Articles | Volume 17, issue 4
https://doi.org/10.5194/essd-17-1501-2025
https://doi.org/10.5194/essd-17-1501-2025
Data description paper
 | 
11 Apr 2025
Data description paper |  | 11 Apr 2025

MDG625: a daily high-resolution meteorological dataset derived by a geopotential-guided attention network in Asia (1940–2023)

Zijiang Song, Zhixiang Cheng, Yuying Li, Shanshan Yu, Xiaowen Zhang, Lina Yuan, and Min Liu

Data sets

MDG625: Meteorological Dataset with 0.0625° resolution produced by GeoAN Zijiang Song et al. https://doi.org/10.57760/sciencedb.17408

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

Model code and software

songzijiang/GeoAN: Initial version Zijiang Song https://doi.org/10.5281/zenodo.15175242

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Short summary
It is hard to access long-time series and high-resolution meteorological data for past years. In this paper, we propose the Geopotential-guided Attention Network (GeoAN) for downscaling which can produce high-resolution data using given low-resolution data. Quantitative and visual comparisons reveal our GeoAN produces better results with regard to most metrics. Using GeoAN, a historical meteorological dataset called MDG625 has been produced daily for the period since 1940.
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