An Efficient Estimation of Time‐Varying Parameters of Dynamic Models by Combining Offline Batch Optimization and Online Data Assimilation

It is crucially important to estimate unknown parameters in process-based models by integrating observation and numerical simulation. For many applications earth system sciences, a parameter estimation method which allows temporally change required. In the present paper, an efficient practical time-varying of relatively low dimensional presented. newly proposed method, called Hybrid Offline Online Parameter Estimation with Particle Filtering (HOOPE-PF), inflation maintain spread ensemble members Sampling-Importance-Resampling Filter (SIRPF) improved using non-parametric posterior probabilistic distribution time-invariant obtained comparing simulated observed climatology. HOOPE-PF outperforms original SIRPF synthetic experiments toy real-data experiment conceptual hydrological model when size small. The advantage that its performance not greatly affected perturbation be added their while it get optimal particle filter. Since extension existing filter has been extensively applied sciences such as land, ecosystem, hydrology, paleoclimate reconstruction, can improve simulation these considering parameters.