2016-11-01
Relevance of conserved quantities for data assimilation
Publication
Publication
In this paper we study relevance of quantities conserved by a numerical scheme for data
assimilation. We consider three Arakawa discretizations of the quasigeostrophic model
that either preserve energy, or enstrophy, or both and an Ensemble Kalman Filter data
assimilation method. Numerical experiments show that if non-local observations are
assimilated, the choice of a numerical scheme is crucial for a good reconstruction of
the mean fields, namely the Arakawa scheme that preserves both energy and enstrophy
provides with the best estimate, while the Arakawa scheme that preserves only energy
completely fails. If local observations are assimilated, a good mean field reconstruction
is independent of a numerical scheme, though it becomes necessary to apply localization
and inflation. Furthermore, we show that even though an estimated probability den-
sity function is normal due to the Ensemble Kalman Filter assumptions, while the true
probability density function is taken to be skewed, the estimated mean fields still exhibit
the nonlinear behavior. This indicates the ability of the Ensemble Kalman Filter to
reproduce nonlinear large-scale behavior without reproducing non-Gaussian small-scale
behavior.
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Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands | |
Scientific Computing [SC] | |
Organisation | Scientific Computing |
Dubinkina, S. (2016). Relevance of conserved quantities for data assimilation. Scientific Computing [SC]. Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands.
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