Engineering Commons^™

The Quest For Model Uncertainty Quantification: A Hybrid Ensemble And Variational Data Assimilation Framework, Peyman Abbaszadeh, Hamid Moradkhani, Dacian Daescu

Civil and Environmental Engineering Faculty Publications and Presentations

This article presents a novel approach to couple 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. The sequential PF is formulated within the 4DVAR system to design a computationally efficient feedback mechanism throughout the assimilation period. In this framework, the 4DVAR optimization produces the maximum a posteriori estimate of state variables at the beginning …