2026-10-13T22:09:16Z https://www.scidb.cn/oai

10.57760/sciencedb.18482 2024-06-24T15:44:30Z

2024-06-24 A deep learning method for bias correction of CMIP6 GCMs doi:10.57760/sciencedb.18482 en 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). Deep learning; CMIP6; Atmospheric rivers Tian Yuan PUBLIC https://api.github.com/licenses/unlicense dataset Science Data Bank