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Model error and sequential data assimilation. A deterministic formulation
Data assimilation schemes are confronted with the presence of model errors
arising from the imperfect description of atmospheric dynamics. These errors
are usually modeled on the basis of simple assumptions such as bias, white
noise, first order Markov process. In the present work, a formulation of the
sequential extended Kalman filter is proposed, based on recent findings on the
universal deterministic behavior of model errors in deep contrast with previous
approaches (Nicolis, 2004). This new scheme is applied in the context of a
spatially distributed system proposed by Lorenz (1996). It is found that (i)
for short times, the estimation error is accurately approximated by an
evolution law in which the variance of the model error (assumed to be a
deterministic process) evolves according to a quadratic law, in agreement with
the theory. Moreover, the correlation with the initial condition error appears
to play a secondary role in the short time dynamics of the estimation error
covariance. (ii) The deterministic description of the model error evolution,
incorporated into the classical extended Kalman filter equations, reveals that
substantial improvements of the filter accuracy can be gained as compared with
the classical white noise assumption. The universal, short time, quadratic law
for the evolution of the model error covariance matrix seems very promising for
modeling estimation error dynamics in sequential data assimilation
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