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- Title
Derivative-free optimization for large-scale nonlinear data assimilation problems.
- Authors
Gratton, S.; Laloyaux, P.; Sartenaer, A.
- Abstract
The computation of derivatives and the development of tangent and adjoint codes represent a challenging issue and a major human time-consuming task when solving operational data assimilation problems. The ensemble Kalman filter provides a suitable derivative-free adaptation for the sequential approach by using an ensemble-based implementation of the Kalman filter equations. This article proposes a derivative-free variant for the variational approach, based on an iterative subspace minimization (ISM) technique. At each iteration, a subspace of low dimension is built from the relevant information contained in the empirical orthogonal functions (EOFs), allowing us to define a reduced 4D-Var subproblem which is then solved using a derivative-free optimization (DFO) algorithm. Strategies to improve the quality of the selected subspaces are presented, together with two numerical illustrations. The ISM technique is first compared with a basic stochastic ensemble Kalman filter on an academic shallow-water problem. The DFO algorithm embedded in the ISM technique is then validated in the NEMO framework, using its GYRE configuration.
- Subjects
PREDICTION theory; STOCHASTIC processes; KALMAN filtering; ALGORITHMS; COMPUTER programming
- Publication
Quarterly Journal of the Royal Meteorological Society, 2014, Vol 140, Issue 680, p943
- ISSN
0035-9009
- Publication type
Article
- DOI
10.1002/qj.2169