Abstract
Fires, including wildfires, harm air quality and essential public services like transportation, communication, and utilities. 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: ⩾ 2.54 cm) in the central US (CUS) over the 20-year period of 2001–20 using the machine learning (ML), Random Forest (RF), and Extreme Gradient Boosting (XGB) methods. The developed RF and XGB models demonstrate high accuracy (> 90%) and F1 scores of up to 0.78 in predicting large hail occurrences when WUS fires and CUS hailstorms coincide, particularly in four states (Wyoming, South Dakota, Nebraska, and Kansas). The key contributing variables identified from both ML models include 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). The results confirm a linkage between WUS fires and severe weather in the CUS, corroborating the findings of our previous modeling study conducted on case simulations with a detailed physics model.
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Acknowledgements
This paper is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research program as part of the Regional and Global Model Analysis and Multi-Sector Dynamics program areas (Award Number DE-SC0016605). Argonne National Laboratory is operated for the DOE by UChicago Argonne, LLC, under contract DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center (NERSC). NERSC is a U.S. DOE Office of Science User Facility operated under Contract DE-AC02-05CH11231.
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Author contributions Xinming LIN conducted the technical work. Jiwen FAN conceived the idea. Jiwen FAN and Z. Jason HOU guided the research. Yuwei ZHANG provided comments on technical details. All authors contributed to the writing of the manuscript.
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Article Highlights
• RF and XGB models are developed to establish connections between WUS fires and large hail occurrence in the CUS.
• The built ML models can make accurate predictions for large hail occurrence in several central US states.
• Meteorology and fire features in the western fire region are identified as important variables contributing to accurate prediction.
This paper is a contribution to the special issue on AI Applications in Atmospheric and Oceanic Science: Pioneering the Future.
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Lin, X., Fan, J., Zhang, Y. et al. Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms. Adv. Atmos. Sci. (2024). https://doi.org/10.1007/s00376-024-3198-7
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DOI: https://doi.org/10.1007/s00376-024-3198-7