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.
为弥补缺失农田灌溉资料对模型结果的影响,提高CoLM模型地表水热通量的估算精度,基于集合卡尔曼滤波算法,将表观热惯量(ATI)作为土壤水分的代表值,同化到CoLM(Common Land Model)模型中。选取黑河流域玉米下垫面的大满站,同化MODIS表观热惯量到模型中,将同化结果与模型估算结果、观测值相对比。结果显示:同化后得到的地表水热通量明显比模拟结果更加接近观测值,而MODIS表观热惯量数据的质量和数量也是影响同化结果精度的重要因素,表明表观热惯量的同化能够填补农田灌溉资料的缺失,改进模型地表水热通量的估算结果。
