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Progress toward forecasting excessive rainfall with random forests based on a deterministic convection-allowing model
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- v1Published
- First published
- Evidence type
- Observational study
- Sources
- 1 sourcePeer-reviewed
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Abstract Machine learning applied to high-resolution numerical weather prediction forecasts is gaining popularity for prediction of high-impact weather events, but questions remain on how to design such systems. Building off prior work, we describe sensitivity experiments using a random forest (RF) system for excessive rainfall prediction using the deterministic High-Resolution Rapid Refresh (HRRR). We find that applying an RF trained on HRRRv3 to daily forecasts from HRRRv4 does not significantly degrade result…
55Gain side
How the two sides scoreClose call
57Problem side
The gain
In sensitivity experiments on a random forest excessive rainfall system built on HRRR forecasts, using all nearby gridpoints and averaging predictors across several model initializations significantly improved forecasts, especially for small-scale rainfall maxima.
The problem
In more than four years of real-time forecasts, random forests based on HRRR rainfall forecasts underperformed a Global Ensemble Forecast System-based random forest, and neither matched the operational day-one excessive rainfall outlook; the HRRR version also underpredicted events.
A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.
What the scores mean
G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.
P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.
What this doesn’t fix
The HRRR-based system showed an underprediction bias, partly because verification used a more comprehensive excessive rainfall dataset than the one used for training.
Sources
1 on file- Artificial Intelligence for the Earth Systemsdoi:10.1175/aies-d-26-0005.1
Cite this record
v1Truvace Impact Record TRV-2026-2655, v1: “Progress toward forecasting excessive rainfall with random forests based on a deterministic convection-allowing model.” Truvace, 2026-11-12. /record/TRV-2026-2655 (accessed on the day you copy it). sha256 7d750c512561566b…
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