Error covariance modeling in sequential data assimilation

doi:10.1007/PL00009788

Error covariance modeling in sequential data assimilation

2001-03-18

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DOI：10.1007/PL00009788

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The efficiency of a sequential data assimilation scheme relies on the capability to describe the error covariance. This aspect is all the more relevant if one needs accurate statistics on the estimation error. Frequently an ad hoc function depending on a few parameters is proposed, and these parameters are tuned, estimated or updated. This usually requires that the covariance is second-order stationary (i.e. depends only on the distance between two points). In this paper, we discuss this feature and show that even in simple applications (such as one-dimensional hydrodynamics), this assumption does not hold and may lead to poorly described estimation errors. We propose a method relying on the analysis of the error term and the use of the hydrodynamical model to generate one part of the covariance function, the other part being modeled using a second-order stationary approach. This method is discussed using a twin experiment in the case where a physical parameter is erroneous, and improves significantly the results: the model bias is strongly reduced and the estimation error is well described. Moreover, it enables a better adaptation of the Kalman gain to the actual estimation error.

Keywords:

ATMOSPHERIC DATA ASSIMILATION

MAXIMUM-LIKELIHOOD-ESTIMATION

KALMAN FILTER

FORECAST

PARAMETERS

FLOW

Error covariance modeling in sequential data assimilation

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