An efficient estimation of time-varying parameters of dynamic models by combining offline batch optimization and online data assimilation
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It is crucially important to estimate unknown parameters in earth system models by integrating observation and numerical simulation. For many applications in earth system sciences, the optimization method which allows parameters to temporally change is required. Here I present an efficient and practical method to estimate the time-varying parameters of relatively low dimensional models. I propose combining offline batch optimization and online data assimilation. In the newly proposed method, called Hybrid Offline Online Parameter Estimation with Particle Filtering (HOOPE-PF), I constrain the estimated model parameters in sequential data assimilation to the result of offline batch optimization in which the posterior distribution of model parameters is obtained by comparing the simulated and observed climatology. The HOOPE-PF outperforms the original sampling-importance-resampling particle filter in the synthetic experiment with the toy model and the real-data experiment with the conceptual hydrological model. The advantage of HOOPE-PF is that the performance of the online data assimilation is not greatly affected by the hyperparameter of ensemble data assimilation which contributes to inflating the ensemble variance of estimated parameters.
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