Forecast dataset associated with “From Random Forests to Flood Forecasts: A Research to Operations Success Story” - Schumacher, Russ S.; Hill, Aaron J.; Klein, Mark; Nelson, James A.; Erickson, Michael J.; Herman, Gregory R. (Colorado State University. Libraries, 2021)
Because excessive rainfall is poorly defined and difficult to forecast, there is a need for tools for Weather Prediction Center (WPC) forecasters to use when generating Excessive Rainfall Outlooks (EROs), which are issued for the contiguous United States at lead times of 1--3 days. To address this need, a probabilistic forecast system for excessive rainfall, known as the Colorado State University-Machine Learning Probabilities (CSU-MLP) system, was developed based on ensemble reforecasts, precipitation observations, and machine learning algorithms, specifically random forests. The CSU-MLP forecasts were designed to emulate the EROs, with the goal being a tool that forecasters can use as a ``first guess'' in the ERO forecast process. Resulting from close collaboration between CSU and WPC and evaluation at the Flash Flood and Intense Rainfall experiment, iterative improvements were made to the forecast system and it was transitioned into operational use at WPC. Quantitative evaluation shows that the CSU-MLP forecasts are skillful and reliable, and they are now being used as a part of the WPC forecast process. This project represents an example of a successful research-to-operations transition, and highlights the potential for machine learning and other post-processing techniques to improve operational predictions.
Schumacher, Hill, and Herman: Department of Atmospheric Science, Colorado State Universit; Klein, Nelson, and Erickson: NOAA Weather Prediction Center.
Gridded forecasts from the Colorado State University-Machine Learning Probabilities (CSU-MLP) system for excessive rainfall prediction over the continental United States. The dataset includes probabilistic forecasts for days 1, 2, and 3 from the 2017, 2019, and 2020 versions of the CSU-MLP forecast system. For the day 2 and 3 forecasts, daily forecasts are included from 19 June 2018 through 15 October 2020; for day-1 forecasts a period from 15 March 2019 through 15 October 2020 is used.
Department of Atmospheric Science
Keywords: weather prediction; machine learning; probabilistic forecasting; excessive rainfall