Page Personnelle de Christophe Baehr

Christophe Baehr's Homepage
Météo-France/CNRS Researcher member of the CNRM/GAME UMR3589, French National Centre for Meteorological Research, Toulouse

Associated member of the Laboratory of Statistics and Probability University of Toulouse III - Paul Sabatier

Associated Editor "American Journal of Algorithms and Computing"
Office: CNRM-GMEI, B035

Postal address:
Météo-France
CNRM/GMEI
42 Avenue de Coriolis
31057 Toulouse cedex
France

Phone: +33 561 079 641

E-mail: christophe.baehr_{at}_meteo_{dot}_fr, christophe.baehr_{at}_math.univ-toulouse.fr .

Short CURRICULUM VITAE

Chair of the ISEM 2013 on Ecological Modelling in the context of the Global Change

ISEM 2013 website.


Recherche / Research

Stochastic Filtering of Observations on Random Media. Application to Measurements of Turbulent Fluids

Stochastic Filters for Data Assimilation. Application to Atmospheric Data Assimilation

Algorithm Design to include Met Information in Aircraft Navigation Predictors.

Stochastic engineering for Ecological Modelling.

En quelques mots / In some words:
Until now, the filtering of experimental measurements on a turbulent fluid was done by linear digital techniques. In order to use non-linear filters, we have developed stochastic models for measurements of a fluid. To characterize the measurements made with a mobile sensor in a random medium, we have defined an acquisition process of a vector field along a random path. Then, we modify deeply the Lagrangian models of fluids proposed by the physicists to make them compatible with the filtering problem. These models initiated by S.B. Pope belong to the class of McKean-Vlasov equations with mean field. The closure of these equations is obtained by conditioning the Markovian dynamic: first to the observation and then to the acquisition along the sensor path.

For the stochastic filtering of the conditioned acquisition process, we propose new algorithms of non-linear filtering for mean-field processes and for some various types of laws. We prove the convergence of the particle approximation for each new algorithm of estimates we gave.

The new filtering algorithms are based on the dynamics of genealogical trees where the processes interact by genetic selections and by their mean-field law.

Finally, this innovative work is applied to velocity measurements of a turbulent fluid. We present several applications of our methods by using some 1D, 2D or 3D measurements, simulated or real. Our techniques make it possible to obtain high frequency estimates of the fluid velocities as well as quantities characterizing turbulence, and we proceed to a systematic study on numerical errors produced by our methods of calculation.

Using these results, we have developed new methods to learn the turbulence parameters with remote sensors like Doppler LIDAR. These works are still on progress, with convincing results in 3D OSSE mode.

The second part of my research is linked to data assimilation. We study different non-linear techniques used in stochastic engineering in order to suggest new algorithms appropriate to high dimensional problems occurring in atmospheric data assimilation. In the end, we study the mathematical properties of these new estimators.

The last part of my research concerns the design of stochastic algorithm in order to include Met information in the Aircraft Navigation predictors. The trajectory based SESAR concept of operations calls for new concepts in the provision of MET information to Air Traffic Management. This task aims at quantifying the optimal time and spatial resolution of wind and temperature data for trajectory calculation and the probabilistic representation of wind, temperature and weather hazards in trajectory calculation schemes.

ANR Research Projects 2009-2011 :

ANR PREVASSEMBLE; Forecasting and Data assimilation. Subject : Particle Filter in Geosciences Applications with Pierre Del Moral

SESAR Joint Undertaking 2009-2016 :

SESAR J.U., Meteo-France affiliate of the DGAC/DSNA/DTI ; Work Package 4.7.1 and 4.7.2. Subject : New algorithms and stochastic estimators for the weather environment in the optimization of the commercial aircraft trajectory prediction problem
SESAR J.U., Meteo-France affiliate of EUMENET ; Work Package 11.2.1 and 11.2.2. Subject : Support to using MET information for 4D trajectory prediction.
SESAR J.U., Meteo-France co-leader ; Work Package WP-E IMET Subject : Probabilistic trajectory predictors and optimization of aircraft trajectory in random Met environments

Publications

Workshop

Talks

Lectures


Background

Short CURRICULUM VITAE

Researcher employed by Meteo-France at the National Research Center, Toulouse since 2001.

Ph.D. degree, Paul Sabatier University, Toulouse, Speciality : Applied Mathematics, option Probability, 2008.

M.Sc in Applied Mathematics, Probability Option, Université Paul Sabatier, Toulouse, 2004.
Maitrise in Mathematical Engineering, Université Paul Sabatier, Toulouse, 2003.
Maitrise in Mathematics and Fundamental Applications, Université Pierre et Marie Curie, Paris 6, 1998.
B.Sc in Pure Mathematics, Université Pierre et Marie Curie, Paris 6, 1996.


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