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 2023-03-07.
+
Scores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since 2023-03-08.
@@ -233,8 +233,8 @@
Aggregated scores
diff --git a/search.json b/search.json
index 5db390c464..a66135f821 100644
--- a/search.json
+++ b/search.json
@@ -1,31 +1,73 @@
[
{
- "objectID": "performance.html",
- "href": "performance.html",
- "title": "Forecast performance",
+ "objectID": "index.html",
+ "href": "index.html",
+ "title": "EFI-USGS River Chlorophyll Forecasting Challenge",
"section": "",
- "text": "This page visualizes the forecasts and forecast performance for the focal target variables."
+ "text": "We invite you to submit forecasts!\nThe EFI-USGS River Chlorophyll Forecasting Challenge is an open platform for the ecological and data science communities to forecast data from the U.S. Geological Survey (USGS) before they are collected.\nThe Challenge is hosted by the Ecological Forecasting Initiative Research Coordination Network and sponsored by the U.S. National Science Foundation. This challenge is co-hosted by the USGS Proxies Project, an effort supported by the Water Mission Area Water Quality Processes program to develop estimation methods for per- and polyfluoroalkyl substances (PFAS), harmful algal blooms (HABs), and 12 elements of concern, at multiple spatial and temporal scales."
},
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- "objectID": "performance.html#sec-performance",
- "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 2024-03-05\n\nAquatics: Chlorophyll-a\nForecast summaries are available here\n\n\n\n\n\n\n:::"
+ "objectID": "index.html#why-a-forecasting-challenge",
+ "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\n83\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-03-07\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n15.13\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of variables being forecasted\n1"
},
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- "objectID": "performance.html#forecast-analysis",
- "href": "performance.html#forecast-analysis",
- "title": "Forecast performance",
- "section": "Forecast analysis",
- "text": "Forecast analysis\nBelow are forecasts submitted 30 days ago and include the observations used to evaluate them. Mouse over to see the team id, scroll to zoom. Only the top five performing models are shown. Information on how to access the scores can be found in our catalog\n\nAquatics: chrophyll-a"
+ "objectID": "index.html#the-challenge-is-a-platform",
+ "href": "index.html#the-challenge-is-a-platform",
+ "title": "EFI-USGS River Chlorophyll Forecasting Challenge",
+ "section": "The Challenge is a platform",
+ "text": "The Challenge is a platform\nOur platform is designed to empower you to contribute by providing target data, numerical weather forecasts, and tutorials. We automatically score your forecasts using the latest USGS data. All forecasts and scores are publicly available through cloud storage and discoverable through our catalog.\n \nFigure from Thomas et al. 2023"
},
{
- "objectID": "performance.html#aggregated-scores",
- "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 2023-03-07.\n\nLearn about the continous ranked probablity score here\n\nAquatics: chrophyll-a"
+ "objectID": "index.html#contact",
+ "href": "index.html#contact",
+ "title": "EFI-USGS River Chlorophyll Forecasting Challenge",
+ "section": "Contact",
+ "text": "Contact\neco4cast.initiative@gmail.com and jzwart@usgs.gov"
+ },
+ {
+ "objectID": "index.html#acknowledgements",
+ "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 2024-03-07"
+ },
+ {
+ "objectID": "targets.html",
+ "href": "targets.html",
+ "title": "What to forecast",
+ "section": "",
+ "text": "The “targets” are time-series of United States Geological Survey (USGS) data for use in model development and forecast evaluation.\nThe targets are updated as new USGS data are made available.\nThis challenge focuses on forecasting river chlorophyll-a at select USGS monitoring locations. The links to targets files are included below."
+ },
+ {
+ "objectID": "targets.html#tldr-forecast-the-targets",
+ "href": "targets.html#tldr-forecast-the-targets",
+ "title": "What to forecast",
+ "section": "",
+ "text": "The “targets” are time-series of United States Geological Survey (USGS) data for use in model development and forecast evaluation.\nThe targets are updated as new USGS data are made available.\nThis challenge focuses on forecasting river chlorophyll-a at select USGS monitoring locations. The links to targets files are included below."
