Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method

被引：0

作者：

He, Jiaying ^{[1
]}

Loboda, Tatiana V ^{[1
]}

机构：

[1] Department of Geographical Sciences, University of Maryland, 2181 Samuel J. Lefrak Hall, 7251 Preinkert Drive, College Park,MD, United States

来源：

Environmental Research Letters | 2020年 / 15卷 / 11期

关键词：

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;

D O I：

115009

中图分类号：

学科分类号：

摘要：

引用

共 2 条

[1] Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method
He, Jiaying
Loboda, Tatiana, V
[J]. ENVIRONMENTAL RESEARCH LETTERS, 2020, 15 (11):
[2] PREDICTION OF CLOUD-TO-GROUND LIGHTNING THROUGH GAUSSIAN PROCESS REGRESSION WITH SATELLITE THERMAL INFRARED IMAGERY AND NUMERICAL WEATHER PREDICTION MODELING DATA
La Fata, Alice
Farina, Lorenzo
Bernardi, Marina
Moser, Gabriele
Procopio, Renato
Fiori, Elisabetta
[J]. IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 2508 - 2511

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