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Dr. Olivier Pannekoucke (hdr)


ORCID number: http://orcid.org/0000-0002-3249-2818
email: olivier dot pannekoucke at meteo dot fr
Searcher-Engineer for the French National Weather Service at
Centre National de Recherches Météoorologiques
CNRM  -GAME (Météo-France and CNRS) UMR 3589
42, Av Gaspard Coriolis
31057 Toulouse cedex 1
France
Tél. +33 (0) 5 61 07 93 57
Fax +33 (0) 5 61 07 84 53

Research interests

  • Theoretical and practical aspects on data assimilations (in particular: covariances modeling with spherical wavelets and the diffusion equation ; particle filters)
  • Dynamical systems and predictability
  • Numerical fluid dynamics
  • Toy models: 2D turbulence, quasi-geostrophic model...

Examples:

2D homogeneous turbulence on the sphere with/without rotation:
The sphere Planar projection: vorticity without rotation Planar projection: vorticity with rotation exciting Rossby waves Planar projection: potential vorticity with rotation
example at t=600h:
animation ( 20Mo)
example at t=600h: animation ( 20Mo) example at t=600h: animation ( 20Mo)

It corresponds to the dynamic $\frac{dq}{dt}=\partial_t q+\textbf{u}\nabla q=0$, where $q$ is the potential vorticity. In that case, $q=\zeta+f=\Delta \psi+f$, where $\psi$ is the stream function defined by $\textbf{u}=\textbf{k}\times\nabla\psi$, and $f=2\Omega\sin \phi$ the Coriolis parameter depending on the latitude $\psi$ and the angular velocity $\Omega$. When there is no rotation, $\Omega$ is null. Actually, in these simulations, a slight dissipation is used, so that $\frac{dq}{dt}=-\nu \Delta^2 q$.

2D homogeneous turbulence on a bi-periodic domain:
2D turbulence 2D Quasi-Geostrophic turbulence
example at t=6s: animation ( 20Mo) example at t=6s: animation ( 20Mo)

The two simulations lead to very different dynamics. The former is the classical 2D turbulence with $q=\Delta \psi$, while the latter is the quasi-geostrophic turbulence with $q=\Delta \psi -\lambda^{-2}\psi$, and where $\lambda=\frac{\sqrt{gH}}{f}$ is the Rossby radius of deformation for barotropic flow (also defined by $\lambda=\frac{N H}{f}$, for baroclinic flow with $N$ the Brunt-Väisälä frequency). The Rossby radius of deformation is the length scale at which rotational effects become as important as gravity wave (or buoyancy) effects in the evolution of the flow.

The physical understanding of the influence of $\lambda$ can be seen as follows: The dynamic of $q$ can be rewritten as $\frac{dq}{dt}=\partial_t q+J(\psi, q)=0$ where $J(a,b)=\partial_x a\partial_y b-\partial_y a\partial_x b$ is the Jacobian of two fields $a$ and $b$. For small scales ($l\ll \lambda$), the potential vorticity can be approximated by $q\approx\Delta\psi$ leading to a classical 2D turbulence. The reason is that Laplace operator is sensitive to small scales and thus it will dominate the contribution in $q$. Reversely, for the larger scales ($l\gg\lambda$), the Laplace operator is no more dominant, then the potential vorticity is $q\approx -\lambda^{-2}\psi$, leading to nullify the Jacobian $J(\psi,-\lambda^{-2}\psi)=0$ and to a stationary flow $\frac{dq}{dt}=\partial_t q=0$. Hence, after non-linear interactions (for $l\ll\lambda$) associated with a condensation of the potential vorticity into large scale structures (leading to $l\sim\lambda$), the flow is frozen (for $l\gg\lambda$) while motions remain i.e. the velocity is not nul.


Publications and reports

Publication or invited proceeding

Refeered documents are in bold

2016

25. O. Pannekoucke, E. Emili, and O. Thual. Modelling of Local Length-Scale Dynamics and Isotropizing Deformations: Formulation in Natural Coordinate System Mathematical and Computational Approaches in Advancing Modern Science and Engineering, Springer. link

24. O. Pannekoucke, P. Cebron, N. Oger, and P. Arbogast. From the Kalman Filter to the Particle Filter: A geometrical perspective of the curse of dimensionality. Advances in Meteorology, 2016, 9372786 link

23. O. Pannekoucke , S. Ricci, S. Barthelemy, R. Menard and O. Thual, Parametric Kalman filter for Chemical Transport Models. Tellus A, 68:31547, link .

