Multiscale Data Assimilation for Very High Resolution Models
Abstract
The commonly used data assimilation algorithms, including three-/four-dimensional variational and Kalman filter-based algorithm, are based on optimal estimation theory, in which both background state and observation error covariances are of fundamental importance. Using a variety of theoretical and numerical analyses, we show that those optimal estimation algorithms are inherently ineffective when they are applied to models at a horizontal resolution of the order of 1 km. The ineffectiveness arises from its filtering properties that are dictated by the error covariance. We suggest a multiscale data assimilation algorithm, in which the cost function is decomposed for a set of distinct spatial scales. Data assimilation is implemented sequentially from large to small scales. Results are presented to demonstrate the algorithm.
- Publication:
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American Geophysical Union, Ocean Sciences Meeting
- Pub Date:
- February 2016
- Bibcode:
- 2016AGUOSPO53C..05L
- Keywords:
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- 1910 Data assimilation;
- integration and fusion;
- INFORMATICSDE: 1990 Uncertainty;
- INFORMATICSDE: 4260 Ocean data assimilation and reanalysis;
- OCEANOGRAPHY: GENERALDE: 4263 Ocean predictability and prediction;
- OCEANOGRAPHY: GENERAL