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Gift Dumedah and Jeffrey P. Walker

Abstract

Data assimilation (DA) methods are commonly used for finding a compromise between imperfect observations and uncertain model predictions. The estimation of model states and parameters has been widely recognized, but the convergence of estimated parameters has not been thoroughly investigated. The distribution of model state and parameter values is closely linked to convergence, which in turn impacts the ultimate estimation accuracy of DA methods. This demonstration study examines the robustness and convergence of model parameters for the ensemble Kalman filter (EnKF) and the evolutionary data assimilation (EDA) in the context of the Soil Moisture and Ocean Salinity (SMOS) soil moisture assimilation into the Joint UK Land Environment Simulator in the Yanco area in southeast Australia. The results show high soil moisture estimation accuracy for the EnKF and EDA methods when compared with the open loop estimates during evaluation and validation stages. The level of convergence was quantified for each model parameter in the EDA approach to illustrate its potential in the retrieval of variables that were not directly observed. The EDA was found to have a higher estimation accuracy than the EnKF when its updated members were evaluated against the SMOS level 2 soil moisture. However, the EnKF and EDA estimations are comparable when their forward soil moisture estimates were validated against SMOS soil moisture outside the assimilation time period. This suggests that parameter convergence does not significantly influence soil moisture estimation accuracy for the EnKF. However, the EDA has the advantage of simultaneously determining the convergence of model parameters while providing comparably higher accuracy for soil moisture estimates.

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Gift Dumedah, Aaron A. Berg, and Mark Wineberg

Abstract

This study has applied the Nondominated Sorting Genetic Algorithm II (NSGA-II) in a two-step assimilation procedure to jointly assimilate brightness temperature into a radiative transfer model and soil moisture into a land surface model. The first assimilation procedure generates a time series of soil moisture by assimilating brightness temperature from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) into the Land Parameter Retrieval Model (LPRM). The second procedure generates assimilated soil moisture by assimilating the soil moisture from LPRM into the Canadian Land Surface Scheme (CLASS). Note that the assimilated soil moisture was generated by merging two soil moisture estimates: one from LPRM and the other from the CLASS simulation. The assimilated soil moisture is better than using the soil moisture determined either from the satellite observation or the land surface scheme alone. This method provides improved model state and parameterizations for both LPRM and CLASS with the aim to facilitate real-time forecasts when satellite information becomes available. Application of this framework to the Brightwater Creek watershed in southern Saskatchewan illustrates the utility of the joint assimilation framework to improve a time series of soil moisture estimates. The estimated soil moisture datasets were evaluated over an agricultural site in southern Saskatchewan using in situ monitoring networks. These results demonstrate that soil moisture generated from assimilation of brightness temperature could be improved by incorporating it into a land surface model. A comparison between the assimilated soil moisture and in situ dataset demonstrates an improvement in accuracy and temporal pattern that is accomplished through the assimilation framework.

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