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Relative Robustness of Machine Learning (ML) Postprocessing Schemes in Forecasting Wintertime Precipitation Types: A CONUS-Wide Comparison
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- TRV-2026-2702raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Controlled or validated comparison
- Sources
- 1 sourcePeer-reviewed
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- dca3712400da…check it
Abstract Accurate forecasts of precipitation type (p-type), rain (RN), snow (SN), ice pellets (IP), and freezing rain (FZRA) are essential for managing winter weather impacts. Operational p-type products, diagnosed from numerical weather prediction (NWP) models using physical schemes, perform reliably for common classes but fail to capture minority classes (IP/FZRA). This study evaluates three machine learning (ML) techniques, such as multinomial logistic regression (MLR), artificial neural network (ANN), and qu…
55Gain side
How the two sides scoreClose call
55Problem side
The gain
In a test on November–March 2017–19 data across the contiguous US, a multinomial logistic regression postprocessing model beat the operational GEFS forecast for ice pellets in both accuracy and equitable threat score, while GEFS showed negligible skill.
The problem
In a contiguous-US test of machine-learning postprocessing of GEFS winter forecasts, all models were poorly calibrated for ice pellets and freezing rain, giving high probabilities to events that occurred far less often.
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 ML models overpredicted ice pellets and freezing rain, and the observations came from crowdsourced mPING reports that can be spurious and inconsistent. The baseline also ran at coarser resolution than the ML predictors.
Sources
1 on file- Weather and Forecastingdoi:10.1175/waf-d-25-0205.1
Cite this record
v1Truvace Impact Record TRV-2026-2702, v1: “Relative Robustness of Machine Learning (ML) Postprocessing Schemes in Forecasting Wintertime Precipitation Types: A CONUS-Wide Comparison.” Truvace, 2026-11-12. /record/TRV-2026-2702 (accessed on the day you copy it). sha256 dca3712400daeb7e…
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