A deep learning method for bias correction of CMIP6 GCMs
收藏科学数据银行2024-06-24 更新2026-04-23 收录
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资源简介：
Given the important role of Atmospheric River precipitation (ARP) in the global hydrological cycle, accurate representation of ARP is significant. However, general circulation models (GCMs) demonstrate bias in simulating ARP. The target of this method is to improve ARP estimation of CMIP6 using Cycle-Consistent Generative Adversarial Networks (CycleGAN).
提供机构：
Beijing Normal University
创建时间：
2024-04-27
