Olivier Pannekoucke

Dr. Olivier Pannekoucke (HDR)
Professor

CNRM UMR 3589
INPT-ENM
CERFACS

News

The article "An anisotropic formulation of the parametric Kalman filter assimilation" is now published in Tellus A 10.1080/16000870.2021.1926660

This research article detail how to assimilate local observations and how predict the dynamics of the error covariance matrix with the PKF. The work relies on the parametric Kalman filter introduced in two previous contributions (Pannekoucke et al. 2016, Pannekoucke et al. 2018, )

Summary

In geophysics, the direct application of covariance matrix dynamics described by the Kalman filter (KF) is limited by the high dimension of such problems. The parametric Kalman filter (PKF) is a recent alternative to the ensemble Kalman filter, where the covariance matrices are approximated by a covariance model featured by a set of parameters. The covariance dynamics is then described by the time evolution of these parameters during the analysis and forecast cycles. This study focuses on covariance model parametrized by the variance and the local anisotropic tensor fields (VLATcov). The analysis step of the PKF for VLATcov in a 2D/3D domain is first introduced. Then, using 2D univariate numerical investigations, the PKF is shown to be able to provide a low numerical cost approximation of the Kalman filter analysis step, even for anisotropic error correlation functions. Moreover the PKF has been shown able to reproduce the KF over several assimilation cycles in a transport dynamics. An extension toward the multivariate situation is theoretically studied in a 1D domain.

Figure: Time evolution of the analysis-error variance field predicted by the PKF assimilation versus the one diagnosed from the EnKF estimated from a large ensemble (1000 members)

The article "A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective" is now published in Nonlin. Processes Geophys https://doi.org/10.5194/npg-28-1-2021

This research article estimate for the first time the model-error covariance statistics due to the discretization of the partial differential equation. The work relies on the parametric Kalman filter introduced in two previous contributions (Pannekoucke et al. 2016, Pannekoucke et al. 2018, )

Summary

This contribution addresses the characterization of the model-error covariance matrix from the new theoretical perspective provided by the parametric Kalman filter method which approximates the covariance dynamics from the parametric evolution of a covariance model. The classical approach to obtain the modified equation of a dynamics is revisited to formulate a parametric modelling of the model-error covariance matrix which applies when the numerical model is dissipative compared with the true dynamics. As an illustration, the particular case of the advection equation is considered as a simple test bed. After the theoretical derivation of the predictability-error covariance matrices of both the nature and the numerical model, a numerical simulation is proposed which illustrates the properties of the resulting model-error covariance matrix.

Figure: Diagnosis of the mode-error statistics for the advection solved by considering an Euler-upwind scheme: the model-error variance increases in time while the model-error length-scale is deformed by the stretching of the flow.

The article "PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations" is now published in Geo. Mod. Dev. https://doi.org/10.5194/gmd-13-3373-2020

This research article bridges physics and design of neural network. The ongoing version of the code is on github https://github.com/opannekoucke/pdenetgen (a snapshot is available here: DOI)

Summary

Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physicallyconsistent deep neural network architectures is an open issue. In the spirit of physics-informed NNs, PDE-NetGen package provides new means to automatically translate physical equations, given as PDEs, into neural network architectures. PDE-NetGen combines symbolic calculus and a neural network generator. The later exploits NN-based implementations of PDE solvers using Keras. With some knowledge of a problem, PDE-NetGen is a plug-and-play tool to generate physics-informed NN architectures. They provide computationally-efficient yet compact representations to address a variety of issues, including among others adjoint derivation, model calibration, forecasting, data assimilation as well as uncertainty quantification. As an illustration, the workflow is first presented for the 2D diffusion equation, then applied to the data-driven and physics-informed identification of uncertainty dynamics for the Burgers equation.

Figure: Sample of code generated with PDE-NetGen 1.0.

Figure: Illustration of the uncertainty propagation for the Burgers dynamics (a) and its trained generated NN (b).

Invited talk at the AI4OAC workshop, Brest, 20-25 January, 2020

In this talk I'll present neural network generator for evolution equations.

Talk at the Climath WK2 "Big Data, Data Assimilation, Uncertainty quantification", Institute Henri Poincaré, Paris, 12-15 November, France, 2019.

In this talk I'll present symbolic tools for the computation of the parametric Kalman filter dynamics and the neural network generator for evolution equations. In particular, I'll show how to merge known physical dynamics and deep learning to learn unknown processes.

Invited talk at the European Geosciences Union Vienna 7-12 April 2019

In this talk I'll show how the parametric Kalman filter can improve our understanding of the model error.

