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The Impact of Target Dataset Choice on Machine Learning Prediction of Severe Hail, Wind, and Tornadoes
- Truvace record
- TRV-2026-2543raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
- sha256
- 1c4557399f41…check it
Abstract Supervised machine learning (ML) methods implicitly assume that their target dataset is error free. For severe weather prediction, point-based observed Storm Data storm reports that have been expanded in space and time are commonly used targets. However, other target datasets could potentially better represent severe weather occurrence, especially for situations requiring more precision. This study analyzes 1-h probabilistic severe weather hazard forecasts made at lead times from 0 to 3 h from random fo…
The problem
In a comparison of random forest models, severe weather forecasts trained on National Weather Service warnings scored notably worse when checked against storm reports than report-trained models checked against warnings.
What this doesn’t fix
Verification results depend on which dataset is used as truth, since the two target datasets have different spatial and temporal climatologies, so neither model clearly outperforms the other overall.
Sources
1 on file- Artificial Intelligence for the Earth Systemsdoi:10.1175/aies-d-26-0004.1
Cite this record
v1Truvace Impact Record TRV-2026-2543, v1: “The Impact of Target Dataset Choice on Machine Learning Prediction of Severe Hail, Wind, and Tornadoes.” Truvace, 2026-11-12. /record/TRV-2026-2543 (accessed on the day you copy it). sha256 1c4557399f410bc5…
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