A hybrid approach to generating search subspaces in dynamically constrained 4-dimensional data assimilation. (September 2017)

• Record Type: Journal Article
• Authors: Yaremchuk, Max; Martin, Paul; Beattie, Christopher
• Is Part Of: Ocean modelling. Volume 117(2017:Sep.)
• Page Start: 41
• Page End: 51
• Publication Date: 2017-09
• Subjects: Data assimilation -- Adjoint analysis -- Regional modeling; Oceanography -- Periodicals; Océanographie -- Périodiques; Oceanography; Periodicals; 551.46
• DOI: 10.1016/j.ocemod.2017.08.003
• Languages: English
• ISSNs: 1463-5003

Abstract

Highlights: A hybrid adjoint-free (ha4dVar) method of data assimilation is proposed. The method is based on the ensemble technique. In the linear case ha4dVar converges to the 4dVar solution. Performance is tested against the standard data space 4dVar in a realistic setting. Ha4dVar is found to be 4dVar-compatible in terms of the computational cost and the forecast skill. Abstract: Development and maintenance of the linearized and adjoint code for advanced circulation models is a challenging issue, requiring a significant proportion of total effort in operational data assimilation (DA). The ensemble-based DA techniques provide a derivative-free alternative, which appears to be competitive with variational methods in many practical applications. This article proposes a hybrid scheme for generating the search subspaces in the adjoint-free 4-dimensional DA method (a4dVar) that does not use a predefined ensemble. The method resembles 4dVar in that the optimal solution is strongly constrained by model dynamics and search directions are supplied iteratively using information from the current and previous model trajectories generated in the process of optimization. In contrast to 4dVar, which produces a single search direction from exact gradient information, a4dVar employs an ensemble of directions to form a subspace in order to proceed. In the earlier versions of a4dVar, search subspaces were built using the leading EOFs of either the model trajectory or the projections of the model-data misfits onto the range of the background error covariance (BEC) matrix at the current iteration. In the present study, we blend both approaches and explore a hybrid scheme of ensemble generation in order to improve the performance and flexibility of the algorithm. In addition, we introduce balance constraints into the BEC structure and periodically augment the search ensemble with BEC eigenvectors to avoid repeating minimization over already explored subspaces. Performance of the proposed hybrid a4dVar (ha4dVar) method is compared with that of standard 4dVar in a realistic regional configuration assimilating real data into the Navy Coastal Ocean Model (NCOM). It is shown that the ha4dVar converges faster than a4dVar and can be potentially competitive with 4dvar both in terms of the required computational time and the forecast skill.