+ },
+ {
+ "objectID": "targets.html#sec-starting-sites",
+ "href": "targets.html#sec-starting-sites",
+ "title": "What to forecast",
+ "section": "Where to start",
+ "text": "Where to start\n\n\n\nAs you develop your forecasting skills and want to expand to more sites, the targets are available at all 10 USGS sites. You may also consider submitting forecasts to sites that match your interests. For example, a class being taught in the winter may be more interested in forecasting southern sites while a summer class may focus on more northern sites.\nMore information about USGS sites can be found in the site metadata and on USGS’s website"
+ },
+ {
+ "objectID": "targets.html#sec-targets",
+ "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\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",
+ "href": "targets.html#explore-the-sites",
+ "title": "What to forecast",
+ "section": "Explore the sites",
+ "text": "Explore the sites\n\n\n\n\n\n\n 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.\n\n\n\n\n\n\n\n\n\n\n\nsite_id\nsite_no\nstation_nm\nsite_url\n\n\n\n\nUSGS-14211720\n14211720\nWILLAMETTE RIVER AT PORTLAND, OR\nhttps://waterdata.usgs.gov/monitoring-location/14211720\n\n\nUSGS-14211010\n14211010\nCLACKAMAS RIVER NEAR OREGON CITY, OR\nhttps://waterdata.usgs.gov/monitoring-location/14211010\n\n\nUSGS-14181500\n14181500\nNORTH SANTIAM RIVER AT NIAGARA, OR\nhttps://waterdata.usgs.gov/monitoring-location/14181500\n\n\nUSGS-05586300\n05586300\nILLINOIS RIVER AT FLORENCE, IL\nhttps://waterdata.usgs.gov/monitoring-location/05586300\n\n\nUSGS-05558300\n05558300\nILLINOIS RIVER AT HENRY, IL\nhttps://waterdata.usgs.gov/monitoring-location/05558300\n\n\nUSGS-05553700\n05553700\nILLINOIS RIVER AT STARVED ROCK, IL\nhttps://waterdata.usgs.gov/monitoring-location/05553700\n\n\nUSGS-05543010\n05543010\nILLINOIS RIVER AT SENECA, IL\nhttps://waterdata.usgs.gov/monitoring-location/05543010\n\n\nUSGS-05549500\n05549500\nFOX RIVER NEAR MCHENRY, IL\nhttps://waterdata.usgs.gov/monitoring-location/05549500\n\n\nUSGS-01427510\n01427510\nDELAWARE RIVER AT CALLICOON NY\nhttps://waterdata.usgs.gov/monitoring-location/01427510\n\n\nUSGS-01463500\n01463500\nDelaware River at Trenton NJ\nhttps://waterdata.usgs.gov/monitoring-location/01463500"
},
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@@ -49,39 +91,25 @@
"text": "Lewis, A., W. Woelmer, H. Wander, D. Howard, J. Smith, R. McClure, M. Lofton, N. Hammond, R. Corrigan, R.Q. Thomas, C.C. Carey. 2022. Increased adoption of best practices in ecological forecasting enables comparisons of forecastability across systems. Ecological Applications 32: e02500 https://doi.org/10.1002/eap.2500\nLewis, A. S. L., Rollinson, C. R., Allyn, A. J., Ashander, J., Brodie, S., Brookson, C. B., et al. (2023). The power of forecasts to advance ecological theory. Methods in Ecology and Evolution, 14(3), 746–756. https://doi.org/10.1111/2041-210X.13955\n\n\n\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\nThomas, R.Q, R.P. McClure, T.N. Moore, W.M. Woelmer, C. Boettiger, R.J. Figueiredo, R.T. Hensley, C.C. Carey. Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S. Frontiers in Ecology and Environment 21: 220–226. https://doi.org/10.1002/fee.2623\nWheeler, K., M. Dietze, D. LeBauer, J. Peters, A.D. Richardson, R.Q. Thomas, K. Zhu, U. Bhat, S. Munch, R.F Buzbee, M. Chen, B. Goldstein, J.S. Guo, D. Hao, C. Jones, M. Kelly-Fair, H. Liu, C. Malmborg, N. Neupane. D. Pal, A. Ross, V. Shirey, Y. Song, M. Steen, E.A. Vance, W.M. Woelmer, J. Wynne and L. Zachmann. Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge.\n\n\n\nDietze, M., R.Q. Thomas, J. Peters, C. Boettiger, A. Shiklomanov, and J. Ashander. 2023. A community convention for ecological forecasting: output files and metadata v1.0. Ecosphere 14: e4686 https://doi.org/10.1002/ecs2.4686\n\n\n\nMoore, T.N., R.Q. Thomas, W.M. Woelmer, C.C Carey. 2022. Integrating ecological forecasting into undergraduate ecology curricula with an R Shiny application-based teaching module. Forecasting 4:604-633. https://doi.org/10.3390/forecast4030033\nPeters, J. and R.Q. Thomas. 2021. Going Virtual: What We Learned from the Ecological Forecasting Initiative Research Coordination Network Virtual Workshop. Bulletin of the Ecological Society of America 102: e01828 https://doi.org/10.1002/bes2.1828\nWillson, A.M., H. Gallo, J.A. Peters, A. Abeyta, N. Bueno Watts, C.C. Carey, T.N. Moore, G. Smies, R.Q. Thomas, W.M. Woelmer, and J.S. McLachlan. 2023. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution 13: e10001. https://doi.org/10.1002/ece3.10001\nWoelmer, W. M., Bradley, L. M., Haber, L. T., Klinges, D. H., Lewis, A. S. L., Mohr, E. J., et al. (2021). Ten simple rules for training yourself in an emerging field. PLOS Computational Biology, 17(10), e1009440. https://doi.org/10.1371/journal.pcbi.1009440\nWoelmer, W.M., T.N. Moore, M.E. Lofton, R.Q. Thomas, and C.C. Carey. 2023. Embedding communication concepts in forecasting training increases students’ understanding of ecological uncertainty Ecosphere 14: e4628 https://doi.org/10.1002/ecs2.4628"
},
{
- "objectID": "index.html",
- "href": "index.html",
- "title": "EFI-USGS River Chlorophyll Forecasting Challenge",
+ "objectID": "catalog.html",
+ "href": "catalog.html",
+ "title": "Forecast catalog",
"section": "",
- "text": "We invite you to submit forecasts!\nThe EFI-USGS River Chlorophyll Forecasting Challenge is an open platform for the ecological and data science communities to forecast data from the U.S. Geological Survey (USGS) before they are collected.\nThe Challenge is hosted by the Ecological Forecasting Initiative Research Coordination Network and sponsored by the U.S. National Science Foundation. This challenge is co-hosted by the USGS Proxies Project, an effort supported by the Water Mission Area Water Quality Processes program to develop estimation methods for per- and polyfluoroalkyl substances (PFAS), harmful algal blooms (HABs), and 12 elements of concern, at multiple spatial and temporal scales."