22. Ph. Arbogast, O. Pannekoucke, L. Raynaud, R. Lalanne, and E. Memin. Object-oriented processing of CRM precipitation forecasts by stochastic filtering. Q. J. R. Meteorol. Soc. 142:2827—2838 link

21. R. Mechri, C. Ottle, O. Pannekoucke, A. Kallel, F. Maignan, D. Courault and I. Trigo, Downscaling Meteosat Land Surface Temperature over a Heterogeneous Landscape Using a Data Assimilation Approach Remote Sensing, MDPI AG, 2016, 8, 586. link

2014

20. L. Raynaud, O. Pannekoucke, P. Arbogast, and F. Bouttier. Application of a Bayesian weighting for short-range lagged ensemble forecasting at convective scale. Q. J. R. Meteorol. Soc. 141:459—468 link

19. R. Mechri, C. Ottle, O. Pannekoucke and A. Kallel. Genetic Particle Filter application to Land Surface Temperature downscaling Accepted in Journal of Geophysical Research.

18. E. Emili, B. Barret, S. Massart, E. Le Flochmoen, A. Piacentini, L. El Amraoui, O. Pannekoucke, and D. Cariolle. Combined assimilation of IASI   and MLS observations to constrain tropospheric and stratospheric ozone in a global chemical transport model. Atmos. Chem. Phys., 14, 177-198, 2014. link

17. M. Zamo, O. Mestre, Ph. Arbogast, and O. Pannekoucke, A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: deterministic forecast of hourly production, Accepted in Solar Energy

16. M. Zamo, O. Mestre, Ph. Arbogast, and O. Pannekoucke, A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part II: probabilistic forecast of daily production, Accepted in Solar Energy

15. O. Pannekoucke, E. Emili and O. Thual, Modeling of local length-scale dynamics and isotropizing deformations, Accepted in Q. J. R. Meteorol. Soc.

14. M. Boisserie, Ph. Arbogast, L. Descamps, O. Pannekoucke, L. Raynaud. Estimating and diagnosing model error variances in the Meteo-France global NWP   model, Accepted in Q. J. R. Meteorol. Soc. link

2013

13. O. Pannekoucke, L. Raynaud and M. Farge, A wavelet-based filtering of ensemble background-error variances, Q. J. R. Meteorol. Soc. 140:846—854
link

12. L. Raynaud and O. Pannekoucke, Sampling properties and spatial filtering of ensemble background-error length-scales, Q. J. R. Meteorol. Soc. 139:784—794
link

2012

11. L. Raynaud and O. Pannekoucke. Heterogeneous filtering of ensemble-based background-error variances. Q. J. R. Meteorol. Soc. 138: 1589—1598link

10. S. Massart, A. Piacentini, and O. Pannekoucke. How important is to use diagnosed background error covariances for the atmospheric ozone analysis? Q. J. R. Meteorol. Soc. 138: 889—905
link

9. N. Oger, O. Pannekoucke, A. Doerenbecher and P. Arbogast. Assessing the trajectory influence in adaptive observation Kalman filter
sensitivity method. Q. J. R. Meteorol. Soc.
138: 813—825 (2012)
link

8. S. Remy, O. Pannekoucke, T. Bergot and C. Baehr. Adaptation of a particle filtering method for data assimilation in a 1D numerical model used for fog forecasting. Q. J. R. Meteorol. Soc. 138: 536—551 (2012) link

2010

7. S. Massart, B. Pajot, A. Piacentini and O. Pannekoucke. On the merits of using a 3D-FGAT assimilation scheme with an outer loop for atmospheric situations governed by transport. Mon. Wea. Rev. 138:4509-4522. (2010)
link

6. O. Pannekoucke and L. Vezard. Stochastic integration for the heterogeneous correlation modeling using a diffusion equation. Mon. Wea. Rev. 138: 3356—3365 (2010) link

2009


5. O. Pannekoucke. Heterogeneous correlation modelling based on the wavelet diagonal assumption and on the diffusion operator. Mon. Wea. Rev. 137: 2995—3012 (2009). Special Issue on Mathematical Advances in Data Assimilation.
Preprint Official link : Journal issue /
Special issue

4. T. Lauvaux, O. Pannekoucke, C. Sarrat, F. Chevallier, P. Ciais, J. Noilhan and P.J.O Rayner. Structure of the transport uncertainty in mesoscale inversions of CO_2 sources and sinks using ensemble model simulations. Biogeosciences 6: 1089-1102 (2009).
link

2008


3. O. Pannekoucke and S. Massart. Estimation of the local diffusion tensor and normalization for heterogeneous correlation modelling using a diffusion. Q. J. R. Meteorol. Soc. 134: 1425—1438 (2008).
Preprint link

2. O. Pannekoucke, L. Berre and G. Desroziers. Background error correlation length-scale estimates and their sampling statistics. Q. J. R. Meteorol. Soc. 134: 497—508 (2008).
Preprint link

