Boundary Conditions for the Parametric Kalman Filter forecast submited

TL;DR

This paper develops boundary condition specifications for the parametric Kalman filter in bounded domains, demonstrating through ensemble validation that the PKF accurately reproduces boundary-influenced uncertainties for transport and diffusion equations.

Contribution

It provides a method to specify boundary conditions for the PKF's covariance model, enabling accurate uncertainty prediction in bounded physical domains.

Findings

PKF reproduces ensemble-based uncertainty with boundary perturbations

Dirichlet boundary conditions on physical variables imply Dirichlet conditions on covariance parameters

PKF effectively handles boundary effects in uncertainty forecasts

Abstract

This paper is a contribution to the exploration of the parametric Kalman filter (PKF), which is an approximation of the Kalman filter, where the error covariances are approximated by a covariance model. Here we focus on the covariance model parameterized from the variance and the anisotropy of the local correlations, and whose parameters dynamics provides a proxy for the full error-covariance dynamics. For this covariance model, we aim to provide the boundary condition to specify in the prediction of PKF for bounded domains, focusing on Dirichlet and Neumann conditions when they are prescribed for the physical dynamics. An ensemble validation is proposed for the transport equation and for the heterogeneous diffusion equation over a bounded 1D domain. This ensemble validation requires to specify the auto-correlation time-scale needed to populate boundary perturbation that leads to prescribed uncertainty characteristics. The numerical simulations show that the PKF is able to reproduce the uncertainty diagnosed from the ensemble of forecast appropriately perturbed on the boundaries, which show the ability of the PKF to handle boundaries in the prediction of the uncertainties. It results that Dirichlet condition on the physical dynamics implies Dirichlet condition on the variance and on the anisotropy. Codes are available at https://github.com/opannekoucke/pkf-boundary