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

作     者：XU TongRen LIU ShaoMin XU ZiWei LIANG ShunLin XU Lu 

作者机构：State Key Laboratory of Remote Sensing Science and School of Geography Beijing Normal University State Key Laboratory of Remote Sensing Science and College of Global Change and Earth System ScienceBeijing Normal University Department of Geographical Sciences University of Maryland College Park Information Technology DepartmentNational Library of China 

出 版 物：《Science China Earth Sciences》 (中国科学（地球科学英文版）)

年 卷 期：2015年第58卷第2期

页      面：211-230页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：funded by the National Natural Science Foundation of China(Grant Nos.91125002,41201330) the Fundamental Research Funds for the Central Universities the Special Foundation for Free Exploration of State Laboratory of Remote Sensing Science(Grant No.13ZY-06) 

主　　题：assimilation moisture latent weekly vegetation weather pixel covariance aperture Figure 

摘      要：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.