2007


L. Berre, O. Pannekoucke, G. Desroziers, S. E. Stefanescu, B. Chapnik, and L. Raynaud, 2007 : A variational assimilation ensemble and the spatial filtering of its error covariances : increase of sample size by local spatial averaging. Proceedings of the ECMWF   Workshop on Flow-dependent aspects
of data assimilation, 11-13 June 2007, pages 151—168.
link

1. O. Pannekoucke, L. Berre and G. Desroziers, Filtering properties of wavelets for the local background error correlations. Q. J. R. Meteorol. Soc. 133: 363—379 (2007).
link Official link

Reports/Books/Proceeding

2012

O. Pannekoucke. Dynamique et modelisation de l’information dans les modeles meteorologique. Habilitation dissertation. Novembre 2012.

2011

M. Farge, K. Schneider, O. Pannekoucke and R. Nguyen van Yen. 2011. Multiscale methods for fluid dynamics: fractals, self-similar random processes and wavelets. Chapter in "Handbook on environmental fluid dynamics", Taylor and Francis (Publisher).

2010

B. Pajot, S. Massart, D. Cariolle, A. Piacentini, O. Pannekoucke, W. Lahoz, C. Clerbaux, P. F. Coheur, and D. Hurtmans. High resolution assimilation of IASI   ozone data with a global CTM. In Concordiasi Workshop, Toulouse, France, Meteo-France/CNES  

O. Pannekoucke and C. Baehr.
Kalman Filters Family in Geoscience and Beyond.
chapter in "Kalman Filtering"
Nova Science (Publisher).

O. Pannekoucke, T. Lauvaux, C. Sarrat, P. Rayner, F. Chevallier et J. Noilhan.
Utilisation de previsions d’ensemble pour la modelisation des erreurs liees au transport applique a l’inversion du CO2 a mesoechelle, "atelier de modelisation de l’atmosphere 2010",
Toulouse, du 26 au 28 janvier 2010. pdf

2009

How important is to use diagnosed background error covariances for the atmospheric ozone analysis? S. Massart, A. Piacentini, and O. Pannekoucke. 5th WMO SYMPOSIUM ON DATA ASSIMILATION
Melbourne, Australia, 5 - 9 October 2009. pdf

C. Baehr and O. Pannekoucke. Some Issues and results on the EnKF and particule filters for meteorological models.
chapter in Chaotic Systems: Theory and Applications;
C. H. Skiadas and I. Dimotikalis (Editors)
World Scientific (Publisher) Proceeding of the 2nd Chaotic Modeling and Simulation International Conference 1 - 5 June 2009 Chania Crete Greece. (Chapter in Chaotic Systems: Theory and Applications ) link

2008

O. Pannekoucke. Modelisation des structures locales de covariance des erreurs de prevision a l’aide des ondelettes. Ph.D dissertation. Mars 2008.
Ph.D dissertation pdf

G. Desroziers, L. Berre, O. Pannekoucke, S. Ecaterina Stefenescu, P. Brousseau, L. Auger, B. Chapnik and L. Raynaud.
Flow-dependent error covariances from variational assimilation ensembles on global and regional domains
HIRLAM Technical Report No. 68, July 2008. (The SRNWP workshop on High resolution data assimilation with emphasis on the use of moisture-related observations was arranged 21-23 March 2007 at the Museum of Work, Norrkping, Sweden.)pdf

Reviewing job for

* Quarterly Journal of the Royal Meteorological Society (link)

* Monthly Weather Review (link)

* Inverse Problems in Science and Engineering (link)

* Ocean Modelling (link)

* Water Resources Research (link)

* Nonlinear Processes in Geophysics (link)


Invited workshop & seminar talks

Multi-scale issues in Data-assimilation for geophysical applications, Workshop on Multiscale Modeling and its Applications: From Weather and Climate Models to Models of Materials Defects, April, 25-29, 2016, Fields Institute.

Practical use of the length-scale. in Workshop on Theoretical aspects of ensemble data assimilation for the Earth system. Les Houches, 6—10 Avril 2015, France.

Optimization for numerical weather prediction. SMAI Workshop, Dijon, 28-30 Mars, 2012, France.

Wavelets in data assimilation, CNMAC 2012, Brazilian Society for Computational and Applied Mathematics, 17-21 Septembre, Aguas de Lindoia, Brazil 2012. Worshop.

Application of optimization for weather forecasting, SMAI / MODE, Orleans, 28th and 30 Mars, 2012. Workshop

Ensemble methods for variational data assimilation and forecasting. Rennes Statistics Days (Jstar), Rennes, 20th and 21 October, 2011. Workshop

Numerical forecasting of geophysical vortex. IMFT, Toulouse. June 2011. Seminar.

