Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports
Output from a machine learning tool that assigns a probability that a severe thunderstorm wind report was caused by severe intensity wind was evaluated to understand counterintuitive cases where reports that had a high (low) wind speed received a low (high) diagnosed probability. Meteorological data...
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| Main Authors: | Elizabeth Tirone, William A. Gallus, Alexander J. Hamilton |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/16/7/881 |
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