作者:Xu, TR (Xu TongRen)[ 1,2 ] ; Liu, SM (Liu ShaoMin)[ 1,2 ] ; Xu, ZW (Xu ZiWei)[ 1,2 ] ; Liang, SL (Liang ShunLin)[ 1,3,4 ] ; Xu, L (Xu Lu)[ 1,2,5 ]

SCIENCE CHINA-EARTH SCIENCES

卷: 58  期: 2  页: 211-230

DOI: 10.1007/s11430-014-4964-7

出版年: FEB 2015

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.

通讯作者地址: Liu, SM (通讯作者)

Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China

[ 3 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China

[ 4 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA

[ 5 ] Natl Lib China, Dept Informat Technol, Beijing 100081, Peoples R China