Parametric Kalman filter for chemical transport models

Olivier Pannekoucke (1, 2) , Sophie Ricci (2) , Sebastien Barthelemy (2) , Richard Ménard , Olivier Thual (3, 2)

1 CNRM - Centre national de recherches météorologiques
2 CECI - Climat, Environnement, Couplages et Incertitudes [Toulouse]
3 IMFT - Institut de mécanique des fluides de Toulouse

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.

Domains: Sciences of the Universe [physics]

Olivier Pannekoucke, Sophie Ricci, Sebastien Barthelemy, Richard Ménard, Olivier Thual. Parametric Kalman filter for chemical transport models. Tellus A: Dynamic Meteorology and Oceanography, 2016, 68, ⟨10.3402/tellusa.v68.31547⟩. ⟨insu-03668258⟩