Dan Crisan

Noise calibration for geophysical fluid dynamics models

Stochastic partial differential equations have been used in a variety of contexts to model the evolution of uncertain dynamical systems. In recent years, their applications to geophysical fluid dynamics has increased massively. For a judicious usage in modelling fluid evolution, one needs to calibrate the amplitude of the noise to data. In this paper we address this requirement for the stochastic rotating shallow water (SRSW) model. This work is a continuation of [1], where a data assimilation methodology has been introduced for the SRSW model. The noise used in [1] was introduced as an arbitrary random phase shift in the Fourier space. This is not necessarily consistent with the uncertainty induced by a model reduction procedure. In this paper, we introduce a new method of noise calibration of the SRSW model which is compatible with the model reduction technique. The method is generic and can be applied to arbitrary stochastic parametrizations. It is also agnostic as to the source of data (real or synthetic). It is based on a principal component analysis technique to generate the eigenvectors and the eigenvalues of the covariance matrix of the stochastic parametrization. For SRSW model covered here, we calibrate the noise by using the elevation variable of the model, as this is an observable easily obtainable in practical application, and use synthetic data as input for the calibration procedure. If time permits I will present an alternative calibration method based on generative models technology. This is joint work with Alexander Lobbe, Oana Lang, Peter Jan van Leewuen and Roland Potthast and is based on paper [2].

[1] Dan Crisan, Oana Lang, Peter Jan van Leewuen and Roland Potthast. Bayesian inference for fluid dynamics: a case study for the stochastic rotating shallow water model. Frontiers in Applied Mathematics and Statistics, 2023

[2] D Crisan, Oana Lang, Alexander Lobbe, Peter Jan van Leewuen and Roland Potthast, Noise calibration for SPDEs: A case study for the rotating shallow water model, Foundations of Data Science, 2023.