Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms  

作　　者：Xinming LIN Jiwen FAN Yuwei ZHANG ZJason HOU 

机构地区：[1]Pacific Northwest National Laboratory,Richland,WA 99354,USA [2]Argonne National Laboratory,Lemont,IL 60439,USA

出　　处：《Advances in Atmospheric Sciences》2024年第7期1450-1462,共13页大气科学进展（英文版）

基　　金：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;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.

摘　　要：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.

关 键 词：WILDFIRE severe convective storm HAILSTORM machine learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] P426.64[自动化与计算机技术—控制科学与工程]