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 proposed 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 particle 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 the 40-variable Lorenz-96 dynamical systems designed to represent some common scenarios in data assimilation such as non-linearity, chaoticity and model misspecification.

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• A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation
• Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters