Mapping near-surface air temperature, pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution

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• 作者：Tao Li (1)
  Xiaogu Zheng (1)
  Yongjiu Dai (1)
  Chi Yang (1)
  Zhuoqi Chen (1)
  Shupeng Zhang (1)
  Guocan Wu (1)
  Zhonglei Wang (1)
  Chengcheng Huang (1)
  Yan Shen (2)
  Rongwei Liao (2)
  
• 关键词：atmospheric forcing data ; land surface model ; thin ; plate smoothing spline ; kriging ; reanalysis data
• 刊名：Advances in Atmospheric Sciences
• 出版年：2014
• 出版时间：September 2014
• 年：2014
• 卷：31
• 期：5
• 页码：1127-1135
• 全文大小：575 KB
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• 作者单位：Tao Li (1)
  Xiaogu Zheng (1)
  Yongjiu Dai (1)
  Chi Yang (1)
  Zhuoqi Chen (1)
  Shupeng Zhang (1)
  Guocan Wu (1)
  Zhonglei Wang (1)
  Chengcheng Huang (1)
  Yan Shen (2)
  Rongwei Liao (2)
  
  1. College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China
  2. National Meteorological Information Center, China Meteorological Administration, Beijing, 100081, China
  
• ISSN：1861-9533

文摘

As part of a joint effort to construct an atmospheric forcing dataset for mainland China with high spatiotemporal resolution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km×1 km. The approach comprises two steps: (1) fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; (2) apply a simple kriging procedure to the residual for trend surface correction. The proposed approach is applied to observations collected at approximately 700 stations over mainland China. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets.