美国阿贡国家实验室Jiwen Fan及其团队成员，在研究美国西部火灾对中部冰雹影响的机器学习分析中取得新进展。该项研究成果发表在2024年2月1日出版的《大气科学进展》上。

研究人员使用机器学习(ML)方法、随机森林(RF)和极端梯度增强(XGB)，研究了20年间(2001-2020)美国西部火灾对美国中部大冰雹(尺寸>=1英寸)发生的远程影响。当美国西部火灾和美国中部冰雹同时发生时，特别是在四个州(即WY, SD, NE和KS)，开发的随机森林和极端梯度增强模型在预测大冰雹发生率方面显示出较高的准确性(>90%)和高达0.78 F1分数。

两个机器学习模型中确定的关键影响变量包括火区的气象变量(温度和湿度)，烟流传输路径上的西风，以及火灾特征(即最大火灾功率和燃烧面积)。结果证实了美国西部火灾与美国中部恶劣天气之间的联系，和研究人员之前在案例模拟中进行的建模研究的结果。

据介绍，包括野火在内的火灾会损害空气质量和交通、通信和公用事业等基本公共服务。这些火灾还会影响大气条件，包括温度和气溶胶，可能会影响强对流风暴。

附：英文原文

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

Author: Xinming Lin, Jiwen Fan, Yuwei Zhang, Z. Jason Hou

Issue&Volume: 2024-02-01

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 >=1 inch) in central US (CUS) over the 20-year period (2001- 2020) using machine learning (ML) methods, Random Forest (RF) and Extreme Gradient Boosting (XGB). 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 (i.e., WY, SD, NE, and KS). 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 the case simulations.

DOI: 10.1007/s00376-024-3198-7

Source: http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-024-3198-7viewType=HTML