Correcting Land Surface Model Predictions for the Impact of Temporally Sparse Rainfall Rate Measurements Using an Ensemble Kalman Filter and Surface Brightness Temperature Observations
Correcting Land Surface Model Predictions for the Impact of Temporally Sparse Rainfall Rate Measurements Using an Ensemble Kalman Filter and Surface Brightness Temperature Observations
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DOI：
10.1175/1525-7541(2003)004
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发表时间：
2003-10
影响因子：
3.8
通讯作者：
W. Crow
中科院分区：
文献类型：
--
作者：
W. Crow
摘要目前使用星载辐射计测量短期（<1个月）降雨累积量的尝试的特点是与地球仪上任何一点的低观测频率（每天两到八次测量）相关的大采样误差。这降低了空间降雨反演对监测地表水和能量平衡过程的价值。在这里，数据同化系统，通过集合卡尔曼滤波（EnKF）同化的表面L-波段亮温（TB）观测的基础上，引入到纠正不良采样降雨对陆面模型预测的根区土壤水分和地表能量通量的影响。1997年4月1日至1998年3月31日期间，在美国南部大平原的两个地点对该系统进行了评估。该评估包括一个数据同化实验，综合产生的TB测量的基础上，和同化的真实的TB观测期间获得的19...
Abstract Current attempts to measure short-term (<1 month) rainfall accumulations using spaceborne radiometers are characterized by large sampling errors associated with low observation frequencies for any single point on the globe (from two to eight measurements per day). This degrades the value of spaceborne rainfall retrievals for the monitoring of surface water and energy balance processes. Here a data assimilation system, based on the assimilation of surface L-band brightness temperature (TB) observations via the ensemble Kalman filter (EnKF), is introduced to correct for the impact of poorly sampled rainfall on land surface model predictions of root-zone soil moisture and surface energy fluxes. The system is evaluated during the period from 1 April 1997 to 31 March 1998 over two sites within the U.S. Southern Great Plains. This evaluation includes both a data assimilation experiment, based on synthetically generated TB measurements, and the assimilation of real TB observations acquired during the 19...