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 2023-05-08.
+
Scores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since 2023-05-09.
@@ -234,8 +234,8 @@
Aggregated scores
diff --git a/search.json b/search.json
index fbd520d6a0..733bdfe56e 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 2024-05-06\n\nRiver 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 2024-05-07\n\nRiver 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 2023-05-08.\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 2023-05-09.\n\nLearn about the continous ranked probablity score here\n\nAquatics: chrophyll-a"
},
{
"objectID": "targets.html",
@@ -144,7 +144,7 @@
"href": "instructions.html#representing-uncertainity",
"title": "How to forecast",
"section": "5 Representing uncertainity",
- "text": "5 Representing uncertainity\nUncertainty is represented through the family - parameter columns in the file that you submit.\n\n5.0.1 Parameteric forecast\nFor a parametric forecast with a normal distribution, the family column would have the word normal to designate a normal distribution and the parameter column must have values of mu and sigma for each forecasted variable, site_id, depth, and time combination.\nParameteric forecasts for binary variables should use bernoulli as the family and prob as the parameter.\nThe following names and parameterization of the distribution are currently supported (family: parameters):\n\nlognormal: mu, sigma\nnormal: mu,sigma\nbernoulli: prob\nbeta: shape1, shape2\nuniform: min, max\ngamma: shape, rate\nlogistic: location, scale\nexponential: rate\npoisson: lambda\n\nIf you are submitting a forecast that is not in the supported list, we recommend using the ensemble format and sampling from your distribution to generate a set of ensemble members that represents your forecast distribution.\n\n\n5.0.2 Ensemble (or sample) forecast\nEnsemble (or sample) forecasts use the family value of ensemble and the parameter values are the ensemble index.\nWhen forecasts using the ensemble family are scored, we assume that the ensemble is a set of equally likely realizations that are sampled from a distribution that is comparable to that of the observations (i.e., no broad adjustments are required to make the ensemble more consistent with observations). This is referred to as a “perfect ensemble” by Brcker and Smith (2007). Ensemble (or sample) forecasts are transformed to a probability distribution function (e.g., dressed) using the default methods in the scoringRules R package (empirical version of the quantile decomposition for the crps).\n\n\n5.1 Example forecasts\nHere is an example of a forecast that uses a normal distribution:\n\ndf <- readr::read_csv(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/forecasts/raw/project_id=usgsrc4cast/T20240506050515_usgsrc4cast-2024-05-05-climatology.csv.gz\", show_col_types = FALSE)\n\n\ndf |> \n head() |> \n knitr::kable()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nproject_id\nmodel_id\ndatetime\nreference_datetime\nduration\nsite_id\nfamily\nparameter\nvariable\nprediction\n\n\n\n\nusgsrc4cast\nclimatology\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nmu\nchla\n2.316215\n\n\nusgsrc4cast\nclimatology\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nsigma\nchla\n1.013190\n\n\nusgsrc4cast\nclimatology\n2024-05-07\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nmu\nchla\n2.016441\n\n\nusgsrc4cast\nclimatology\n2024-05-07\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nsigma\nchla\n1.013190\n\n\nusgsrc4cast\nclimatology\n2024-05-08\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nmu\nchla\n2.694340\n\n\nusgsrc4cast\nclimatology\n2024-05-08\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nsigma\nchla\n1.013190\n\n\n\n\n\nFor an ensemble (or sample) forecast, the family column uses the word ensemble to designate that it is a ensemble forecast and the parameter column is the ensemble member number (1, 2, 3 …)\n\ndf <- readr::read_csv(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/forecasts/raw/project_id=usgsrc4cast/T20240506050515_usgsrc4cast-2024-05-05-persistenceRW.csv.gz\", show_col_types = FALSE)\n\n\ndf |> \n dplyr::arrange(variable, site_id, datetime, parameter) |> \n head() |> \n knitr::kable()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nproject_id\nmodel_id\ndatetime\nreference_datetime\nduration\nsite_id\nfamily\nparameter\nvariable\nprediction\n\n\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n1\nchla\n0.1739193\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n2\nchla\n0.1651993\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n3\nchla\n0.2120872\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n4\nchla\n0.1574321\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n5\nchla\n0.2035837\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n6\nchla\n0.3037156"
