SymPKF: a symbolic and computational toolbox for the design of parametric Kalman filter dynamics

TL;DR

SymPKF is a Python toolbox that automates the derivation and implementation of parametric Kalman filter dynamics, facilitating data assimilation in complex systems with symbolic and numerical capabilities.

Contribution

It introduces SymPKF, a symbolic and computational toolbox that automates the derivation of PKF dynamics for univariate and multivariate covariance models.

Findings

Successfully applied to Burgers equation and 2D linear advection.

Automates symbolic derivation and code generation for PKF.

Demonstrates potential for complex multivariate data assimilation.

Abstract

Recent researches in data assimilation lead to the introduction of the parametric Kalman filter (PKF): an implementation of the Kalman filter, where the covariance matrices are approximated by a parameterized covariance model. In the PKF, the dynamics of the covariance during the forecast step relies on the prediction of the covariance parameters. Hence, the design of the parameter dynamics is crucial while it can be tedious to do this by hand. This contribution introduces a python package, SymPKF, able to compute PKF dynamics for univariate statistics and when the covariance model is parameterized from the variance and the local anisotropy of the correlations. The ability of SymPKF to produce the PKF dynamics is shown on a non-linear diffusive advection (Burgers equation) over a 1D domain and the linear advection over a 2D domain. The computation of the PKF dynamics is performed at a symbolic level, but an automatic code generator is also introduced to perform numerical simulations. A final multivariate example illustrates the potential of SymPKF to go beyond the univariate case.