标题：Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation作者：Chen, Weijing; Huang, Chunlin; Shen, Huanfeng; Li, Xin

来源出版物：ADVANCES IN WATER RESOURCES 卷：86 页：425-438 DOI：10.1016/j.advwatres.2015.08.003 出版年：DEC 2015

摘要：Model parameters are a source of uncertainty that can easily cause systematic deviation and significantly affect the accuracy of soil moisture generation in assimilation systems. This study addresses the issue of retrieving model parameters related to soil moisture via the simultaneous estimation of states and parameters based on the Common Land Model (CoLM). The state-parameter estimation algorithms AEnKF (Augmented Ensemble Kalman Filter), DEnKF (Dual Ensemble Kalman Filter) and SODA (Simultaneous optimization and data assimilation) are entirely implemented within an EnKF framework to investigate how the three algorithms can correct model parameters and improve the accuracy of soil moisture estimation. The analysis is illustrated by assimilating the surface soil moisture levels from varying observation intervals using data from Mongolian plateau sites. Furthermore, a radiation transfer model is introduced as an observation operator to analyze the influence of brightness temperature assimilation on states and parameters that are estimated at different microwave signal frequencies. Three cases were analyzed for both soil moisture and brightness temperature assimilation, focusing on the progressive incorporation of parameter uncertainty, forcing data uncertainty and model uncertainty. It has been demonstrated that EnKF is outperformed by all other methods, as it consistently maintains a bias. State-parameter estimation algorithms can provide a more accurate estimation of soil moisture than EnKF. AEnKF is the most robust method, with the lowest RMSE values for retrieving states and parameters dealing only with parameter uncertainty, but it possesses disadvantages related to increasing sources of uncertainty and decreasing numbers of observations. SODA performs well under the complex situations in which DEnKF shows slight disadvantages in terms of statistical indicators: however, the former consumes far more memory and time than the latter.

入藏号：WOS:000365623500014

文献类型：Article

语种：English

作者关键词：Data assimilation, Soil moisture, Brightness temperature, State-parameter estimation, Common Land Model

扩展关键词：LAND DATA ASSIMILATION; BRIGHTNESS TEMPERATURE OBSERVATIONS; HYDROLOGIC DATA ASSIMILATION; QUASI-GEOSTROPHIC MODEL; SIMULATED RADAR DATA; ROOT KALMAN FILTER; MICROPHYSICAL PARAMETERS; ATMOSPHERIC STATE; SURFACE MODEL; VEGETATION

通讯作者地址：Huang, Chunlin; Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Gansu, Peoples R China.

电子邮件地址：huangcl@lzb.ac.cn

地址：

[Chen, Weijing; Shen, Huanfeng] Wuhan Uni*, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.

[Chen, Weijing; Huang, Chunlin; Li, Xin]Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Gansu, Peoples R China.

[Huang, Chunlin] Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Gansu, Peoples R China.

[Li, Xin] Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China.

研究方向：Water Resources

ISSN：0309-1708

eISSN:1872-9657

影响因子（2014）：3.417