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Machine Learning Analysis of Western US Fire Impacts on Hailstorms in the Central US

Presentation Date
Wednesday, December 13, 2023 at 2:10pm - Wednesday, December 13, 2023 at 6:30pm
MC - Poster Hall A-C - South



Fires including wildfires have detrimental effects on air quality and various essential services, including transportation, communication, and utilities such as power, gas, and water supply. These fires can also influence atmospheric conditions, including temperature and aerosols, potentially affecting severe convective storms. Here we investigate the remote impacts of fires in western United States (WUS) on the occurrence of large hail (size >=1 inch) in central US (CUS) over the 20-year period (2001- 2020) using machine learning (ML) methods. We develop random forest (RF) and eXtreme Gradient Boosting (XGB) classification models to establish connections between fire characteristics in the WUS and the occurrence of large hail in the CUS. Additionally, we identify key variables that contribute to the occurrence of large hail. Both RF and XGB models demonstrate high accuracy in predicting large hail occurrences when WUS fires and CUS hailstorms coincide, particularly in states such as South Dakota (SD) and Nebraska (NE), achieving model accuracy rates exceeding 90% and F1-scores of up to 0.78. The variable rankings from both models show that temperature and relative humidity in the fire region as well as westerly winds responsible for transporting moisture and aerosols from the WUS to CUS, are the most influential factors. Notably, these results obtained from the analysis of the long-term data align with the results of our previous modeling study conducted on the case simulations.

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