"Parametric covariance dynamics for the nonlinear diffusive Burgers equation" is published in Nonlinear Processes in Geophysics

This research article shows the ability of the parametric Kalman filter to predict the dynamics of the uncertainty at the tangent-linear approximation in the Burgers equation, and without any ensemble estimation.

Short Summary

The forecast of weather prediction uncertainty is a real challenge and is crucial for risk management. However, uncertainty prediction is beyond the capacity of supercomputers, and improvements of the technology may not solve this issue. A new uncertainty prediction method is introduced which takes advantage of fluid equations to predict simple quantities which approximate real uncertainty but at a low numerical cost. A proof of concept is shown by an academic model derived from fluid dynamics.

PKF prediction of Burgers uncertainty
Figure: Illustration of the uncertainty predicted from the PKF equations versus the ensemble estimation (corresponding here to the reference)

Publications

Research articles

Codes in open source

  1. O. Pannekoucke SymPKF: a symbolic and computational toolbox for the design of parametric Kalman filter dynamics whose snapshot can be refered from the doi 10.5281/zenodo.4608514
  2. O. Pannekoucke PDE-NetGen: from symbolic PDE representations of physical processes to trainable neural network representations whose snapshot can be refered from the doi 10.5281/zenodo.3891100
  3. O. Pannekoucke CAC-PKF-M (v0.1): Computer-aided calculation of PKF dynamics with Maxima whose snapshot can be refered from the doi 10.5281/ZENODO.4708316

Research articles

ORCID : 0000-0002-3249-2818

  1. R. Fablet, B. Chapron, L. Drumetz, E. Mémin, O. Pannekoucke and F. Rousseau, Learning Variational Data Assimilation Models and Solvers. preprint HAL, 2020.
  2. C. Besombes, O. Pannekoucke, C. Lapeyre, B. Sanderson, and O. Thual, “Producing realistic climate data with GANs” 2021, accepted in NPG doi: 10.5194/npg-2021-6
  3. O. Pannekoucke, An anisotropic formulation of the parametric Kalman filter assimilation,Tellus A: Dynamic Meteorology and Oceanography, vol. 73, no. 1, pp. 1–27, Jan. 2021, doi: 10.1080/16000870.2021.1926660.
  4. O. Pannekoucke and R. Ménard, M. El Aabaribaoune and M. Plu, A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective, Nonlin. Processes Geophys., 28, 1–22, 2021. https://doi.org/10.5194/npg-28-1-2021 The paper has been selected as the Paper of the Month for January issue (2021) by the NP division of EGU
  5. O. Pannekoucke and R. Fablet, PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations, Geosci. Model Dev., 13, 3373–3382, 2020. https://doi.org/10.5194/gmd-13-3373-2020,
    snapshot of the code associated to the article: DOI
    ongoing version on github: https://github.com/opannekoucke/pdenetgen
  6. O. Pannekoucke; Bocquet, M. & Ménard, R. Parametric covariance dynamics for the nonlinear diffusive Burgers equation Nonlinear Processes in Geophysics, 1-21. (2018) https://doi.org/10.5194/npg-25-481-2018, The article is one of the 2018 hightlight articles at the EGU
  7. O. Pannekoucke Ricci, S.; Barthelemy, S.; Ménard, R. & Thual., O. Parametric Kalman filter for chemical transport models - Corrigendum Tellus A: Dynamic Meteorology and Oceanography, 70, 1-2 (2018) link
  8. 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 (2016) link
  9. O. Pannekoucke, S. Ricci, S. Barthelemy, R. Menard and O. Thual, Parametric Kalman filter for Chemical Transport Models. Tellus A, 68:31547, (2016). https://doi.org/10.3402/tellusa.v68.31547
  10. 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 (2016) link
  11. 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. (2016) link
  12. 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 (2014) link
  13. R. Mechri, C. Ottle, O. Pannekoucke and A. Kallel. Genetic Particle Filter application to Land Surface Temperature downscaling in Journal of Geophysical Research. (2014) link
  14. 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
  15. 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. Solar Energy (2014) link
  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. Solar Energy (2014) link
  17. O. Pannekoucke, E. Emili and O. Thual, Modeling of local length-scale dynamics and isotropizing deformations, Q. J. R. Meteorol. Soc. (2014) link
  18. M. Boisserie, Ph. Arbogast, L. Descamps, O. Pannekoucke, L. Raynaud. Estimating and diagnosing model error variances in the Meteo-France global NWP model, Q. J. R. Meteorol. Soc. (2014) link
  19. O. Pannekoucke, L. Raynaud and M. Farge, A wavelet-based filtering of ensemble background-error variances, Q. J. R. Meteorol. Soc. 140:846--854 (2014) link
  20. L. Raynaud and O. Pannekoucke, Sampling properties and spatial filtering of ensemble background-error length-scales, Q. J. R. Meteorol. Soc. 139:784--794 (2013) link
  21. L. Raynaud and O. Pannekoucke. Heterogeneous filtering of ensemble-based background-error variances. Q. J. R. Meteorol. Soc. 138: 1589--1598 (2012) link
  22. 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 (2012) link
  23. 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
  24. 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
  25. 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
  26. O. Pannekoucke and L. Vezard. Stochastic integration for the heterogeneous correlation modeling using a diffusion equation. Mon. Wea. Rev. 138: 3356--3365 (2010) link
  27. 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. link special issue
  28. 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
  29. 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). link
  30. 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). link
  31. 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

