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J. Sénégas H. Wackernagel W. Rosenthal T. Wolf 《Stochastic Environmental Research and Risk Assessment (SERRA)》2001,15(1):65-86
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. 相似文献
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