info:eu-repo/semantics/article
Model error covariance estimation in particle and ensemble kalman filters using an online expectation–maximization algorithm
Fecha
2020-11Registro en:
Cocucci, Tadeo Javier; Pulido, Manuel Arturo; Lucini, María Magdalena; Tandeo, Pierre; Model error covariance estimation in particle and ensemble kalman filters using an online expectation–maximization algorithm; John Wiley & Sons Ltd; Quarterly Journal of the Royal Meteorological Society; 2020; 11-2020; 1-27
0035-9009
0035-9009
CONICET Digital
CONICET
Autor
Cocucci, Tadeo Javier
Pulido, Manuel Arturo
Lucini, María Magdalena
Tandeo, Pierre
Resumen
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 Lorenz-96 dynamical systems designed to represent some common scenarios in data assimilation such as nonlinearity, chaoticity and model mis-specification.