Parametric Kalman filter for chemical transport models

Olivier Pannekoucke; Sophie Ricci; Sebastien Barthelemy; Richard Ménard; Olivier Thual

doi:10.3402/tellusa.v68.31547

Parametric Kalman filter for chemical transport models

Tellus

Volume 68 (2016): Issue 1

By: Olivier Pannekoucke, Sophie Ricci, Sebastien Barthelemy, Richard Ménard and Olivier Thual

Open Access

|Dec 2016

Abstract

A computational simplification of the Kalman filter (KF) is introduced – the parametric Kalman filter (PKF). The full covariance matrix dynamics of the KF, which describes the evolution along the analysis and forecast cycle, is replaced by the dynamics of the error variance and the diffusion tensor, which is related to the correlation length-scales. The PKF developed here has been applied to the simplified framework of advection–diffusion of a passive tracer, for its use in chemical transport model assimilation. The PKF is easy to compute and computationally cost-effective than an ensemble Kalman filter (EnKF) in this context. The validation of the method is presented for a simplified 1-D advection–diffusion dynamics.

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Articles in this issue

DOI: https://doi.org/10.3402/tellusa.v68.31547 | Journal eISSN: 3035-9554

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Language: English

Page range: 31547 - 31547

Submitted on: Mar 8, 2016

Accepted on: Sep 7, 2016

Published on: Dec 1, 2016

Published by: Stockholm University Press

In partnership with: Paradigm Publishing Services

Keywords:

data assimilation,

Kalman filter,

covariance dynamics,

parameterisation of analysis

© 2016 Olivier Pannekoucke, Sophie Ricci, Sebastien Barthelemy, Richard Ménard, Olivier Thual, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 68 (2016): Issue 1