 +92,22 @@ "gcc_90", "Daily", "P1D", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -122,23 +138,7 @@ "ORNL", "OSBS", "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "RMNP" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json index 0b3640c229..3f85896d1a 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json index 6fb785b38c..bf823cc614 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json @@ -11,7 +11,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/tg_tbats.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_temp_lm.json" }, { "rel": "item", @@ -26,22 +31,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/baseline_ensemble.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_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", @@ -61,27 +66,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/climatology.json" }, { "rel": "item", diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json index dd56d69220..75702d9a41 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json @@ -61,7 +61,7 @@ "properties": { "title": "PEG", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json index 01d5944ae4..4577c680e2 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json @@ -51,17 +51,17 @@ [-109.3883, 38.2483], [-105.5824, 40.0543], [-100.9154, 46.7697], - [-156.6194, 71.2824], [-147.5026, 65.154], [-145.7514, 63.8811], [-149.2133, 63.8758], - [-149.3705, 68.6611] + [-149.3705, 68.6611], + [-156.6194, 71.2824] ] }, "properties": { "title": "baseline_ensemble", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BARR, BONA, DEJU, HEAL, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -134,11 +134,11 @@ "MOAB", "NIWO", "NOGP", - "BARR", "BONA", "DEJU", "HEAL", - "TOOL" + "TOOL", + "BARR" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json index 40955da4ae..3d8c2e00d6 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json @@ -9,6 +9,23 @@ "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], @@ -38,30 +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] + [-110.5391, 44.9535] ] }, "properties": { "title": "cb_prophet", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ @@ -92,6 +92,23 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", "KONA", "KONZ", "LAJA", @@ -121,24 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN" + "YELL" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json index 2abefc65ad..11334872a0 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json @@ -54,14 +54,14 @@ [-145.7514, 63.8811], [-149.2133, 63.8758], [-147.5026, 65.154], - [-156.6194, 71.2824], - [-149.3705, 68.6611] + [-149.3705, 68.6611], + [-156.6194, 71.2824] ] }, "properties": { "title": "climatology", - "description": "All summaries 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: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -137,8 +137,8 @@ "DEJU", "HEAL", "BONA", - "BARR", - "TOOL" + "TOOL", + "BARR" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json index eca383caf3..066fa6ec5a 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json @@ -44,24 +44,24 @@ [-99.0588, 35.4106], [-84.4686, 31.1948], [-106.8425, 32.5907], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], [-149.3705, 68.6611], [-89.5857, 45.4937], [-95.1921, 39.0404], [-89.5373, 46.2339], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758] + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697] ] }, "properties": { "title": "persistenceRW", - "description": "All summaries 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: KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, ONAQ, ORNL, OSBS, PUUM, SRER, STEI, STER, TALL, TEAK, RMNP, SCBI, SERC, SJER, SOAP, OAES, JERC, JORN, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, ONAQ, ORNL, OSBS, PUUM, SRER, STEI, STER, TALL, TEAK, RMNP, SCBI, SERC, SJER, SOAP, OAES, JERC, JORN, GRSM, GUAN, HARV, HEAL, TOOL, TREE, UKFS, UNDE, MLBS, MOAB, NIWO, NOGP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -127,18 +127,18 @@ "OAES", "JERC", "JORN", - "MLBS", - "MOAB", - "NIWO", - "NOGP", + "GRSM", + "GUAN", + "HARV", + "HEAL", "TOOL", "TREE", "UKFS", "UNDE", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "MLBS", + "MOAB", + "NIWO", + "NOGP" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json index ae3fb6967c..148b3e167e 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json @@ -9,6 +9,26 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-67.0769, 18.0213], + [-88.1612, 31.8539], + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], [-155.3173, 19.5531], [-105.546, 40.2759], [-78.1395, 38.8929], @@ -35,33 +55,13 @@ [-97.57, 33.4012], [-104.7456, 40.8155], [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893] + [-145.7514, 63.8811] ] }, "properties": { "title": "tg_arima", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -92,6 +92,26 @@ "rcc_90", "Daily", "P1D", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", "PUUM", "RMNP", "SCBI", @@ -118,27 +138,7 @@ "CLBJ", "CPER", "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS" + "DEJU" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json index 2439b5a440..591a1085e6 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_ets", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json index 77d5765341..34d9c1ef62 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 763fb3071b..2776420ab2 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json index 0e53ebcd79..905ba6c81d 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json index 275da5fc08..5f4d404b90 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_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_precip_lm", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,6 +92,21 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -123,22 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "YELL" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json index 579e3e090e..5eccb2ac83 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -9,18 +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], @@ -55,13 +43,25 @@ [-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] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,18 +92,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", "GUAN", "HARV", "HEAL", @@ -138,7 +126,19 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json index 7606dcb41d..94e93c4747 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json @@ -9,6 +9,22 @@ "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], @@ -39,29 +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] + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_randfor", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,6 +92,22 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -122,23 