An Improved Particle Filter Algorithm Based on Ensemble Kalman Filter and Markov Chain Monte Carlo Method


Knowledge assimilation (DA) has developed into an vital methodology in Earth science analysis due to its capability of mixing model dynamics and observations. Among numerous DA ways, the particle filter (PF) is free from the constraints of linear models and Gaussian error distributions. Therefore, it's currently receiving increasing attention in DA. However, the particle degeneracy still remains a major downside in sensible application of PF. During this paper, an improved PF is proposed primarily based on ensemble Kalman filter (EnKF) and the Markov Chain Monte Carlo (MCMC) technique. It uses an EnKF analysis to outline the proposal density of PF instead of the prior density, therefore reducing the chance of particle degeneracy. Furthermore, when particle degeneracy happens, resampling is performed follow by an MCMC move step to increase the diversity of particles, so reducing the potential of particle impoverishment and improving the accuracy of the filter. Finally, the improved PF is tested by assimilating brightness temperatures from the Advanced Microwave Scanning Radiometer (AMSR-E) into the variance infiltration capacity (VIC) model to estimate soil moisture within the NaQu network region at the Tibetan Plateau. The experiment results show that the improved PF can offer additional correct assimilation results and also want fewer particles to induce reliable estimations than the EnKF and the quality PF, therefore demonstrating the effectiveness and practicality of the improved PF.

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