- },
- {
- "objectID": "index.html#why-a-forecasting-challenge",
- "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\n80\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-03-06\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n15.13\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",
- "href": "index.html#the-challenge-is-a-platform",
- "title": "EFI-USGS River Chlorophyll Forecasting Challenge",
- "section": "The Challenge is a platform",
- "text": "The Challenge is a platform\nOur platform is designed to empower you to contribute by providing target data, numerical weather forecasts, and tutorials. We automatically score your forecasts using the latest USGS data. All forecasts and scores are publicly available through cloud storage and discoverable through our catalog.\n \nFigure from Thomas et al. 2023"
+ "text": "Note: This figure will become more complete, as more models register and provide model descriptions"
},
{
- "objectID": "index.html#contact",
- "href": "index.html#contact",
- "title": "EFI-USGS River Chlorophyll Forecasting Challenge",
- "section": "Contact",
- "text": "Contact\neco4cast.initiative@gmail.com and jzwart@usgs.gov"
+ "objectID": "catalog.html#what-types-of-models-are-submitting-forecasts",
+ "href": "catalog.html#what-types-of-models-are-submitting-forecasts",
+ "title": "Forecast catalog",
+ "section": "",
+ "text": "Note: This figure will become more complete, as more models register and provide model descriptions"
},
{
- "objectID": "index.html#acknowledgements",
- "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 2024-03-06"
+ "objectID": "catalog.html#sec-spatiotemporal-asset-catalog",
+ "href": "catalog.html#sec-spatiotemporal-asset-catalog",
+ "title": "Forecast catalog",
+ "section": "Catalog of forecast submissions and evaluations",
+ "text": "Catalog of forecast submissions and evaluations\nThe catalog of submitted forecasts and the evaluation of the forecasts (“scores”) is available through the SpatioTemporal Asset Catalogs browser (below). \nThe catalog provides the code that you can use to access forecasts and scores. \nA full page version can be found here"
},
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"objectID": "instructions.html",
@@ -147,59 +175,31 @@
"text": "8 Questions?\nThanks for reading this document!\n\nIf you still have questions about how to submit your forecast to the EFI-USGS River Chlorophyll Forecasting Challenge, we encourage you to email Dr. Jacob Zwart (jzwart{at}usgs.gov)."
},
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- "objectID": "catalog.html",
- "href": "catalog.html",
- "title": "Forecast catalog",
- "section": "",
- "text": "Note: This figure will become more complete, as more models register and provide model descriptions"
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- "objectID": "catalog.html#what-types-of-models-are-submitting-forecasts",
- "href": "catalog.html#what-types-of-models-are-submitting-forecasts",
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- "section": "",
- "text": "Note: This figure will become more complete, as more models register and provide model descriptions"
- },
- {
- "objectID": "catalog.html#sec-spatiotemporal-asset-catalog",
- "href": "catalog.html#sec-spatiotemporal-asset-catalog",
- "title": "Forecast catalog",
- "section": "Catalog of forecast submissions and evaluations",
- "text": "Catalog of forecast submissions and evaluations\nThe catalog of submitted forecasts and the evaluation of the forecasts (“scores”) is available through the SpatioTemporal Asset Catalogs browser (below). \nThe catalog provides the code that you can use to access forecasts and scores. \nA full page version can be found here"
- },
- {
- "objectID": "targets.html",
- "href": "targets.html",
- "title": "What to forecast",
- "section": "",
- "text": "The “targets” are time-series of United States Geological Survey (USGS) data for use in model development and forecast evaluation.\nThe targets are updated as new USGS data are made available.\nThis challenge focuses on forecasting river chlorophyll-a at select USGS monitoring locations. The links to targets files are included below."
- },
- {
- "objectID": "targets.html#tldr-forecast-the-targets",
- "href": "targets.html#tldr-forecast-the-targets",
- "title": "What to forecast",
+ "objectID": "performance.html",
+ "href": "performance.html",
+ "title": "Forecast performance",
"section": "",
- "text": "The “targets” are time-series of United States Geological Survey (USGS) data for use in model development and forecast evaluation.\nThe targets are updated as new USGS data are made available.\nThis challenge focuses on forecasting river chlorophyll-a at select USGS monitoring locations. The links to targets files are included below."
+ "text": "This page visualizes the forecasts and forecast performance for the focal target variables."
