The long-term and reliable meteorological reanalysis dataset with high spatial–temporal resolution is crucial for various hydrological and meteorological applications, especially in regions or periods with scarce in situ observations and with limited open-access data.
Based on the fifth-generation reanalysis dataset (ERA5, produced by the European Centre for Medium-Range Weather Forecasts, 0.25°×0.25°, since 1940) and CLDAS (China Meteorological Administration Land Data Assimilation System, 0.0625°×0.0625°, since 2008), we propose a novel downscaling method Geopotential-guided Attention Network (GeoAN), leveraging the high spatial resolution of CLDAS and the extended historical coverage of ERA5, and produce the daily multi-variable (2 m temperature, surface pressure, and 10 m wind speed) meteorological dataset MDG625. MDG625 (0.0625° Meteorological Dataset derived by GeoAN) covers most of Asia from 0.125° S to 64.875° N and 60.125 to 160.125° E, and contains data starting in 1940. Compared with other downscaling methods, GeoAN shows better performance with R2 (2 m temperature, surface pressure, and 10 m wind speed reach 0.990, 0.998, and 0.781, respectively).
MDG625 demonstrates superior continuity and consistency from both spatial and temporal perspectives. We anticipate that the GeoAN method and this dataset, MDG625, will aid in climate studies of Asia and will contribute to improving the accuracy of reanalysis products from the 1940s.
The data is sourced from the ESSD website（ https://essd.copernicus.org/articles/17/1501/2025/ ）.
Based on ERA5 and CLDAS, a Potential Energy Guided Attention Network (GeoAN) method is proposed, which utilizes the high spatial resolution of CLDAS and the extended historical coverage of ERA5 to generate a daily multivariate (2m temperature, surface pressure, and 10m wind speed) meteorological dataset MDG625.
The data quality is good.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
https://cstr.cn/31253.11.sciencedb.17408
10.57760/sciencedb.17408
