Deep learning downscaled high-resolution daily near surface meteorological datasets over East Asia

Hai Lin; Jianping Tang; Shuyu Wang; Shuguang Wang; Guangtao Dong

doi:10.1038/s41597-023-02805-9

Deep learning downscaled high-resolution daily near surface meteorological datasets over East Asia

Sci Data. 2023 Dec 12;10(1):890. doi: 10.1038/s41597-023-02805-9.

Authors

Hai Lin^{1

2}, Jianping Tang^{3

4

5}, Shuyu Wang², Shuguang Wang^{1

2}, Guangtao Dong⁶

Affiliations

¹ Key Laboratory of Mesoscale Severe Weather/Ministry of Education, Nanjing University, Nanjing, 210023, China.
² School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China.
³ Key Laboratory of Mesoscale Severe Weather/Ministry of Education, Nanjing University, Nanjing, 210023, China. jptang@nju.edu.cn.
⁴ School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China. jptang@nju.edu.cn.
⁵ Key Laboratory of Citie's Mitigation and Adaptation to Climate Change in Shanghai, China Meteorological Administration, Shanghai, 200030, China. jptang@nju.edu.cn.
⁶ Key Laboratory of Citie's Mitigation and Adaptation to Climate Change in Shanghai, China Meteorological Administration, Shanghai, 200030, China.

Abstract

U-Net, a deep-learning convolutional neural network, is used to downscale coarse meteorological data. Based on 19 models from the Coupled Model Intercomparison Project Phase 6 and the Multi-Source Weather (MSWX) dataset, bias correction and UNet downscaling approaches are used to develop high resolution dataset over the East Asian region, referred to as Climate Change for East Asia with Bias corrected UNet Dataset (CLIMEA-BCUD). CLIMEA-BCUD provides nine meteorological variables including 2-m air temperature, 2-m daily maximum air temperature, 2-m daily minimum air temperature, precipitation, 10-m wind speed, 2-m relative humidity, 2-m specific humidity, downward shortwave radiation and downward longwave radiation with 0.1° horizontal resolution at daily intervals over the historical period of 1950-2014 and three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) of 2015-2100. Validation against MSWX indicates that CLIMEA-BCUD shows reasonable performance in terms of climatology, and it is capable of simulating seasonal cycles and future changes well. It is suggested that CLIMEA-BCUD can promote the application of deep learning in climate research in the areas of climate change, hydrology, etc.

Publication types

Dataset