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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MDPI AG
2025-07-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/7/881 |
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| author | Elizabeth Tirone William A. Gallus Alexander J. Hamilton |
| author_facet | Elizabeth Tirone William A. Gallus Alexander J. Hamilton |
| author_sort | Elizabeth Tirone |
| collection | DOAJ |
| description | 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 for these cases was compared to that for valid cases where the machine learning probability seemed consistent with the observed severity of the winds. The comparison revealed that the cases with high winds but low probabilities occurred in less conducive environments for severe wind production (less instability, greater low-level relative humidity, weaker lapse rates) than in the cases where high winds occurred with high probabilities. Cases with a low speed but a high probability had environmental characteristics that were more conducive to producing severe wind. These results suggest that the machine learning model is assigning probabilities based on storm modes that more often have measured severe wind speeds (i.e., clusters of cells and bow echoes), and counterintuitive values may reflect events where storm interactions or other smaller-scale features play a bigger role. In addition, some evidence suggests improper reporting may be common for some of these counterintuitive cases. |
| format | Article |
| id | doaj-art-feea17fb527842db8ec960ef017cc159 |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-feea17fb527842db8ec960ef017cc1592025-08-20T03:35:27ZengMDPI AGAtmosphere2073-44332025-07-0116788110.3390/atmos16070881Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind ReportsElizabeth Tirone0William A. Gallus1Alexander J. Hamilton2Department of Earth, Atmosphere, and Climate, Iowa State University, Ames, IA 50011, USADepartment of Earth, Atmosphere, and Climate, Iowa State University, Ames, IA 50011, USADepartment of Earth, Atmosphere, and Climate, Iowa State University, Ames, IA 50011, USAOutput 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 for these cases was compared to that for valid cases where the machine learning probability seemed consistent with the observed severity of the winds. The comparison revealed that the cases with high winds but low probabilities occurred in less conducive environments for severe wind production (less instability, greater low-level relative humidity, weaker lapse rates) than in the cases where high winds occurred with high probabilities. Cases with a low speed but a high probability had environmental characteristics that were more conducive to producing severe wind. These results suggest that the machine learning model is assigning probabilities based on storm modes that more often have measured severe wind speeds (i.e., clusters of cells and bow echoes), and counterintuitive values may reflect events where storm interactions or other smaller-scale features play a bigger role. In addition, some evidence suggests improper reporting may be common for some of these counterintuitive cases.https://www.mdpi.com/2073-4433/16/7/881machine learningthunderstormssevere wind |
| spellingShingle | Elizabeth Tirone William A. Gallus Alexander J. Hamilton Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports Atmosphere machine learning thunderstorms severe wind |
| title | Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports |
| title_full | Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports |
| title_fullStr | Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports |
| title_full_unstemmed | Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports |
| title_short | Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports |
| title_sort | exploring the explainability of a machine learning tool to improve severe thunderstorm wind reports |
| topic | machine learning thunderstorms severe wind |
| url | https://www.mdpi.com/2073-4433/16/7/881 |
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