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Evaluating the Skill of Medium-Range Machine Learning–Based Convective Hazard Forecasts Derived from Convection-Allowing Ensembles
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- TRV-2026-2601raw data (JSON)
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- v1Published
- First published
- Evidence type
- Controlled or validated comparison
- Sources
- 1 sourcePeer-reviewed
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Abstract We assess the skill of daily machine learning (ML)-based probabilistic convective hazard predictions generated from two experimental medium-range (i.e., extending to day 8) convection-allowing ensemble (CAE) systems to determine if such predictions are skillful over the CONUS. CAE forecasts were generated in real time for events in spring 2023 and 2024, and convective hazard predictions were produced using neural networks, trained with CAE output and observed storm reports (i.e., reports of tornadoes, h…
The gain
In a study of real-time forecasts from spring 2023 and 2024 over the continental US, machine learning hazard forecasts built from convection-allowing ensembles outperformed both updraft-helicity forecasts and climatology through day 8.
Sources
1 on file- Weather and Forecastingdoi:10.1175/waf-d-25-0243.1
Cite this record
v1Truvace Impact Record TRV-2026-2601, v1: “Evaluating the Skill of Medium-Range Machine Learning–Based Convective Hazard Forecasts Derived from Convection-Allowing Ensembles.” Truvace, 2026-11-12. /record/TRV-2026-2601 (accessed on the day you copy it). sha256 9084e3d3af6b496a…
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