Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 17, issue 4
Articles | Volume 17, issue 4
https://doi.org/10.5194/essd-17-1501-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-1501-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 4
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

Viewed

Total article views: 3,325 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,839 401 85 3,325 80 155
  • HTML: 2,839
  • PDF: 401
  • XML: 85
  • Total: 3,325
  • BibTeX: 80
  • EndNote: 155
Views and downloads (calculated since 24 Jun 2024)
Cumulative views and downloads (calculated since 24 Jun 2024)

Viewed (geographical distribution)

Total article views: 3,325 (including HTML, PDF, and XML) Thereof 3,238 with geography defined and 87 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Jan 2026
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint