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

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 the 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 examine the connections between fire features in the WUS and the occurrence of large hail in the CUS and identify key contributing variables. Both RF and XGB models demonstrate high accuracy in predicting large hail occurrences when WUS fires and CUS hailstorms coincide, particularly in four states, achieving model accuracy rates exceeding 90% and F1-scores of up to 0.78. The variable rankings from both models the ML results indicate that the meteorological variables in the fire region (temperature and moisture), the westerly wind over the plume transport path, and the fire features (i.e., the maximum fire power and burned area) are important contributing factors. The results confirm a linkage between WUS fires and severe weather in the CUS, corroborating the findings of our previous modeling study conducted on the case simulations.