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In this work, we use a tempering-based adaptive particle filter to infer froma partially observed stochastic rotating shallow water (SRSW) model which hasbeen derived using the Stochastic Advection by Lie Transport (SALT) approach.The methodology we present here validates the applicability of tempering andsample regeneration via a Metropolis-Hastings algorithm to high-dimensionalmodels used in stochastic fluid dynamics. The methodology is first tested onthe Lorenz '63 model with both full and partial observations. Then we discussthe efficiency of the particle filter the SALT-SRSW model.

Bayesian Inference for Fluid Dynamics: A Case Study for the Stochastic Rotating Shallow Water Model