A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature

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• 作者：TongRen Xu (1)
  ShaoMin Liu (1)
  ZiWei Xu (1)
  ShunLin Liang (2) (3)
  Lu Xu (1) (4)
  
  1. State Key Laboratory of Remote Sensing Science and School of Geography ; Beijing Normal University ; Beijing ; 100875 ; China
  2. State Key Laboratory of Remote Sensing Science ; and College of Global Change and Earth System Science ; Beijing Normal University ; Beijing ; 100875 ; China
  3. Department of Geographical Sciences ; University of Maryland ; College Park ; MD ; 20742 ; USA
  4. Information Technology Department ; National Library of China ; Beijing ; 100081 ; China
  
• 关键词：sensible heat flux ; latent heat flux ; ensemble Kalman filter ; common land model
• 刊名：Science China Earth Sciences
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：58
• 期：2
• 页码：211-230
• 全文大小：3,673 KB
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• 刊物主题：Earth Sciences, general;
• 出版者：Springer Berlin Heidelberg
• ISSN：1869-1897

文摘

In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 of the dual-pass data assimilation scheme optimizes the model vegetation parameters at the weekly temporal scale, and Pass 2 optimizes the soil moisture at the daily temporal scale. Based on ensemble Kalman filter (EnKF), the land surface temperature (LST) data derived from the new generation of Chinese meteorology satellite (FY3A-VIRR) are assimilated into common land model (CoLM) for the first time. Six sites, Daman, Guantao, Arou, BJ, Miyun and Jiyuan, are selected for the data assimilation experiments and include different climatological conditions. The results are compared with those from a dataset generated by a multi-scale surface flux observation system that includes an automatic weather station (AWS), eddy covariance (EC) and large aperture scintillometer (LAS). The results indicate that the dual-pass data assimilation scheme is able to reduce model uncertainties and improve predictions of surface flux with the assimilation of FY3A-VIRR LST data.