气候变化领域集成服务门户(CCPortal): Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method

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DOI	10.1088/1748-9326/abbc3b
	Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method
	He J.; Loboda T.V.
发表日期	2020
ISSN	17489318
卷号	15 期号:11
英文摘要	Wildland fires exert substantial impacts on tundra ecosystems of the high northern latitudes (HNL), ranging from biogeochemical impact on climate system to habitat suitability for various species. Cloud-to-ground (CG) lightning is the primary ignition source of wildfires. It is critical to understand mechanisms and factors driving lightning strikes in this cold, treeless environment to support operational modeling and forecasting of fire activity. Existing studies on lightning strikes primarily focus on Alaskan and Canadian boreal forests where land-atmospheric interactions are different and, thus, not likely to represent tundra conditions. In this study, we designed an empirical-dynamical method integrating Weather Research and Forecast (WRF) simulation and machine learning algorithm to model the probability of lightning strikes across Alaskan tundra between 2001 and 2017. We recommended using Thompson 2-moment and Mellor-Yamada-Janjic schemes as microphysics and planetary boundary layer parameterizations for WRF simulations in the tundra. Our modeling and forecasting test results have shown a strong capability to predict CG lightning probability in Alaskan tundra, with the values of area under the receiver operator characteristics curves above 0.9. We found that parcel lifted index and vertical profiles of atmospheric variables, including geopotential height, dew point temperature, relative humidity, and velocity speed, important in predicting lightning occurrence, suggesting the key role of convection in lightning formation in the tundra. Our method can be applied to data-scarce regions and support future studies of fire potential in the HNL. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	Alaskan tundra; cloud-to-ground lightning; empirical-dynamic modeling; lightning-ignited wildfire; random forest; Weather Research and Forecast (WRF)
语种	英语
scopus关键词	Atmospheric humidity; Boundary layer flow; Boundary layers; Clouds; Landforms; Learning algorithms; Lightning; Machine learning; Atmospheric interaction; Canadian boreal forest; Cloud-to-ground lightning; Machine learning methods; Modeling and forecasting; Planetary boundary layers; Receiver operator characteristics curves; Weather Research and Forecast models; Weather forecasting; atmospheric modeling; climate modeling; cloud to ground lightning; machine learning; probability; tundra; weather forecasting; Alaska; United States
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153494
作者单位	Department of Geographical Sciences, University of Maryland, 2181 Samuel J. Lefrak Hall, 7251 Preinkert Drive, College Park, MD, United States
推荐引用方式 GB/T 7714	He J.,Loboda T.V.. Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method[J],2020,15(11).
APA	He J.,&Loboda T.V..(2020).Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method.Environmental Research Letters,15(11).
MLA	He J.,et al."Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method".Environmental Research Letters 15.11(2020).