DOI: 10.5061/dryad.2z34tmpzp
title: Progress toward forecasting excessive rainfall with random forests based on a deterministic convection-allowing model

description: 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.
description: # 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-21-0130.1) Forecast zip files contain forecasts in GRIB2 format. Gridded forecasts are encoded in values between 0 and 1, indicating probabilities of excessive rainfall from 0 to 100%. GRIB2 file naming convention includes a date stamp YYYYMMDD00, which indicates the initialization time of the HRRR forecast used to provide predictors to the RF. The forecast is valid for the 24-h period from 1200 UTC to 1200 UTC. For example, a file named EXQPF_DAY1_NSSL_PROBS_V1_2024042400.grib2 uses predictors derived from the HRRR forecast initialized at 0000 UTC 24 Apr 2024, and contains a gridded forecast valid for the 24-h period from 1200 UTC 24 Apr - 1200 UTC 25 Apr 2024. The grid_stat files, which contain verification data and official WPC excessive rainfall outlooks (EROs), have a date stamp which explicitly indicates the valid time. For example, the file named grid_stat_ALL_ERO_s2024042412_e2024042512_vhr09_240000L_20240425_120000V_pairs.nc is valid for the 24-h period from 1200 UTC 24 Apr - 1200 UTC 25 Apr 2024. The files are in netCDF format. The variable named FCST_ERO_Surface contains the 0900 UTC day one WPC ERO valid for the indicated time period, again with values between 0 and 1 indicating probabilities of excessive rainfall from 0 to 100%. The variable beginning with OBS_ALL contains the verification dataset derived from the Unified Flood Verification System (UFVS), described in more detail in this reference: [https://doi.org/10.1175/WAF-D-20-0020.1](https://doi.org/10.1175/WAF-D-20-0020.1) ### Files and variables #### File: control.zip **Description:** Daily random forest (RF) forecasts from the control experiment (CTRL) #### File: mean.zip **Description:** Daily RF forecasts from the MEAN experiment (using the spatial mean of predictor values) #### File: mean_max.zip **Description:** Daily RF forecasts from the MEAN_MAX experiment (using spatial max/min of storm attribute predictors, and spatial mean of environmental predictor values) #### File: tl.zip **Description:** Daily RF forecasts from the MEAN_MAX_TL experiment, using the mean of predictor values across three HRRR simulations (0000, 0600, 1200 UTC) #### File: hrrr2021.zip **Description:** Daily RF forecasts from the 2021 version of the HRRR-based RF, evaluated at the Flash Flood and Intense Rainfall (FFaIR) Experiment during 2021. #### File: hrrr2022.zip **Description:** Daily RF forecasts from the 2022 version of the HRRR-based RF, evaluated at the 2022 FFaIR experiment. #### File: hrrr2023.zip **Description:** Daily RF forecasts from the 2023 version of the HRRR-based RF, evaluated at the 2023 FFaIR experiment. #### File: hrrr2021_mismatch.zip **Description:** Daily RF forecasts from the 2021 version of the HRRR-based RF, trained on HRRRv3 but applied to HRRRv4 forecasts during Aug - Dec 2020. #### File: grid_stat_2020.zip **Description:** Daily verification data for 2020 from the Unified Flood Verification System (UFVS), as well as day one excessive rainfall outlooks (EROs) issued at 0900 UTC by the Weather Prediction Center (WPC) #### File: grid_stat_2021.zip **Description:** Daily verification data and EROs for 2021. #### File: grid_stat_2025.zip **Description:** Daily verification data and EROs for 2025. #### File: grid_stat_2023.zip **Description:** Daily verification data and EROs for 2023. #### File: grid_stat_2022.zip **Description:** Daily verification data and EROs for 2022. #### File: grid_stat_2024.zip **Description:** Daily verification data and EROs for 2024. ## Access information Other publicly accessible locations of the data: * none Data was derived from the following sources: * HRRR data available at several sites, outlined at [https://rapidrefresh.noaa.gov/hrrr/](https://rapidrefresh.noaa.gov/hrrr/)