2021/03/16 by Olivier Pannekoucke, Pannekoucke, Olivier, Philippe Arbogast +1
Earth and Planetary Sciences · Environmental Science · #Climate variability and models #Data Analysis #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Hydrological Forecasting Using AI #Mathematical Software (cs.MS) #Meteorological Phenomena and Simulations #Numerical Analysis (math.NA) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2103.09226
openalex publication_date 2021/03/16 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.