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TRUVACE RECORD VERSION
record: TRV-2026-2655
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T16:57:14.991599Z
status: published
lens: trace
sector: climate
headline: Progress toward forecasting excessive rainfall with random forests based on a deterministic convection-allowing model
dek: 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…
gain_title: 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.
problem_title: 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.
trace_subject: Random forest excessive rainfall forecasting based on HRRR
gain_reading: 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.
gain_evidence: forecasts are significantly improved when the system uses data from all nearby model gridpoints, rather than just sparse input points | Forecasts are also improved when predictors are averaged across multiple HRRR initializations
problem_reading: 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.
problem_evidence: RFs based on deterministic convection allowing models continue to underperform relative to a Global Ensemble Forecast System based RF | neither RF is as skillful as the operational excessive rainfall outlook for the day one period
quick_read: Researchers tested how to design a random forest system that predicts excessive rainfall from the High-Resolution Rapid Refresh (HRRR) weather model. Using data from all nearby gridpoints and averaging predictors across several model runs significantly improved forecasts, particularly for events with small-scale rainfall peaks.

The system was evaluated during the Flash Flood and Intense Rainfall (FFaIR) Experiment over three consecutive years and showed year-over-year improvement. Over more than four years of real-time forecasts, however, it still underperformed a random forest built on the Global Ensemble Forecast System, and neither matched the operational day-one excessive rainfall outlook.

The finding shows that machine-learning post-processing of single high-resolution models has not yet overtaken existing forecast products for this task. The HRRR-based system also tended to underpredict excessive rainfall, partly because verification used a more comprehensive event dataset than training did.
limitation: The HRRR-based system showed an underprediction bias, partly because verification used a more comprehensive excessive rainfall dataset than the one used for training.
tag: Dual reading
key_points: A random forest trained on HRRRv3 and applied to daily HRRRv4 forecasts did not significantly lose accuracy. | Forecasts improved significantly when the system used all nearby gridpoints instead of sparse input points. | Averaging predictors across multiple HRRR initializations improved forecasts because the random forest could learn from lower-uncertainty inputs. | The system was evaluated in the FFaIR Experiment over three consecutive years, with year-over-year improvement. | The HRRR-based random forest underpredicted excessive rainfall, partly because verification used a broader event dataset than training. | Over more than four years of real-time forecasts, it trailed a Global Ensemble Forecast System-based random forest and the operational day-one outlook.
rundown: The work builds on earlier research using machine learning with high-resolution numerical weather prediction to forecast high-impact weather. The team ran sensitivity experiments on how to design the system, using the deterministic HRRR model as the source of predictors.

Two design choices mattered most: spatial aggregation of predictors across nearby gridpoints helped most for events with small-scale precipitation maxima, and time-lagged ensemble mean predictors from multiple HRRR initializations reduced uncertainty the random forest had to learn around.

Real-time evaluation covered more than four years and included the FFaIR Experiment across three consecutive years, where the system improved each year. Even so, it remained less skillful than a Global Ensemble Forecast System random forest and the operational day-one excessive rainfall outlook.
sources:
- peer_reviewed | Artificial Intelligence for the Earth Systems | https://doi.org/10.1175/aies-d-26-0005.1 | 2026-10-26
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