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 Eulerian Hemispheric Model, a three-dimensional chemical transport model. Observations of CO were assimilated, leading to substantially reduced bias at surface monitoring stations.
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Questioner Name: Valerie Garcia
Q: How did you determine your assumptions for the error correlations (horizontal separation, vertical and chemical levels)?
A: The assumptions you refer to were a compromise between a reality and simplicity. The assumption of horizontally homogeneous and isotropic error correlations is a basic starting point for modelling error correlations, and it dramatically reduces the amount of data required to parameterise the matrices involved in the decomposition of the background error covariance matrix. The results should, however, be treated with caution; in work not presented here, we have found that that error correlations are, in general, neither horizontally homogeneous nor isotropic.
However the assumptions only applied to the ensemble perturbations (to generate initial conditions and to represent model error), and were not used explicitly in the assimilation step. The ensemble Kalman filter inherently captures flow dependent structures in error correlations, and the assumptions used in the decomposition of the climatological background covariance matrix are not invoked.
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Silver, J.D., Brandt, J., Christensen, J.H., Kahnert, M., Robertson, L. (2014). Ensemble Perturbations for Chemical Data Assimilation. In: Steyn, D., Builtjes, P., Timmermans, R. (eds) Air Pollution Modeling and its Application XXII. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5577-2_38
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DOI: https://doi.org/10.1007/978-94-007-5577-2_38
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