Scores are shown by reference date and forecast horizon (in days).
-Scores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since 2023-03-05.
+
Scores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since 2023-03-06.
@@ -233,8 +233,8 @@
Aggregated scores
diff --git a/search.json b/search.json
index 06f714a16a..1acb0ffa81 100644
--- a/search.json
+++ b/search.json
@@ -11,7 +11,7 @@
"href": "performance.html#sec-performance",
"title": "Forecast performance",
"section": "Most recent forecasts",
- "text": "Most recent forecasts\nOnly the top performing models from the last 30 days are shown.\nForecasts submitted on 2024-03-03\n\nAquatics: Chlorophyll-a\nForecast summaries are available here\n\n\n\n\n\n\n:::"
+ "text": "Most recent forecasts\nOnly the top performing models from the last 30 days are shown.\nForecasts submitted on 2024-03-04\n\nAquatics: Chlorophyll-a\nForecast summaries are available here\n\n\n\n\n\n\n:::"
},
{
"objectID": "performance.html#forecast-analysis",
@@ -25,7 +25,7 @@
"href": "performance.html#aggregated-scores",
"title": "Forecast performance",
"section": "Aggregated scores",
- "text": "Aggregated scores\nAverage skill scores of each model across all sites.\n\nScores are shown by reference date and forecast horizon (in days).\n\nScores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since 2023-03-05.\n\nLearn about the continous ranked probablity score here\n\nAquatics: chrophyll-a"
+ "text": "Aggregated scores\nAverage skill scores of each model across all sites.\n\nScores are shown by reference date and forecast horizon (in days).\n\nScores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since 2023-03-06.\n\nLearn about the continous ranked probablity score here\n\nAquatics: chrophyll-a"
},
{
"objectID": "learn-more.html",
@@ -60,7 +60,7 @@
"href": "index.html#why-a-forecasting-challenge",
"title": "EFI-USGS River Chlorophyll Forecasting Challenge",
"section": "Why a forecasting challenge?",
- "text": "Why a forecasting challenge?\nOur vision is to use forecasts to advance theory and to support natural resource management. We can begin to realize this vision by creating and analyzing a catalog of forecasts from a range of ecological systems, spatiotemporal scales, and environmental gradients.\nOur forecasting challenge is platform for the ecological and data science communities to advance skills in forecasting ecological systems and for generating forecasts that contribute to a synthetic understanding of patterns of environmental predictability. Rewards for contributing are skill advancement, joy, and potential involved in manuscripts. We do not currently crown winner nor offer financial awards.\nThe original NEON forecasting challenge has been an excellent focal project in university courses and this EFI-USGS challenge could be used as classroom projects as well.\n \n\n\n\n\n\n\n\n\n\nTotal forecasts submitted to the EFI-USGS Challenge\n74\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-03-04\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n15.12\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of variables being forecasted\n1"
+ "text": "Why a forecasting challenge?\nOur vision is to use forecasts to advance theory and to support natural resource management. We can begin to realize this vision by creating and analyzing a catalog of forecasts from a range of ecological systems, spatiotemporal scales, and environmental gradients.\nOur forecasting challenge is platform for the ecological and data science communities to advance skills in forecasting ecological systems and for generating forecasts that contribute to a synthetic understanding of patterns of environmental predictability. Rewards for contributing are skill advancement, joy, and potential involved in manuscripts. We do not currently crown winner nor offer financial awards.\nThe original NEON forecasting challenge has been an excellent focal project in university courses and this EFI-USGS challenge could be used as classroom projects as well.\n \n\n\n\n\n\n\n\n\n\nTotal forecasts submitted to the EFI-USGS Challenge\n77\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-03-04\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n15.12\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of variables being forecasted\n1"
},
{
"objectID": "index.html#the-challenge-is-a-platform",
@@ -81,7 +81,7 @@
"href": "index.html#acknowledgements",
"title": "EFI-USGS River Chlorophyll Forecasting Challenge",
"section": "Acknowledgements",
- "text": "Acknowledgements\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616 We thank the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388. Page last updated on 2024-03-04"
