Observation thinning in data assimilation computations
We propose to use an observation-thinning method for the efficient numerical solution of large-scale incremental four- dimensional (4D-Var) data assimilation problems. This decomposition is based on exploiting an adaptive hierarchy of the observations. Starting with a low-cardinality set and the sol...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2015
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/62a298c003384283901627d22f1dae46 |
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| Sumario: | We propose to use an observation-thinning method for the efficient numerical solution of large-scale incremental four- dimensional (4D-Var) data assimilation problems. This decomposition is based on exploiting an adaptive hierarchy of the observations. Starting with a low-cardinality set and the solution of its corresponding optimization problem, observations are successively added based on a posteriori error estimates. The particular structure of the sequence of associated linear systems allows the use of a variant of the conjugate gradient algorithm which effectively exploits the fact that the number of observations is smaller than the size of the vector state in the 4D-Var model. The new algorithm is tested on a one-dimensional-wave equation and on the Lorenz96 system, the latter one being of special interest because of its similarity with numerical weather prediction systems. |
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