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Development of a Chinese land data assimilation system:its progress and prospects

Progress in Natural Science:Materials International 2007年第8期17卷 881-892页

作者： Li Xin~(1**) Huang Chunlin~1 Che Tao~1 Jin Rui~1 Wang Shugong~l Wang Jiemin~1 Gao Feng~1 Zhang Shuwen~2 Qiu Chongjian~2 Wang Chenghai~2 (1.Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences,Lanzhou 730000,China 2.College of Atmospheric Sciences,Lanzhou University,Lanzhou 730000,China) Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences Lanzhou 730000 China College of Atmospheric Sciences Lanzhou University Lanzhou 730000 China

The objective of land data assimilation is to merge multi-source observations into the dynamics of land surface model for improving the estimation of land surface states.We have developed a land data assimilation system for China’s land territory.In this sys- tem,the Common land Model and Simple Biosphere Model 2 are used to simulate land surface processes.The radiative transfer models of thawed and frozen soil,snow,lake,and vegetation are used as observation operators to transfer model predictions into estimated bright- ness temperatures.A Monte-Carlo based sequential filter,the ensemble Kalman filter,is implemented as data assimilation method to inte- grate modeling and observation.The system is capable of assimilating passive microwave remotely sensed data such as special sensor mi- crowave/imager (SSM/I),TRMM microwave imager (TMI),and advanced microwave scanning radiometer enhanced for EOS (AMSR- E) and the conventional in situ measurements of soil and snow.A spatiotemporally consistent assimilated dataset for soil moisture,soil temperature,snow and frozen soil,with a spatial resolution of 0.25 degree and temporal resolution of one hour,has been produced.This paper introduces the development of Chinese land data assimilation system and the progress made on data assimilation algorithms,land sur- face modeling,microwave remote sensing of land surface hydrological variables,and the preparation of atmospheric forcing data.The dis- tinct characteristics and challenges of developing land data assimilation system and the perspectives for future development are also dis- cussed.

关键词： land data assimilation land surface model passive microwave remote sensing Kalman filter

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A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles

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Advances in Atmospheric Sciences 2011年第1期28卷 195-206页

作者：张述文李得勤邱崇践 Key Laboratory for Semi-Arid Climate Change of the Ministry of Education College of Atmospheric Sciences Lanzhou University Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province

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.

关键词： multimodel EnKF soil moisture land data assimilation land surface model

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Estimating the Soil Moisture Profile by Assimilating Near-Surface Observations with the Ensemble Kalman Filter (EnKF)

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Advances in Atmospheric Sciences 2005年第6期22卷 936-945页

作者：张述文李吴睿张卫东邱崇践李新 School of Atmospheric Sciences Lanzhou University Lanzhou 730000 Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province Lanzhou 7300203 Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province Lanzhou 730020 School of Physical Sciences and Technology Lanzhou University Lanzhou 730000 Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences Lanzhou 730000

The paper investigates the ability to retrieve the true soil moisture profile by assimilating near-surface soil moisture into a soil moisture model with an ensemble Kalman filter （EnKF） assimilation scheme, including the effect of ensemble size, update interval and nonlinearities in the profile retrieval, the required time for full retrieval of the soil moisture profiles, and the possible influence of the depth of the soil moisture observation. These questions are addressed by a desktop study using synthetic data. The ＂true＂ soil moisture profiles are generated from the soil moisture model under the boundary condition of 0.5 cm d^-1 evaporation. To test the assimilation schemes, the model is initialized with a poor initial guess of the soil moisture profile, and different ensemble sizes are tested showing that an ensemble of 40 members is enough to represent the covariance of the model forecasts. Also compared are the results with those from the direct insertion assimilation scheme, showing that the EnKF is superior to the direct insertion assimilation scheme, for hourly observations, with retrieval of the soil moisture profile being achieved in 16 h as compared to 12 days or more. For daily observations, the true soil moisture profile is achieved in about 15 days with the EnKF, but it is impossible to approximate the true moisture within 18 days by using direct insertion. It is also found that observation depth does not have a significant effect on profile retrieval time for the EnKF. The nonlinearities have some negative influence on the optimal estimates of soil moisture profile but not very seriously.

关键词： soil moisture ensemble Kalman filter insertion land data assimilation

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