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json index e6df99b3c4..7ce0bf4212 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json @@ -9,6 +9,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -51,17 +55,13 @@ [-119.006, 37.0058], [-149.3705, 68.6611], [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-95.1921, 39.0404] ] }, "properties": { "title": "tg_tbats", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -92,6 +92,10 @@ "rcc_90", "Daily", "P1D", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -134,11 +138,7 @@ "TEAK", "TOOL", "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "UKFS" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json index fd7b43049f..052b8e0240 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json @@ -9,21 +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], @@ -55,13 +40,28 @@ [-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] ] }, "properties": { "title": "tg_temp_lm", - "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,21 +92,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", "JERC", "JORN", "KONA", @@ -138,7 +123,22 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL" ], "table:columns": [ { diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json index 540a46d9b7..3bc7b32e2b 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json b/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json index 1f9db7519d..2aae91f81e 100644 --- a/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json +++ b/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json @@ -61,7 +61,7 @@ "properties": { "title": "climatology", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json b/catalog/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json index ef860f4805..2f2678d2fc 100644 --- a/catalog/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json +++ b/catalog/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json @@ -61,7 +61,7 @@ "properties": { "title": "climatology", "description": "All summaries 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: 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, SCBI, RMNP, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json index d1a44feffd..649e7adb1c 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json @@ -21,57 +21,57 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_tbats.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_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_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.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", diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json index 13b1f1ea74..06987d61c6 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json @@ -15,7 +15,7 @@ "properties": { "title": "USUNEEDAILY", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-12-12T00:00:00Z", "end_datetime": "2024-01-16T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json index 75d0e339e3..52ed53b624 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json @@ -16,7 +16,7 @@ "properties": { "title": "bookcast_forest", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the bookcast_forest model. Information for the model is provided as follows: A simple daily timestep process-based model of a terrestrial carbon cycle. It includes leaves, wood, and soil pools. It uses a light-use efficiency GPP model to convert PAR to carbon. The model is derived from https://github.com/mdietze/FluxCourseForecast..\n The model predicts this variable at the following sites: TALL, OSBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2024-01-10T00:00:00Z", "end_datetime": "2024-07-12T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json index 09d5dd73f1..9e11174663 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json @@ -9,49 +9,49 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-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], - [-122.3303, 45.7624], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], [-89.5373, 46.2339], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-147.5026, 65.154], [-97.57, 33.4012], [-104.7456, 40.8155], [-99.1066, 47.1617], [-145.7514, 63.8811], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], + [-87.8039, 32.5417], + [-149.2133, 63.8758], [-84.4686, 31.1948], [-106.8425, 32.5907], [-96.6129, 39.1104], [-96.5631, 39.1008], + [-67.0769, 18.0213], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-88.1612, 31.8539], + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], [-155.3173, 19.5531], [-105.546, 40.2759], [-78.1395, 38.8929], @@ -60,7 +60,7 @@ }, "properties": { "title": "persistenceRW", - "description": "All summaries 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: BARR, BART, BLAN, BONA, SJER, SOAP, SRER, STEI, STER, TALL, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, ABBY, TEAK, TOOL, TREE, UKFS, UNDE, DELA, DSNY, GRSM, GUAN, HARV, HEAL, CLBJ, CPER, DCFS, DEJU, WOOD, WREF, YELL, JERC, JORN, KONA, KONZ, PUUM, RMNP, SCBI, SERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries 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: NOGP, OAES, ONAQ, ORNL, OSBS, UNDE, WOOD, WREF, YELL, BONA, CLBJ, CPER, DCFS, DEJU, DELA, HEAL, JERC, JORN, KONA, KONZ, LAJA, SJER, SOAP, SRER, STEI, STER, TALL, DSNY, GRSM, GUAN, HARV, TEAK, TOOL, TREE, UKFS, ABBY, BARR, BART, BLAN, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", @@ -92,49 +92,49 @@ "nee", "Daily", "P1D", - "BARR", - "BART", - "BLAN", - "BONA", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", "NOGP", "OAES", "ONAQ", "ORNL", "OSBS", - "ABBY", - "TEAK", - "TOOL", - "TREE", - "UKFS", "UNDE", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", + "WOOD", + "WREF", + "YELL", + "BONA", "CLBJ", "CPER", "DCFS", "DEJU", - "WOOD", - "WREF", - "YELL", + "DELA", + "HEAL", "JERC", "JORN", "KONA", "KONZ", + "LAJA", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "ABBY", + "BARR", + "BART", + "BLAN", + "LENO", + "MLBS", + "MOAB", + "NIWO", "PUUM", "RMNP", "SCBI", diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json index 9fa3b2dd7e..59a4e73b39 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_arima", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json index ea9bb39136..ed0a5257e3 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_ets", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "datetime": "2024-10-23T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json index a225b8b957..5ea20d9458 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json index 218a743d8d..54e51c88a4 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json @@ -9,19 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], [-119.2622, 37.0334], [-110.8355, 31.9107], [-89.5864, 45.5089], @@ -55,13 +42,26 @@ [-96.6129, 39.1104], [-96.5631, 39.1008], [-67.0769, 18.0213], - [-88.1612, 31.8539] + [-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_humidity_lm_all_sites", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-22T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,19 +92,6 @@ "nee", "Daily", "P1D", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", "SOAP", "SRER", "STEI", @@ -138,7 +125,20 @@ "KONA", "KONZ", "LAJA", - "LENO" + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER" ], "table:columns": [ { diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json index 376139ff8e..562b70c288 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json index ed792ab2c2..1b1f74f0ed 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/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 summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json index e70f803701..500db6c5ee 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json index be1433be76..8ca41b6866 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json @@ -9,12 +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], @@ -55,13 +49,19 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012] ] }, "properties": { "title": "tg_tbats", - "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -92,12 +92,6 @@ "nee", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", "CPER", "DCFS", "DEJU", @@ -138,7 +132,13 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ" ], "table:columns": [ { diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json index 9e5ba74460..6950b1e289 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json index ee0984471c..41ea3b833b 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/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 summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json index 65b681a206..31e6d4caf8 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json @@ -13,11 +13,6 @@ "type": "application/json", "href": "./models/tg_arima.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_ets.json" - }, { "rel": "item", "type": "application/json", @@ -46,27 +41,32 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_humidity_lm_all_sites.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_precip_lm.json" }, { "rel": "parent", diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json index 4953895e26..131b2f2b83 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json @@ -60,7 +60,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries 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: DSNY, SCBI, MOAB, PUUM, GUAN, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, OSBS, BONA, ONAQ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json index 93e8538e95..20fca3a3ad 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json @@ -61,7 +61,7 @@ "properties": { "title": "climatology", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json index 115e233a68..40229093fb 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json @@ -9,6 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-67.0769, 18.0213], + [-88.1612, 31.8539], + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], [-119.2622, 37.0334], [-110.8355, 31.9107], [-89.5864, 45.5089], @@ -40,28 +55,13 @@ [-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] + [-96.5631, 39.1008] ] }, "properties": { "title": "tg_arima", - "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -92,6 +92,21 @@ "le", "Daily", "P1D", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", "SOAP", "SRER", "STEI", @@ -123,22 +138,7 @@ "JERC", "JORN", "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER" + "KONZ" ], "table:columns": [ { diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json index 16df490c17..cf74837920 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json index 8a321ef033..0e4a2c60cc 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json index 3ea70563b7..88bbd6dc57 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json index edc75997ae..de8371c2e5 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json index 211f1d45d7..60bd4f4198 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json index 7d7ef0a691..8c8f31855e 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json index fc36c19402..a27ee7d095 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries 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: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json index 6ba83129be..283a131677 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries 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: 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json index db84823a9f..74da8514ec 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json index 53d5ef3cc3..dbda92c42a 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_arima", "description": "All summaries 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: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json index 60aacca24f..e5b6803236 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_ets", "description": "All summaries 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: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json index 89c138f7e4..67c46e95c1 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json index 24fac642cb..71fb330d37 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/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 summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json index 397e8b99bf..2df19c2201 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json @@ -22,7 +22,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json index 6c27696847..df694f454c 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json index 8fc511a53d..711186d140 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/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 summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json index 3a671e83fc..f5aca0383b 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json @@ -22,7 +22,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json index d4d53ad233..103b06d3e4 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries 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: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json index 74c2e0605d..532485c364 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json index 4e9585bcc2..e849ce2c21 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/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 summaries 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 Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-10-23T00:00:00Z", + "datetime": "2024-10-24T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [