Bayesian inference for fluid dynamics: a case study for the stochastic rotating shallow water model

Lang, O., Van Leeuwen, P. J., Crisan, D. and Potthast, R. ORCID: https://orcid.org/0000-0001-6794-2500 (2022) Bayesian inference for fluid dynamics: a case study for the stochastic rotating shallow water model. Frontiers in Applied Mathematics and Statistics, 8. 949354. ISSN 2297-4687 doi: 10.3389/fams.2022.949354

Abstract/Summary

In this work, we use a tempering-based adaptive particle filter to infer from a partially observed stochastic rotating shallow water (SRSW) model which has been derived using the Stochastic Advection by Lie Transport (SALT) approach. The methodology we present here validates the applicability of tempering and sample regeneration using a Metropolis-Hastings procedure to high-dimensional models appearing in geophysical fluid dynamics problems. The methodology is tested on the Lorenz 63 model with both full and partial observations. We then study the efficiency of the particle filter for the SRSW model in a configuration simulating the atmospheric Jetstream.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/110482
Identification Number/DOI	10.3389/fams.2022.949354
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Publisher	Frontiers
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