Ensemble perturbations for chemical data assimilation

Abstract

The ensemble Kalman filter is a commonly used framework for chemical data assimilation. Random perturbations are required for the ensemble initial conditions and to account for model error. For a chemical transport model, such perturbations should represent appropriate scales of variation and correlations in the horizontal, vertical and chemical dimensions. We present a sampling scheme to generate normally distributed perturbations with covariances based on a climatological background covariance matrix, estimated with a spectral decomposition, assuming horizontally homogeneous and isotropic error correlations. We tested the sampling scheme with an ensemble Kalman filter coupled to the Danish..