+ "text": "Acknowledgements\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616 We thank the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388. Page last updated on 2024-03-05"
},
{
"objectID": "instructions.html",
@@ -193,7 +193,7 @@
"href": "targets.html#sec-targets",
"title": "What to forecast",
"section": "Explore the targets and themes",
- "text": "Explore the targets and themes\nInformation on the targets files for the “themes” is below. In the tables,\n\n“duration” is the time-step of the variable where P1D is a daily mean.\nThe “forecast horizon” is the number of days-ahead that we want you to forecast.\nThe “latency” is the time between data collection and data availability in the targets file\n\n\nAquatics\n\nThe aquatics theme challenges you to forecast daily mean water quality variables at up-to 10 river USGS sites.\n\n\n\n\n\nvariable\nduration\n\n\n\n\nchla\nP1D\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=usgsrc4cast/duration=P1D/river-chl-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\naquatics_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\n\n\n\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-17\nP1D\nchla\n2.8\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-18\nP1D\nchla\n2.7\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-19\nP1D\nchla\n3.1\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-20\nP1D\nchla\n3.3\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-21\nP1D\nchla\n4.4\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-22\nP1D\nchla\n7.9\n\n\n\n\n\nand the time series for the focal sites\n\naquatics_targets |> \n filter(site_id %in% aquatics_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_wrap(~site_id, scales = \"free\") +\n theme_bw() + \n ylab(\"Chlorophyll-a (ug/L)\")"
+ "text": "Explore the targets and themes\nInformation on the targets files for the “themes” is below. In the tables,\n\n“duration” is the time-step of the variable where P1D is a daily mean.\nThe “forecast horizon” is the number of days-ahead that we want you to forecast.\nThe “latency” is the time between data collection and data availability in the targets file\n\n\nAquatics\n\nThe aquatics theme challenges you to forecast daily mean water quality variables at up-to 10 river USGS sites.\n\n\n\n\n\n\n\n\n\n\n\n\nvariable\nduration\nDescription\nhorizon\nLatency\n\n\n\n\nchla\nP1D\ndaily mean Chlorophyll-a (ug/L)\n30 days\n~ 0 days\n\n\n\n\n\nThe daily mean target file is located at the following URL.\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=usgsrc4cast/duration=P1D/river-chl-targets.csv.gz\"\n\nYou can directly load it into R using the following\n\naquatics_targets <- read_csv(url, show_col_types = FALSE)\n\nThe file contains the following columns\n\n\n\n\n\n\n\n\n\n\n\n\n\nproject_id\nsite_id\ndatetime\nduration\nvariable\nobservation\n\n\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-17\nP1D\nchla\n2.8\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-18\nP1D\nchla\n2.7\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-19\nP1D\nchla\n3.1\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-20\nP1D\nchla\n3.3\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-21\nP1D\nchla\n4.4\n\n\nusgsrc4cast\nUSGS-01463500\n2017-02-22\nP1D\nchla\n7.9\n\n\n\n\n\nand the time series for the focal sites\n\naquatics_targets |> \n filter(site_id %in% aquatics_focal_sites) |> \n ggplot(aes(x = datetime, y = observation)) +\n geom_point() +\n facet_wrap(~site_id, scales = \"free\") +\n theme_bw() + \n ylab(\"Chlorophyll-a (ug/L)\")"
},
{
"objectID": "targets.html#explore-the-sites",
diff --git a/sitemap.xml b/sitemap.xml
index 0c10410bd4..5b66679124 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,23 +2,23 @@
https://projects.ecoforecast.org/usgsrc4cast-ci/catalog.html
- 2024-03-03
+ 2024-03-04
https://projects.ecoforecast.org/usgsrc4cast-ci/targets.html
- 2024-03-03
+ 2024-03-04
https://projects.ecoforecast.org/usgsrc4cast-ci/instructions.html
- 2024-03-03
+ 2024-03-04
https://projects.ecoforecast.org/usgsrc4cast-ci/performance.html
- 2024-03-03
+ 2024-03-04
https://projects.ecoforecast.org/usgsrc4cast-ci/index.html
- 2024-03-03
+ 2024-03-04
https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/usgsrc4cast-ci/main/catalog/catalog.json
diff --git a/targets.html b/targets.html
index 104f0ef13b..9f5ccc87e0 100644
--- a/targets.html
+++ b/targets.html
@@ -256,16 +256,29 @@ Aquatics
+
+
+
+
+
+
+
| chla |
P1D |
+daily mean Chlorophyll-a (ug/L) |
+30 days |
+~ 0 days |
@@ -373,8 +386,8 @@
Aquatics
Explore the sites
The following table lists all the sites in the EFI-USGS Ecological Forecasting Challenge. The columns with “theme” names incidate whether that site is included in that theme’s target file.