A multi‐model ensemble Kalman filter for data assimilation and forecasting

Bach, E. ORCID: https://orcid.org/0000-0002-9725-0203 and Ghil, M. (2023) A multi‐model ensemble Kalman filter for data assimilation and forecasting. Journal of Advances in Modeling Earth Systems, 15 (1). e2022MS003123. ISSN 1942-2466 doi: 10.1029/2022MS003123

Abstract/Summary

Data assimilation (DA) aims to optimally combine model forecasts and observations that are both partial and noisy. Multi-model DA generalizes the variational or Bayesian formulation of the Kalman filter, and we prove that it is also the minimum variance linear unbiased estimator. Here, we formulate and implement a multi-model ensemble Kalman filter (MM-EnKF) based on this framework. The MM-EnKF can combine multiple model ensembles for both DA and forecasting in a flow-dependent manner; it uses adaptive model error estimation to provide matrix-valued weights for the separate models and the observations. We apply this methodology to various situations using the Lorenz96 model for illustration purposes. Our numerical experiments include multiple models with parametric error, different resolved scales, and different fidelities. The MM-EnKF results in significant error reductions compared to the best model, as well as to an unweighted multi-model ensemble, with respect to both probabilistic and deterministic error metrics.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/116990
Identification Number/DOI	10.1029/2022MS003123
Refereed	Yes
Divisions	No Reading authors. Back catalogue items Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Geophysical Union
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