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

Journal Article · · Advances in Atmospheric Sciences
 [1];  [2];  [1]; ORCiD logo [1]
  1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
  2. Argonne National Laboratory (ANL), Argonne, IL (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
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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: $$\geqslant$$ 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). Importantly, 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.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE
Grant/Contract Number:
AC02-06CH11357; AC02-05CH11231; SC0016605; AC05-76RL01830
OSTI ID:
2337515
Alternate ID(s):
OSTI ID: 2426425
Report Number(s):
PNNL-SA-201265; 184313
Journal Information:
Advances in Atmospheric Sciences, Vol. 41; ISSN 0256-1530
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

54 ENVIRONMENTAL SCIENCES
Hailstorm
Machine Learning
Severe Convective Storm
Wildfire