Estimation of spatially varying parameters in three-dimensional cohesive sediment transport models by assimilating remote sensing data
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Based on a three-dimensional cohesive sediment transport model with the adjoint data assimilation, the spatially varying parameters are estimated by assimilating satellite-retrieved suspended sediment concentrations (SSCs) in the Hangzhou Bay, China. To reduce the ill-posedness of the inverse problem, an independent point scheme is developed and a combined independent point and Tikhonov regularization scheme (CIPTRS) is presented. The CIPTRS is calibrated in ideal twin experiments and the results reveal that the CIPTRS can restrict the influence of ill-posedness and improve the accuracy of parameter estimation. In practical experiments, the spatially varying settling velocity is estimated by assimilating the satellite-retrieved SSCs using different strategies, and the best modeling results are obtained when the CIPTRS is used. To further improve the modeling results, the spatially varying settling velocity and initial conditions are estimated simultaneously using the CIPTRS. The data misfit between observed and simulated SSCs is largely decreased and the simulated SSCs can reproduce the spatial and temporal features of observed SSCs. The experimental results indicate that the adjoint method is a useful method to estimate the poorly known parameters in practical applications and the CIPTRS can effectively improve the results of data assimilation and parameter estimation.
KeywordsCohesive sediment transport Data assimilation Adjoint method Parameter estimation Remote sensing data
The authors would like to thank the reviewers for the constructive suggestions to greatly improve the manuscript. Thanks are extended to Professor Xianqiang He at Second Institute of Oceanography, State Oceanic Administration, China for providing the GOCI-retrieved SSCs data and Professor Jorge Nocedal at Northwestern University for sharing the source code of L-BFGS. Financial support to the study is provided by the national key research and development plan of China [Grant numbers 2017YFC1404000, 2017YFA0604100 and 2016YFC1402304], the Natural Science Foundation of Zhejiang Province [Grant number LY15D060001], the key research and development plan of Shandong Province [Grant number 2016ZDJS09A02], and the National Natural Science Foundation of China [Grant number 41206001].
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