Preprints
https://doi.org/10.5194/npg-2021-32
https://doi.org/10.5194/npg-2021-32

  22 Sep 2021

22 Sep 2021

Review status: this preprint is currently under review for the journal NPG.

A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter

Andrey A. Popov, Amit N. Subrahmanya, and Adrian Sandu Andrey A. Popov et al.
  • Computational Science Laboratory, Department of Computer Science, Virginia Tech, 2202 Kraft Drive, Blacksburg, VA, 24060, USA

Abstract. Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the number of particles is insufficient to sample the high probability regions of the state space. Rejuvenation is often implemented in a heuristic manner by the addition of stochastic samples that widen the support of the ensemble. This work aims at improving canonical rejuvenation methodology by the introduction of additional prior information obtained from climatological samples; the dynamical particles used for importance sampling are augmented with samples obtained from stochastic covariance shrinkage. The ensemble transport particle filter, and its second order variant, are extended with the proposed rejuvenation approach. Numerical experiments show that modified filters significantly improve the analyses for low dynamical ensemble sizes.

Andrey A. Popov et al.

Status: open (until 17 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Andrey A. Popov et al.

Andrey A. Popov et al.

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Short summary
Numerical weather prediction requires the melding of both computational model and data obtained from sensors such as satellites. We focus on one algorithm to accomplish this. We aim to aide its use by additionally supplying it with data obtained from separate models that describe the average behaviour of the computational model at any given time. We show that our approach outperforms the standard approaches to this problem.