Enhancing Hydrologic Data Assimilation Algorithms

Coupling a deterministic four-dimensional variational (4DVAR) assimilation method with the Particle Filter (PF) ensemble data assimilation system, to produce a robust approach for dual-state-parameter estimation. In our proposed method, the Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN), we characterize the model structural uncertainty in addition to model parameter and input uncertainties. Link to paper

The following figure shows the Evolutionary Particle Filter with MCMC (EPFM) algorithm. In this algorithm, the prior distribution undergoes an evolutionary process based on the designed mutation and crossover operators of GA. The merit of this approach is that the particles move to an appropriate position by using the GA optimization and then the number of effective particles is increased by means of MCMC, whereby the particle degeneracy is avoided and the particle diversity is improved. Link to paper