Proceedings

  1. 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
  2. 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 à l'inversion du CO2 a mesoechelle, "atelier de modelisation de l'atmosphere 2010", Toulouse, du 26 au 28 janvier 2010.
  3. 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.
  4. 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.)
  5. 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

Reports/Books

  1. 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
  2. O. Pannekoucke. Dynamique et modelisation de l'information dans les modeles meteorologique. Habilitation dissertation. Novembre 2012.
  3. 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).
  4. 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 ) pdf
  5. O. Pannekoucke and C. Baehr. Kalman Filters Family in Geoscience and Beyond. chapter in link Nova Science (Publisher).
  6. 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 link

Talks

  1. O. Pannekoucke, R. Fablet, S. Ricci, R. Menard, M. Bocquet, O. Thual. Design of the parametric Kalman filter dynamics : from the symbolic computation to the numerical integration., Climath WK2 "Big Data, Data Assimilation, Uncertainty quantification", Institute Henri Poincaré, Paris, 12-15 November, France, 2019.
  2. O. Pannekoucke, S. Ricci, R. Menard, M. Bocquet, O. Thual. Parametric Kalman filter : toward an alternative to the EnKF? Adjoint Workshop on Sensitivity Analysis and Data Assimilation in Meteorology and Oceanography 1-6 July 2018, Aveiro, Portugal.
  3. O. Pannekoucke, S. Ricci, R. Menard, M. Bocquet, O. Thual. Parametric Kalman filter : toward an alternative to the EnKF? Numerical model, predictability and data assimilation in weather, ocean and climate. A Symposium Honoring the Legacy of Anna Trevisan, Bologna, 17-20 October, 2017
  4. O. Pannekoucke, S. Ricci, R. Menard, M. Bocquet, O. Thual. Parametric Kalman filter : toward an alternative to the EnKF? 12th International EnKF workshop June 12-14, 2017
  5. O. Pannekoucke, E. Emili, O. Thual. Modeling of local length-scale dynamics and isotropizing deformations: formulation in natural coordinate system. AMMCS-CAIMS, 2015, June 7-12, Waterloo, Ohayo.
  6. O. Pannekoucke, E. Emili, O. Thual, Modelling of local length-scale dynamics and isotropizing deformations: formulation in natural coordinate system. in World Weather Open Science Conference, Montreal, Canada, 2014.
  7. R. Mechri, C. Ottle, O. Pannekoucke and Kallel, A. Genetic Particle Smoother Thermal Sharpener : Methodology and application to pseudo-observations, 1st international Conference on Advanced Technologies for Signal & Image Processing ATSIP’2014
  8. Oger N., Pannekoucke O., Doeurenbecher, A. and Arbogast, Ph., Sensitivity of the KFS to the trajectory of reference. 9^th Workshop on Adjoint Model Applications in Dynamic Meteorology, 10-41 October 2011, Cefalu, Sicily, Italy.
  9. Ricci S., Pannekoucke O., Ninove F. and Thual O., Emulation of a Kalman Filter algorithm on a diffusive flood wave propagation model. AGU Fall Meeting, August, 2011.
  10. Pannekoucke O. Ateliers de Modélisation de l'Atmosphère, 26 - 28 Janvier 2010, Toulouse.
  11. Pannekoucke O., Non separable diffusion and wavelet covariance model 9th EMS / 9th ECAM, 28 Sept. - 3 Oct. 2009, Toulouse.
  12. Pannekoucke O., Berre L. and Desroziers G. 7th Adjoint Worshop on Ajoint Applications in Dynamic Meteorology, Innsbruck, Austria, 8-13 October 2006.

