TC-GEN: Data-Driven Tropical Cyclone Downscaling Using Machine Learning-Based High-Resolution Weather Model

被引：0
|
作者：
Jing, Renzhi [1 ,2 ]
Gao, Jianxiong [3 ]
Cai, Yunuo [4 ]
Xi, Dazhi [5 ]
Zhang, Yinda [6 ]
Fu, Yanwei [4 ]
Emanuel, Kerry [7 ]
Diffenbaugh, Noah S. [1 ,8 ,9 ]
Bendavid, Eran [2 ,10 ]
机构：
[1] Woods Institute for the Environment, Stanford University, Stanford,CA, United States
[2] School of Medicine, Stanford University, Stanford,CA, United States
[3] Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
[4] School of Data Science and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
[5] Department of Civil and Environmental Engineering, Princeton University, Princeton,NJ, United States
[6] Google LLC, Mountain View,CA, United States
[7] Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge,NA, United States
[8] Department of Earth System Science, Stanford University, Stanford,CA, United States
[9] Doerr School of Sustainability, Stanford University, Stanford,CA, United States
[10] Department of Health Policy, Stanford University, Stanford,CA, United States
来源：
Journal of Advances in Modeling Earth Systems | 2024年 / 16卷 / 10期
关键词：
All Open Access; Gold; Green;
D O I：
10.1029/2023MS004203
中图分类号：
学科分类号：
摘要：
Risk assessment
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