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Data · dataset · 2024

A deep learning method for bias correction of CMIP6 GCMs

Listed in ScienceDB

Given the important role of Atmospheric River precipitation (ARP) in the global hydrological cycle, accurate representation of ARP is significant.

Description

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).

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Where it is published

Catalogue records · 1

Topics

Inferred from text
Hydrology 70%
Provenance · 1 source records, 12 field assertions
SourceKeyLast seenRaw
ScienceDB10.57760/sciencedb.1848211 d agoJSON v1
FieldAssertionExtractorEvidence
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