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Progress toward forecasting excessive rainfall with random forests based on a deterministic convection-allowing model

James, Eric1; Schumacher, Russ2

Published Nov 25, 2025 on Dryad. https://doi.org/10.5061/dryad.2z34tmpzp

Data files

Nov 25, 2025 version files 110.04 MB

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Abstract

This dataset consists of forecasts produced by random forests (RFs) using predictor information from NOAA's deterministic convection-allowing numerical weather prediction model, the High-Resolution Rapid Refresh. Included are sensitivity experiments on predictor assembly and model version, as well as real-time forecasts from three subsequent versions evaluated at the Weather Prediction Center's Flash Flood and Intense Rainfall Experiment (FFaIR) during 2021-2023. Sensitivity experiments reveal that the RF performs better when we use predictor information from all model gridpoints, not just sparse gridpoints, particularly in situations with small-scale precipitation maxima in the model forecast. The RF is also better able to learn the relationships between predictor values and resulting excessive rainfall risk when the RF considers mean predictors from three model simulations rather than predictors from a single simulation. The real-time RFs evaluated at FFaIR exhibited year-over-year improvements stemming from the results of these sensitivity experiments as well as feedback from FFaIR participants. However, RFs based on deterministic convection allow models to continue to underperform those based on coarse global ensemble systems.