作者: TongRen Xu , ShaoMin Liu , ZiWei Xu , ShunLin Liang , Lu Xu
DOI: 10.1007/S11430-014-4964-7
关键词:
摘要: In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 the optimizes model vegetation parameters at weekly temporal scale, and 2 soil moisture daily scale. Based on ensemble Kalman filter (EnKF), land temperature (LST) derived from new generation Chinese meteorology satellite (FY3A-VIRR) are assimilated into common (CoLM) for first time. Six sites, Daman, Guantao, Arou, BJ, Miyun Jiyuan, selected experiments include different climatological conditions. The results compared with those dataset generated by multi-scale flux observation system that includes an automatic weather station (AWS), eddy covariance (EC) large aperture scintillometer (LAS). indicate able reduce uncertainties FY3A-VIRR LST data.