Authors:
Shibin Zheng
1
;
Chenwei Shen
2
and
Bin Li
2
Affiliations:
1
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
;
2
Dawning Information Industry Company Limited, Beijing, China
Keyword(s):
Bias Correction, CORDEX East Asia, Deep Learning, U-Net.
Abstract:
Regional climate models suffer from insufficient resolution and deficiencies in their dynamic processes, leading to systematic biases in surface air temperature simulations that require correction. In this research, a deep learning bias correction model, CE-MS-Unet, is proposed. This model incorporates multi-scale residual blocks and calendar month data to improve surface air temperature simulations of the REMO2015 regional climate model during the second phase of the Coordinated Regional Downscaling Experiment East Asia (CORDEX-EA-II) over mainland China. Experimental results indicate that, compared to Linear Scaling, Quantile Delta Mapping, and the deep learning model CU-net, CE-MS-Unet performs better in correcting climate averages and seasonal cycles, resulting in corrected data with greater overall agreement and improved spatial correlation. It effectively reduces biases and provides more accurate climate predictions. This study offers new insights and methods to improve the bia
s correction of temperature in regional climate models.
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