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

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

Dataset DOI: [10.5061/dryad.2z34tmpzp](10.5061/dryad.2z34tmpzp)

## Description of the data and file structure

The dataset includes zip files which contain daily random forest (RF) excessive rainfall forecasts in GRIB2 format, as well as files containing daily verification data and official excessive rainfall outlooks from the Weather Prediction Center (WPC). This project aimed to test various ways to construct random forests for predicting excessive rainfall based on predictors derived from deterministic convection-allowing numerical weather prediction model forecasts. The forecast model used is the NOAA High-Resolution Rapid Refresh (HRRR), with 3-km grid spacing (see the references below for more information).

[https://doi.org/10.1175/WAF-D-21-0151.1](https://doi.org/10.1175/WAF-D-21-0151.1)

[https://doi.org/10.1175/WAF-D-21-0130.1](https://doi.org/10.1175/WAF-D...,