Online estimation of colored observation-noise parameters within an ensemble Kalman filtering framework

This work addresses the problem of data assimilation in large-dimensional systems with colored observation noise of unknown statistics, a scenario that will become more common in the near future with the deployment of denser observational networks of high spatio-temporal coverage. Here, we are interested in the ensemble Kalman filter (EnKF) framework, which has been derived around a white observation noise assumption. Recently, colored observation-noise aware EnKFs in which the noise was modeled as a first-order autoregressive (AR) model were introduced. This work generalizes the above-mentioned filters to learn the statistics of the AR model further online. We follow the state augmentation approach first to estimate the state and the AR model transfer matrix simultaneously, then the variational Bayesian approach to estimate the AR model noise covariance parameters further. We accordingly derive two filtering EnKF-like algorithms, which estimate those statistics together with the system state. We demonstrate the effectiveness of the proposed colored observation-noise aware filtering schemes and compare their performance based on several numerical experiments conducted with the Lorenz-96 model.

Automated summary

This work addresses the problem of data assimilation in large-dimensional systems with colored observation noise of unknown statistics. The authors generalize the existing filters to learn the statistics of the autoregressive (AR) model online. They propose two filtering EnKF-like algorithms to estimate the statistics together with the system state. Numerical experiments conducted with the Lorenz-96 model demonstrate the effectiveness of the proposed colored observation-noise aware filtering schemes.