Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method

Particle filters (PFs) have become popular for assimilation of a wide range of hydrologic variables in recent years. With this increased use, it has become necessary to increase the applicability of this technique for use in complex hydrologic/land surface models and to make these methods more viable for operational probabilistic prediction. To make the PF a more suitable option in these scenarios, it is necessary to improve the reliability of these techniques. Improved reliability in the PF is achieved in this work through an improved parameter search, with the use of variable variance multipliers and Markov Chain Monte Carlo methods. Application of these methods to the PF allows for greater search of the posterior distribution, leading to more complete characterization of the posterior distribution and reducing risk of sample impoverishment. This leads to a PF that is more efficient and provides more reliable predictions. This study introduces the theory behind the proposed algorithm, with application on a hydrologic model. Results from both real and synthetic studies suggest that the proposed filter significantly increases the effectiveness of the PF, with marginal increase in the computational demand for hydrologic prediction. Citation: Moradkhani, H., C. M. DeChant, and S. Sorooshian (2012), Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method, Water Resour. Res., 48, W12520, doi:10.1029/2012WR012144.