},
{
- "objectID": "targets.html#sec-starting-sites",
- "href": "targets.html#sec-starting-sites",
- "title": "What to forecast",
- "section": "Where to start",
- "text": "Where to start\n\n\n\nAs you develop your forecasting skills and want to expand to more sites, the targets are available at all 10 USGS sites. You may also consider submitting forecasts to sites that match your interests. For example, a class being taught in the winter may be more interested in forecasting southern sites while a summer class may focus on more northern sites.\nMore information about USGS sites can be found in the site metadata and on USGS’s website"
+ "objectID": "performance.html#sec-performance",
+ "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 2024-03-06\n\nAquatics: Chlorophyll-a\nForecast summaries are available here\n\n\n\n\n\n\n:::"
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{
- "objectID": "targets.html#sec-targets",
- "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\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": "performance.html#forecast-analysis",
+ "href": "performance.html#forecast-analysis",
+ "title": "Forecast performance",
+ "section": "Forecast analysis",
+ "text": "Forecast analysis\nBelow are forecasts submitted 30 days ago and include the observations used to evaluate them. Mouse over to see the team id, scroll to zoom. Only the top five performing models are shown. Information on how to access the scores can be found in our catalog\n\nAquatics: chrophyll-a"
},
{
- "objectID": "targets.html#explore-the-sites",
- "href": "targets.html#explore-the-sites",
- "title": "What to forecast",
- "section": "Explore the sites",
- "text": "Explore the sites\n\n\n\n\n\n\n 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.\n\n\n\n\n\n\n\n\n\n\n\nsite_id\nsite_no\nstation_nm\nsite_url\n\n\n\n\nUSGS-14211720\n14211720\nWILLAMETTE RIVER AT PORTLAND, OR\nhttps://waterdata.usgs.gov/monitoring-location/14211720\n\n\nUSGS-14211010\n14211010\nCLACKAMAS RIVER NEAR OREGON CITY, OR\nhttps://waterdata.usgs.gov/monitoring-location/14211010\n\n\nUSGS-14181500\n14181500\nNORTH SANTIAM RIVER AT NIAGARA, OR\nhttps://waterdata.usgs.gov/monitoring-location/14181500\n\n\nUSGS-05586300\n05586300\nILLINOIS RIVER AT FLORENCE, IL\nhttps://waterdata.usgs.gov/monitoring-location/05586300\n\n\nUSGS-05558300\n05558300\nILLINOIS RIVER AT HENRY, IL\nhttps://waterdata.usgs.gov/monitoring-location/05558300\n\n\nUSGS-05553700\n05553700\nILLINOIS RIVER AT STARVED ROCK, IL\nhttps://waterdata.usgs.gov/monitoring-location/05553700\n\n\nUSGS-05543010\n05543010\nILLINOIS RIVER AT SENECA, IL\nhttps://waterdata.usgs.gov/monitoring-location/05543010\n\n\nUSGS-05549500\n05549500\nFOX RIVER NEAR MCHENRY, IL\nhttps://waterdata.usgs.gov/monitoring-location/05549500\n\n\nUSGS-01427510\n01427510\nDELAWARE RIVER AT CALLICOON NY\nhttps://waterdata.usgs.gov/monitoring-location/01427510\n\n\nUSGS-01463500\n01463500\nDelaware River at Trenton NJ\nhttps://waterdata.usgs.gov/monitoring-location/01463500"
+ "objectID": "performance.html#aggregated-scores",
+ "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 2023-03-08.\n\nLearn about the continous ranked probablity score here\n\nAquatics: chrophyll-a"
}
]
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index 008f25cf8f..3de09a1256 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,23 +2,23 @@
https://projects.ecoforecast.org/usgsrc4cast-ci/catalog.html
- 2024-03-05
+ 2024-03-06
https://projects.ecoforecast.org/usgsrc4cast-ci/targets.html
- 2024-03-05
+ 2024-03-06
https://projects.ecoforecast.org/usgsrc4cast-ci/instructions.html
- 2024-03-05
+ 2024-03-06
https://projects.ecoforecast.org/usgsrc4cast-ci/performance.html
- 2024-03-05
+ 2024-03-06
https://projects.ecoforecast.org/usgsrc4cast-ci/index.html
- 2024-03-05
+ 2024-03-06
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 d017d8928f..b103f8587d 100644
--- a/targets.html
+++ b/targets.html
@@ -386,8 +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.
diff --git a/targets_files/figure-html/unnamed-chunk-8-1.png b/targets_files/figure-html/unnamed-chunk-8-1.png
index dc043c06c0..ed69cf376b 100644
Binary files a/targets_files/figure-html/unnamed-chunk-8-1.png and b/targets_files/figure-html/unnamed-chunk-8-1.png differ