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<p>Hydroclimatic assessment in Xinjiang is constrained by sparse observations, complex terrain and systematic biases in widely used gridded and reanalysis products. Here we present the Three-Dimensional Variational Xinjiang Meteorological Forcing Dataset (3DVAR-MF-XJ), a 0.1° gridded near-surface dataset for 1961–2020 at hourly and daily resolution, including 2 m air temperature, relative humidity, 10 m wind speed, surface pressure, downward shortwave radiation and precipitation. 3DVAR-MF-XJ is generated using a hybrid framework in which regional modelling with three-dimensional variational data assimilation first produces dynamically consistent fields (3DVAR-XJ), followed by station-constrained bias correction on a common 0.1° grid. Evaluation against 30 withheld stations and independent benchmark products shows reduced mean and root-mean-square errors, higher correlations, and improved representation of precipitation over complex terrain and shortwave radiation at high elevations relative to ERA5, ERA5-Land and CN05.1. For precipitation, RMSE is reduced by 65.6%, 64.9% and 49.3% relative to ERA5, ERA5-Land and CN05.1, respectively. 3DVAR-MF-XJ provides a unified, well-documented forcing dataset for hydrological, ecological and land–atmosphere applications in arid Xinjiang.</p>

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A high-resolution near-surface meteorological forcing dataset for arid Xinjiang (3DVAR-MF-XJ)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Yang Xu,
  • Liang Zhang,
  • Hui Wang,
  • Dongge Ning,
  • Mengxin Bai,
  • Xueping Cong,
  • Zhixin Hao

摘要

Hydroclimatic assessment in Xinjiang is constrained by sparse observations, complex terrain and systematic biases in widely used gridded and reanalysis products. Here we present the Three-Dimensional Variational Xinjiang Meteorological Forcing Dataset (3DVAR-MF-XJ), a 0.1° gridded near-surface dataset for 1961–2020 at hourly and daily resolution, including 2 m air temperature, relative humidity, 10 m wind speed, surface pressure, downward shortwave radiation and precipitation. 3DVAR-MF-XJ is generated using a hybrid framework in which regional modelling with three-dimensional variational data assimilation first produces dynamically consistent fields (3DVAR-XJ), followed by station-constrained bias correction on a common 0.1° grid. Evaluation against 30 withheld stations and independent benchmark products shows reduced mean and root-mean-square errors, higher correlations, and improved representation of precipitation over complex terrain and shortwave radiation at high elevations relative to ERA5, ERA5-Land and CN05.1. For precipitation, RMSE is reduced by 65.6%, 64.9% and 49.3% relative to ERA5, ERA5-Land and CN05.1, respectively. 3DVAR-MF-XJ provides a unified, well-documented forcing dataset for hydrological, ecological and land–atmosphere applications in arid Xinjiang.