English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The ensemble Kalman filter (EnKF) is a data assimilation technique that usesan ensemble of models, updated with data, to track the time evolution of ausually non-linear system. It does so by using an empirical approximation tothe well-known Kalman filter. However, its performance can suffer when theensemble size is smaller than the state space, as is often necessary forcomputationally burdensome models. This scenario means that the empiricalestimate of the state covariance is not full rank and possibly quite noisy. Tosolve this problem in this high dimensional regime, we propose acomputationally fast and easy to implement algorithm called the penalizedensemble Kalman filter (PEnKF). Under certain conditions, it can betheoretically proven that the PEnKF will be accurate (the estimation error willconverge to zero) despite having fewer ensemble members than state dimensions.Further, as contrasted to localization methods, the proposed approach learnsthe covariance structure associated with the dynamical system. Thesetheoretical results are supported with simulations of several non-linear andhigh dimensional systems.

Penalized Ensemble Kalman Filters for High Dimensional Non-linear Systems