Séminaire du CEREA - 5 mai 2006

Kalman filter algorithms for large scale atmospheric chemistry applications - Remus Hanea

A Kalman filter coupled to the atmospheric chemistry transport model, EUROS, has been used to estimate the ozone concentrations in the boundary layer above Europe. Two lman filter algorithms, the Reduced Rank Square Root (RRSQRT) and the Ensemble Kalman filter (ENKF), were implemented in the first part of the project study. The observations consisted of hourly ozone data in a set of 135 ground-based stations in Europe. Half of these stations were used for the assimilation and the other half only for validation of the results. The combination between data assimilation (Kalman filter) and the atmospheric chemistry transport model, EUROS, gave more accurate results for boundary layer ozone than the EUROS model or measurements used separately.

To combine the best of these two filters, a hybrid filter was constructed by making use of the reduced-rank approximation of the covariance matrix as a variance reductor for the Ensemble Kalman filter. This hybrid algorithm, Complementary Orthogonal subspace Filter For Efficient Ensembles (COFFEE), is coupled to the EUROS model. The performance of all algorithms is compared in terms of residual errors and number of EUROS model evaluations.

The performance of both ENKF and RRSQRT type filters is affected by the nonlinear properties of the model and observation operator, because both rely on linearization to some extent. In order to further study this aspect, several measures of nonlinearity were calculated and linked with the performance of these algorithms.

Le séminaire aura lieu dans la salle de réunion du CEREA B220 à 10h00.

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