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Model error covariance estimation in particle and ensemble Kalman filters using an online expectation‐maximization algorithm
Abstract : The performance of ensemble-based data assimilation techniques that estimate
the state of a dynamical system from partial observations depends crucially on
the prescribed uncertainty of the model dynamics and of the observations. These
are not usually known and have to be inferred. Many approaches have been pro-
posed to tackle this problem, including fully Bayesian, likelihood maximization
and innovation-based techniques. This work focuses on maximization of the
likelihood function via the expectation–maximization (EM) algorithm to infer
the model error covariance combined with ensemble Kalman filters and parti-
cle filters to estimate the state. The classical application of the EM algorithm
in a data assimilation context involves filtering and smoothing a fixed batch of
observations in order to complete a single iteration. This is an inconvenience
when using sequential filtering in high-dimensional applications. Motivated by
this, an adaptation of the algorithm that can process observations and update
the parameters on the fly, with some underlying simplifications, is presented.
The proposed technique was evaluated and achieved good performance in
experiments with the Lorenz-63 and Lorenz-96 dynamical systems designed to
represent some common scenarios in data assimilation such as nonlinearity,
chaoticity and model mis-specification.
https://hal-imt-atlantique.archives-ouvertes.fr/hal-02964198
Contributor : Pierre Tandeo <>
Submitted on : Monday, October 12, 2020 - 11:30:18 AM
Last modification on : Saturday, July 31, 2021 - 3:20:52 AM
Contributor : Pierre Tandeo <>
Submitted on : Monday, October 12, 2020 - 11:30:18 AM
Last modification on : Saturday, July 31, 2021 - 3:20:52 AM
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