Evaluating statistical consistency for the ocean component of earth system models using physics informed convolutional autoencoder

Evaluating statistical consistency for the ocean component of earth system models using physics informed convolutional autoencoder

Abstract Model verification is a crucial step in the development and optimization of Earth system models (ESMs). Recently, a deep learning approach in evaluating statistical consistency for the atmosphere component of ESMs (A-ESM-DCT) has been proven effective in model verification. However, due to...

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Bibliographic Details
Main Authors: Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Dexun Chen, Yishuai Jin, Yang Gao, Xiaopei Lin, Zhao Liu, Xiaojing Lv, Yunlong Fei, Kaidi Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Consistency test approach for the ocean models
Physics-informed neural networks
Earth system model ensemble simulations
Online Access:https://doi.org/10.1038/s41598-025-03092-7
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