SOME ISSUES AND RESULTS ON THE EnKF AND PARTICLE FILTERS FOR METEOROLOGICAL MODELS Christophe Baehr 1, 2 and Olivier Pannekoucke 1 1 M´ et´ eo-France / CNRS CNRM / GAME URA1357 42 Avenue G. Coriolis, 31057 Toulouse Cedex 1, France (e-mail: christophe.baehr@meteo.fr, olivier.pannekoucke@meteo.fr) 2 Universit´ e de Toulouse Paul Sabatier Institut de Math´ ematiques 118 route de Narbonne, 31062 Toulouse Cedex 9, France Abstract. In this paper we examine the links between Ensemble Kalman Filters (EnKF) and Particle Filters (PF). EnKF can be seen as a Mean-Field process with a PF approximation. We explore the problem of dimensionality on a toy model. To by-pass this difficulty, we suggest using Local Particle Filters (LPF) to catch non- lineartity and feed larger scale EnKF. To go one step forward we conclude with a real application and present the filtering of perturbed measurements of atmospheric wind in the domain of turbulence. This example is the cornerstone of the LPF for the assimilation of atmospheric turbulent wind. These local representation tech- niques will be use in further works to assimilate singular data of turbulence linked parameters in non-hydrostatic models. Keywords: Ensemble Kalman Filter, Particle Filter, Data Assimilation, Mean– Field Process. 1 Introduction The major problems in data assimilation for geophysical models come from nonlinearity of dynamics, non-gaussianity of perturbations and high dimen- sions of state space. Ensemble Kalman Filters (EnKF) was a first response of these difficulties. For a few years some authors have tried to use Particle Filters (PF) roughly to propose an alternative strategy. But directly applied this new approach stumbles across the problem of the dimensionality. In this paper, we present the links between EnKF and PF, we also remind that the EnKF converges but tends to a particular process and we describe the dynamical system of the nonlinear filter distribution. In the case of the PF, with a modified selection step, we investigate the effect of an increasing state space dimension for a constant number of particles. Then we propose to cou- ple EnKF and Local Particle Filter (LPF) to propose solution in the area of strong uncertainties. The next step will be the use of LPF with a stochastic representation of the medium and we present some results on the filtering of