Automated commit: Update data folder

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/collection.json

@@ -76,42 +76,42 @@
         {
             "rel": "item",
             "type": "application/json",
-            "href": "./models/tg_humidity_lm_all_sites.json"
+            "href": "./models/tg_tbats.json"
         },
         {
             "rel": "item",
             "type": "application/json",
-            "href": "./models/tg_lasso.json"
+            "href": "./models/tg_temp_lm.json"
         },
         {
             "rel": "item",
             "type": "application/json",
-            "href": "./models/tg_precip_lm.json"
+            "href": "./models/tg_temp_lm_all_sites.json"
         },
         {
             "rel": "item",
             "type": "application/json",
-            "href": "./models/tg_precip_lm_all_sites.json"
+            "href": "./models/tg_humidity_lm_all_sites.json"
         },
         {
             "rel": "item",
             "type": "application/json",
-            "href": "./models/tg_randfor.json"
+            "href": "./models/tg_lasso.json"
         },
         {
             "rel": "item",
             "type": "application/json",
-            "href": "./models/tg_tbats.json"
+            "href": "./models/tg_precip_lm.json"
         },
         {
             "rel": "item",
             "type": "application/json",
-            "href": "./models/tg_temp_lm.json"
+            "href": "./models/tg_precip_lm_all_sites.json"
         },
         {
             "rel": "item",
             "type": "application/json",
-            "href": "./models/tg_temp_lm_all_sites.json"
+            "href": "./models/tg_randfor.json"
         },
         {
             "rel": "parent",

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json

@@ -31,7 +31,7 @@
     "properties": {
         "title": "USGSHABs1",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-12T00:00:00Z",
         "end_datetime": "2024-03-09T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json

@@ -55,7 +55,7 @@
     "properties": {
         "title": "cb_prophet",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-14T00:00:00Z",
         "end_datetime": "2024-03-10T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/climatology.json

@@ -59,7 +59,7 @@
     "properties": {
         "title": "climatology",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-01-02T00:00:00Z",
         "end_datetime": "2024-09-26T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json

@@ -59,7 +59,7 @@
     "properties": {
         "title": "persistenceRW",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-15T00:00:00Z",
         "end_datetime": "2024-09-25T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json

@@ -47,7 +47,7 @@
     "properties": {
         "title": "procBlanchardMonod",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-13T00:00:00Z",
         "end_datetime": "2024-03-06T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json

@@ -47,7 +47,7 @@
     "properties": {
         "title": "procCTMIMonod",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-13T00:00:00Z",
         "end_datetime": "2024-03-06T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json

@@ -47,7 +47,7 @@
     "properties": {
         "title": "procEppleyNorbergMonod",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-13T00:00:00Z",
         "end_datetime": "2024-03-06T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json

@@ -47,7 +47,7 @@
     "properties": {
         "title": "procEppleyNorbergSteele",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-13T00:00:00Z",
         "end_datetime": "2024-03-06T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json

@@ -47,7 +47,7 @@
     "properties": {
         "title": "procHinshelwoodMonod",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-13T00:00:00Z",
         "end_datetime": "2024-03-06T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json

@@ -47,7 +47,7 @@
     "properties": {
         "title": "procHinshelwoodSteele",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-13T00:00:00Z",
         "end_datetime": "2024-03-06T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json

@@ -59,7 +59,7 @@
     "properties": {
         "title": "tg_arima",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-01-01T00:00:00Z",
         "end_datetime": "2024-08-18T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json

@@ -59,7 +59,7 @@
     "properties": {
         "title": "tg_ets",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-01-01T00:00:00Z",
         "end_datetime": "2024-08-18T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json

@@ -59,7 +59,7 @@
     "properties": {
         "title": "tg_humidity_lm",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-14T00:00:00Z",
         "end_datetime": "2024-03-08T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json

@@ -59,7 +59,7 @@
     "properties": {
         "title": "tg_humidity_lm_all_sites",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-14T00:00:00Z",
         "end_datetime": "2024-03-05T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json

@@ -59,7 +59,7 @@
     "properties": {
         "title": "tg_lasso",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-14T00:00:00Z",
         "end_datetime": "2024-03-05T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json

@@ -59,7 +59,7 @@
     "properties": {
         "title": "tg_precip_lm",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-14T00:00:00Z",
         "end_datetime": "2024-03-08T00:00:00Z",
         "providers": [

data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json

@@ -59,7 +59,7 @@
     "properties": {
         "title": "tg_precip_lm_all_sites",
         "description": "All forecasts 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                                    Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)",
-        "datetime": "2024-09-12T00:00:00Z",
+        "datetime": "2024-09-13T00:00:00Z",
         "start_datetime": "2023-11-14T00:00:00Z",
         "end_datetime": "2024-09-19T00:00:00Z",
         "providers": [