Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation |
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| Institution: | 1. School of Resource and Environmental Science, Wuhan University, Wuhan, Hubei 430079, China\n;2. Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China\n;3. Heihe Remote Sensing Experimental Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;4. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China;1. Department of Mathematics, University of Padua, Padua, Italy;2. Biosphere 2—University of Arizona, Tucson, USA;3. Department of Hydrology and Water Resources, University of Arizona, Tucson, USA;4. Institut National de la Recherche Scientifique, Centre Eau Terre Environnement (INRS-ETE), Quebec City, Canada;1. Key Laboratory of Watershed Geographic Sciences, Chinese Academy of Sciences, Nanjing 210008, China;2. Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;3. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;1. Earth and Environmental Systems Institute, The Pennsylvania State Unviersity, University Park, PA, USA;2. Department of Meteorology, The Pennsylvania State Unviersity, University Park, PA, USA;3. Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA;1. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China;2. Department of Geological Sciences, The John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, C1100 Austin, TX 78712–0254, USA;3. University of Chinese Academy of Sciences, Beijing 100049, China;4. Ministry of Education Key Laboratory for Earth System Modeling, and Center for Earth System Science, Tsinghua University, Beijing 100084, China;1. Institute of Fluid Mechanics and Environmental Physics in Civil Engineering, University of Hannover, Germany;2. Center for Applied Geoscience, University of Tübingen, Germany |
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| Abstract: | 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. |
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