Invited talks

  1. O. Pannekoucke, BIRS Workshop on Mathematical approaches for data assimilation of atmospheric constituents and inverse modeling website, Banff, Canada, October, 2020
  2. O. Pannekoucke, Neural Network Generator: from the physics to the deep learning AI4OAC, Brest, 20-25 January, 2020
  3. O. Pannekoucke, S. Ricci, R. Menard, M. Bocquet, O. Thual. Parametric Kalman filter : modelization of the model error? EGU 2019, presentation in session 'Inverse Problems, Data Assimilation and Uncertainty Quantification in Geoscience' (NP5.1/AS5.18/HS3.6/OS4.21) of the General Assembly of the European Geosciences Union, Vienna 7-12 April 2019. website
  4. O. Pannekoucke, S. Ricci, R. Menard, M. Bocquet, O. Thual. Parametric Kalman filter : toward an alternative to the EnKF? Conférence - Colloque National d’Assimilation de Données Rennes, du 26 septembre au 28 septembre 2018.
  5. O. Pannekoucke, 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.
  6. O. Pannekoucke, « practical use of the length-scale » in Workshop « Theoretical aspects of ensemble data assimilation for the Earth system » Les Houches, 6—10 Avril 2015.
  7. O. Pannekoucke « Optimisation pour la prévision numérique opérationnelle ». Présentation orale invitée. Journées Mathématiques de l'optimisation et de la décision (MODE) de la SMAI, Dijon, 28-30 Mars, 2012.
  8. O. Pannekoucke. « The use of wavelets in data assimilation ». Conférence ondelette au CNMAC (congré national de mathématique appliquées et numérique, Société Brésilienne de Mathématique Appliquée et Numérique), 17-21 Septembre, Aguas de Lindoia, Bresil, 2012.
  9. O. Pannekoucke. « Dynamics and modelling of information in geophysical model ». Working Group on PDE Control, CIRM, 5-9 Novembre, Marseille, France, 2012.
  10. O. Pannekoucke, Journées de Statistiques Rennaise « Ensemble methods for variational data assimilation and forecasting», 20-21 Octobre 2011, Rennes, France.
  11. O. Pannekoucke Thematic days on « Vortex @ Toulouse », IMFT Numerical Weather Prediction, IMFT, 27 Juin 2011, Toulouse, France.
  12. O. Pannekoucke Participation au groupe de travail « WAVELET -- Multiresolution and wavelet techniques for plasma and fluid turbulence. » CEMRACS 2010, CIRM, Marseille, France.
  13. O. Pannekoucke, seminar at Laboratoire de Météorologie Dynamique. Paris 25 Juin 2009.
  14. O. Pannekoucke, Berre L. and Desroziers G. Présentation orale invité au workshop on Ensemble Methods in Meteorology and Oceanography. SAMA-IPSL, Paris 15-16 Mai 2008, France.
  15. O. Pannekoucke, Berre L. and Desroziers G. Présentation orale invité au workshop on Mathematical Advancement in Geophysical Data Assimilation. Banff International Research Station for Mathematical Innovation and Discovery, Canada, 3-8 February 2008.
  16. O. Pannekoucke, Berre L. and Desroziers G. Présentation orale invité au workshop on Flow-dependent aspects of data assimilation, ECMWF,Research Department, Shinfield Park, Reading, England, 11-13 June 2007.

Posters

  1. O. Pannekoucke and Ronan Fablet. Automated NN generation: from the symbolic computation to the design of NN architectures for numerical predictions. ECMWF-ESA Virt. Workshop on ML for Earth System Observation and Prediction, 5-8 October 2020.
  2. O. Pannekoucke, Presentation of the Parametric Kalman Filter (KAPA) Colloque de Bilan et de Prospective du programme LEFE, Clermont-Ferrand, 23-31 Mars 2018.
  3. O. Pannekoucke, M. Bocquet, R. Menard. Parametric Covariance Propagation in the non-linear diffusive Burgers equation, 5th International Symposium on Data Assimilation, University of Reading, Reading, UK, 18-22 July 2016.
  4. O. Pannekoucke, S. Ricci, S. Barthelemy, R. Menard, O. Thual. Parametric Kalman filter for Chemical Transport Models, 5th International Symposium on Data Assimilation, University of Reading, Reading, UK, 18-22 July 2016.
  5. O. Pannekoucke, Berre L. and Desroziers G. Poster au Colloque National sur l'Assimilation de données, Toulouse, 9-10 mai 2006 .
  6. Pannekoucke O., Berre L. and Desroziers G. Poster au fourth WMO International Symposium on Assimilation of Observations in Meteorology and Oceanography, Prague, Czech Republic, 18-22 April 2005.