The first high-resolution meteorological forcing dataset for land process studies over China

Area of Science:

• Earth Science
• Atmospheric Science
• Climate Science

Background:

• Land surface process studies require high-quality, high-resolution meteorological data.
• Existing datasets often lack sufficient spatial-temporal detail or consistent quality for China.
• The development of specialized datasets is crucial for advancing regional climate research.

Purpose of the Study:

• To introduce the China Meteorological Forcing Dataset (CMFD), a novel gridded near-surface meteorological dataset.
• To provide a comprehensive dataset for land surface process studies in China.
• To validate the quality and utility of the CMFD compared to existing systems.

Main Methods:

• Fusion of remote sensing products, reanalysis datasets, and in-situ station data.
• Generation of a gridded dataset with 0.1° spatial and 3-hour temporal resolution.
• Inclusion of seven key meteorological variables: air temperature, surface pressure, specific humidity, wind speed, shortwave radiation, longwave radiation, and precipitation.

Main Results:

• The CMFD offers high spatial (0.1°) and temporal (3-hour) resolution data from 1979 to 2018.
• Validation shows CMFD quality surpasses the Global Land Data Assimilation System (GLDAS) due to more extensive station data.
• The dataset provides seven essential near-surface meteorological elements crucial for climate research.

Conclusions:

• The China Meteorological Forcing Dataset (CMFD) is a valuable, high-quality resource for land surface and climate studies in China.
• Its continuous temporal coverage and superior accuracy make it a preferred choice over other available datasets.
• CMFD facilitates more robust research into China's climate dynamics and land-atmosphere interactions.