Particle filtering for stochastic navier–stokes signal observed with linear additive noise

We consider a nonlinear filtering problem whereby the signal obeys the stochastic Navier–Stokes equations and is observed through a linear mapping with additive noise. The setup is relevant to data assimilation for numerical weather prediction and climate modeling, where similar models are used for unknown ocean or wind velocities. We present a particle filtering methodology that uses likelihood-informed importance proposals, adaptive tempering, and a small number of appropriate Markov chain Monte Carlo steps. We provide a detailed design for each of these steps and show in our numerical examples that they are all crucial in terms of achieving good performance and efficiency.