Accelerating the spin-up of Ensemble Kalman Filtering

TL;DR

This paper introduces a scheme using the no-cost ensemble Kalman Smoother to significantly reduce the initial spin-up time of ensemble Kalman Filters, enabling faster convergence to optimal performance.

Contribution

It presents a novel approach that accelerates the spin-up of ensemble Kalman Filters by integrating a smoothing step, demonstrated with the LETKF in a quasi-geostrophic model.

Findings

Fast convergence to optimal error levels achieved

Reduced spin-up time demonstrated in model experiments

Scheme only requires extra computation during initial phase

Abstract

A scheme is proposed to improve the performance of the ensemble-based Kalman Filters during the initial spin-up period. By applying the no-cost ensemble Kalman Smoother, this scheme allows the model solutions for the ensemble to be "running in place" with the true dynamics, provided by a few observations. Results of this scheme are investigated with the Local Ensemble Transform Kalman Filter (LETKF) implemented in a Quasi-geostrophic model, whose original framework requires a very long spin-up time when initialized from a cold start. Results show that it is possible to spin up the LETKF and have a fast convergence to the optimal level of error. The extra computation is only required during the initial spin-up since this scheme resumes to the original LETKF after the "running in place" is achieved.