+ "text": "5 Representing uncertainity\nUncertainty is represented through the family - parameter columns in the file that you submit.\n\n5.0.1 Parameteric forecast\nFor a parametric forecast with a normal distribution, the family column would have the word normal to designate a normal distribution and the parameter column must have values of mu and sigma for each forecasted variable, site_id, depth, and time combination.\nParameteric forecasts for binary variables should use bernoulli as the family and prob as the parameter.\nThe following names and parameterization of the distribution are currently supported (family: parameters):\n\nlognormal: mu, sigma\nnormal: mu,sigma\nbernoulli: prob\nbeta: shape1, shape2\nuniform: min, max\ngamma: shape, rate\nlogistic: location, scale\nexponential: rate\npoisson: lambda\n\nIf you are submitting a forecast that is not in the supported list, we recommend using the ensemble format and sampling from your distribution to generate a set of ensemble members that represents your forecast distribution.\n\n\n5.0.2 Ensemble (or sample) forecast\nEnsemble (or sample) forecasts use the family value of ensemble and the parameter values are the ensemble index.\nWhen forecasts using the ensemble family are scored, we assume that the ensemble is a set of equally likely realizations that are sampled from a distribution that is comparable to that of the observations (i.e., no broad adjustments are required to make the ensemble more consistent with observations). This is referred to as a “perfect ensemble” by Bröcker and Smith (2007). Ensemble (or sample) forecasts are transformed to a probability distribution function (e.g., dressed) using the default methods in the scoringRules R package (empirical version of the quantile decomposition for the crps).\n\n\n5.1 Example forecasts\nHere is an example of a forecast that uses a normal distribution:\n\ndf <- readr::read_csv(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/forecasts/raw/project_id=usgsrc4cast/T20240506050515_usgsrc4cast-2024-05-05-climatology.csv.gz\", show_col_types = FALSE)\n\n\ndf |> \n head() |> \n knitr::kable()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nproject_id\nmodel_id\ndatetime\nreference_datetime\nduration\nsite_id\nfamily\nparameter\nvariable\nprediction\n\n\n\n\nusgsrc4cast\nclimatology\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nmu\nchla\n2.316215\n\n\nusgsrc4cast\nclimatology\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nsigma\nchla\n1.013190\n\n\nusgsrc4cast\nclimatology\n2024-05-07\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nmu\nchla\n2.016441\n\n\nusgsrc4cast\nclimatology\n2024-05-07\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nsigma\nchla\n1.013190\n\n\nusgsrc4cast\nclimatology\n2024-05-08\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nmu\nchla\n2.694340\n\n\nusgsrc4cast\nclimatology\n2024-05-08\n2024-05-05\nP1D\nUSGS-01427510\nnormal\nsigma\nchla\n1.013190\n\n\n\n\n\nFor an ensemble (or sample) forecast, the family column uses the word ensemble to designate that it is a ensemble forecast and the parameter column is the ensemble member number (1, 2, 3 …)\n\ndf <- readr::read_csv(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/forecasts/raw/project_id=usgsrc4cast/T20240506050515_usgsrc4cast-2024-05-05-persistenceRW.csv.gz\", show_col_types = FALSE)\n\n\ndf |> \n dplyr::arrange(variable, site_id, datetime, parameter) |> \n head() |> \n knitr::kable()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nproject_id\nmodel_id\ndatetime\nreference_datetime\nduration\nsite_id\nfamily\nparameter\nvariable\nprediction\n\n\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n1\nchla\n0.1739193\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n2\nchla\n0.1651993\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n3\nchla\n0.2120872\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n4\nchla\n0.1574321\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n5\nchla\n0.2035837\n\n\nusgsrc4cast\npersistenceRW\n2024-05-06\n2024-05-05\nP1D\nUSGS-01427510\nensemble\n6\nchla\n0.3037156"
},
{
"objectID": "instructions.html#submission-process",
@@ -179,7 +179,7 @@
"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\n254\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-05-07\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n15.3\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\n256\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-05-08\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n15.3\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",
@@ -200,6 +200,6 @@
"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<80><93>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 2024-05-07"
+ "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 2024-05-08"
}
]
\ No newline at end of file
diff --git a/targets.html b/targets.html
index 5765cb5585..0e8ce7d848 100644
--- a/targets.html
+++ b/targets.html
@@ -387,8 +387,8 @@
River Chlorophyll
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 cb517b4bbc..158173899f 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