土壤水分同化系统的敏感性试验研究

利用1998年7月6日至8月9日青藏高原GAME-Tibet试验区MS3608站点的4cm、20cm和100cm的土壤水分观测数据同化SiB2模型输出的表层、根区和深层土壤水分,探讨了一个基于集合卡尔曼滤波和简单生物圈模型的单点土壤水分同化方案。分析和评价了集合大小、同化周期、模型误差、背景场误差以及观测误差对同化系统性能的影响。结果表明:①增加集合数目可以减小土壤水分同化系统的误差,但同时又降低了运行效率;②对于集合卡尔曼滤波,初始场的估计是否准确对同化系统性能影响不大;③模型误差和观测误差的准确估计可以提高土壤水分的估计精度;④利用数据同化的方法对土壤水分的估计有显著提高。

Sensitivity analysis on land data assimilation scheme of soil moisture

We develop a one-dimensional land data assimilation scheme based on the ensemble Kalman filter (EnKF) and simple biosphere model (SiB2).In order to evaluate the performance of the assimilation system, we do some assimilation experiments, using GAME-Tibet observation data from July 6 to August 9 in 1998, at the MS3608 site on the Tibetan plateau.When the current observations, in situ observations of soil moisture at the depth of 4,20 and 100 cm are assimilated into land surface model (SiB2), the best estimations of soil moisture at the surface layer, the root zone and the deep layer are calculated.We also analyze the influence of the ensemble size, the assimilation cycle, the model error, the background error and the observation error on the assimilation system.The results indicate: (1) The error in assimilation system can be reduced by increasing the ensemble members, but it will make the operation efficiency lower; (2) As for EnKF, it is unimportant for the performance of the system whether the estimation of the background is accurate; (3) The accurate estimation of the model error and the observation error can decrease the soil moisture error in the surface layer, the root zone and the deep layer; And (4) the estimation of soil moisture can be improved by using the data assimilation method.