A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles |
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| Authors: | ZHANG Shuwen LI Deqin and QIU Chongjian |
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| Institution: | Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province, Lanzhou 730000,Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000,Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 |
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| Abstract: | With the combination of three land surface models (LSMs) and the ensemble
Kalman filter (EnKF), a multimodel EnKF is proposed in which the multimodel
background superensemble error covariance matrix is estimated by two
different algorithms: the Simple Model Average (SMA) and the Weighted
Average Method (WAM). The two algorithms are tested and compared in terms of
their abilities to retrieve the true soil moisture profile by respectively
assimilating both synthetically-generated and actual near-surface soil
moisture measurements. The results from the synthetic experiment show that
the performances of the SMA and WAM algorithms were quite different. The SMA
algorithm did not help to improve the estimates of soil moisture at the deep
layers, although its performance was not the worst when compared with the
results from the single-model EnKF. On the contrary, the results from the
WAM algorithm were better than those from any single-model EnKF. The tested
results from assimilating the field measurements show that the performance
of the two multimodel EnKF algorithms was very stable compared with the
single-model EnKF. Although comparisons could only be made at three shallow
layers, on average, the performance of the WAM algorithm was still slightly
better than that of the SMA algorithm. As a result, the WAM algorithm should
be adopted to approximate the multimodel background superensemble error
covariance and hence used to estimate soil moisture states at the relatively
deep layers. |
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| Keywords: | multimodel EnKF soil moisture land data assimilation land surface model |
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