Subpixel temperature estimation by using a Particle Filter. LSCE, Orme des Merisiers. March 2010. Seminar.

Toward a full heterogeneous and non-separable background error covariance matrix. LMD. ENS rue Lhomond. June 2009. Seminar.

Background error correlation modeling: Representation of the local
length-scale from (“small†) ensembles
. BIRS: Mathematical Advancement in Geophysical Data Assimilation (Banff, Canada). February 2008. Workshop.

Diagnosis, estimation and modelization of forecast error covariances from an ensemble of perturbed forecasts. SAMA-IPSL: Ensemble Methods in Meteorology and Oceanography (Paris). March 2008. Workshop.

Spatial filtering of ensemble statistics: increase of sample size
by local spatial averaging
. CEPMMT Workshop on Flow-dependent aspects of data assimilation, (Reading UK), June 2007.


Teaching

Master level

Course on Stochastic forecasting (M2R CIRMA - ENSEEIHT & ENM, 2012—present)

Course on Data Assimilation (M2R OASC - Univ. Paul Sabatier Toulouse & ENM, 2010)

Course on Numerical modelling (M2R OASC - Univ. Paul Sabatier Toulouse & ENM, 2010)

Engineering school

Atmospheric waves and ray tracing (ENM, 2009— present)

Geophysical dynamics and 2D turbulence: Theoretical aspects, experiments and numerical simulations. (ENM, 2006— present)

Introduction to Linear and Non-linear dynamical systems.
Link for students : Animations of Lorenz’s model (ENSEEIHT, 2006—2008)

Project on rational mechanic. (ENSEEIHT, 2006—2008)

Summer school

Summer School CEA-EDF-INRIA d’Analyse Numérique "L’assimilation de données dans la simulation numérique", Paris, 26 juin-7 juillet 2006 : O. Pannekoucke, L. Berre et G. Desroziers. Introduction aux ondelettes - Modélisation des covariances d’erreurs de prévision à l’aide des ondelettes.


Students supervision

Research training periods (master level):

2012

Jonathan Guth: Particle filtering of potential vorticity for water vapour image (M2 student) co-supervision with Y. Michel(CNRM  ) and P. Arbogast (CNRM  ).

Jonathan Guerra: Theoretical Investigation of the Plume dynamics : The Role of Back Pressure (M2 student) co-supervision with Y.-I. Yano (CNRM  ).

Michael Zamo: Economical value of ensemble prediction system (M2 student) co-supervision with O. Mestre (ENM).

2011

Rihab Mechri: Subpixel temperature estimation by using a Particle Filter. (M2 Student) co-supervision with C. Ottle (LSCE).

Pierre Lafrique: Estimation of CO2 fluxes over the europe within MOCAGE-PALM. (M2 student of Univ. Paul Sabatier, Toulouse III) co-supervision with V.-H. Peuch (CNRM  ), J. C. Calvet (CNRM  ).

William Ohayon: 4D- Var Assimilation in the unstable subspace within a QG model. (M2 student of ENM / ENSEEIHT) co-supervision with O. Talagrand (LMD), A. Trevisan (IASC-CNR, Bologna).

Floriane Ninove: Assimilation within a 1D model of bief (M2 student Apply Mathematics UPS, co-supervision with S. Ricci (CERFACS)

Alain Delplanque, Damien Raynaud and Jonathan Guth: Assimilation within a 1D model of bief (M1 student ENM) co-supervision with S. Ricci (CERFACS)

2010

Niels OGER : Variability of the Kalman Filter Sensitivity when using an ensemble of forecasts (master 2 student ENM / ENSEEIHT / master IMFT). Award: "Prix URISMIP"

2009

Laurent VEZARD : Stochastic integration for the heterogeneous correlation modeling using a diffusion equation (master 1 students of INSA Toulouse)

Robin LOCATELLI, Niels OGER, Florian SUZAT : Hybrid formulation Ensemble Kalman filter and Particle Filter (master 1 student of Ecole Nationale d’Ingenieur la Météorologie). Co-supervision with Christophe Baehr (CNRM  ).

2007

Laura PEBERNET : Background Error Covariance Modeling using an Heterogeneous diffusion equation. (master 2 student of applied mathematics at Univ. of Pau).
Co-supervision with Sebastien MASSART (CERFACS).

2005

Cedric MARTEL : Spectral and Spatial Filtering of Background Error Standard deviation (master 2 student of applied mathematics at Univ. of Toulouse).
Co-supervision with Loïk BERRE (CNRM  ) and Gérald DESROZIERS (CNRM  ).


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