The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework. Issue 3 (25th March 2019)

• Record Type: Journal Article
• Authors: Abbaszadeh, Peyman; Moradkhani, Hamid; Daescu, Dacian N.
• Is Part Of: Water resources research. Volume 55:Issue 3(2019)
• Page Start: 2407
• Page End: 2431
• Publication Date: 2019-03-25
• Subjects: four‐dimensional variational system -- particle filter -- hydrologic data assimilation; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2018WR023629
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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 of the assimilation window without the need to develop the adjoint of the forecast model. The 4DVAR solution is then perturbed by a newly defined prior error covariance matrix to generate an initial condition ensemble for the PF system to provide more accurate and reliable posterior distributions within the same assimilation window. The prior error covariance matrix is updated from one cycle to another over the main assimilation period to account for model structural uncertainty resulting in an improved estimation of posterior distribution. The premise of the presented approach is that it (1) accounts for all sources of uncertainties involved in hydrologic predictions, (2) uses a small ensemble size, and (3) precludes the particle degeneracy and sample impoverishment. The proposed method is applied on a nonlinear hydrologic model and the effectiveness, robustness, and reliability of the method is demonstrated for several river basins across the United States. Key Points: A joint sequential and variational data assimilation method was developed for superior and robust dual‐state‐parameter estimation The proposed HEAVEN approach accounts for all sources of uncertainties involved in model predictions The effectiveness and usefulness of HEAVEN was evaluated by both deterministic and probabilistic measures