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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 2T. Capelle, P. Sturm, A. Vidard, B. Morton.

    Calibration of the Tranus Land Use Module: Optimisation-Based Algorithms, their Validation, and Parameter Selection by Statistical Model Selection, in: Computers, Environment and Urban Systems, September 2019, vol. 77, pp. 101146:1-13. [ DOI : 10.1016/j.compenvurbsys.2017.04.009 ]

    https://hal.inria.fr/hal-01519654
  • 3V. Chabot, M. Nodet, A. Vidard.

    Multiscale Representation of Observation Error Statistics in Data Assimilation, in: Sensors, 2019, pp. 1-19, forthcoming.

    https://hal.inria.fr/hal-02421699
  • 4X. Couvelard, F. Lemarié, G. Samson, J.-L. Redelsperger, F. Ardhuin, R. Benshila, G. Madec.

    Development of a 2-way coupled ocean-wave model: assessment on a global NEMO(v3.6)-WW3(v6.02) coupled configuration, in: Geoscientific Model Development Discussions, August 2019, pp. 1-36. [ DOI : 10.5194/gmd-2019-189 ]

    https://hal.inria.fr/hal-02267188
  • 5L. Debreu, N. K.-R. Kevlahan, P. Marchesiello.

    Brinkman volume penalization for bathymetry in three-dimensional ocean models, in: Ocean Modelling, January 2020, vol. 145, pp. 1-13. [ DOI : 10.1016/j.ocemod.2019.101530 ]

    https://hal.inria.fr/hal-02416084
  • 6J. Demange, L. Debreu, P. Marchesiello, F. Lemarié, E. Blayo, C. Eldred.

    Stability analysis of split-explicit free surface ocean models: implication of the depth-independent barotropic mode approximation, in: Journal of Computational Physics, December 2019, vol. 398, no 108875, pp. 1-26. [ DOI : 10.1016/j.jcp.2019.108875 ]

    https://hal.inria.fr/hal-01947706
  • 7M. R. El Amri, C. Helbert, O. Lepreux, M. Munoz Zuniga, C. Prieur, D. Sinoquet.

    Data-driven stochastic inversion via functional quantization, in: Statistics and Computing, 2019, pp. 1-17, forthcoming. [ DOI : 10.1007/s11222-019-09888-8 ]

    https://hal-ifp.archives-ouvertes.fr/hal-02291766
  • 8C. Eldred, T. Dubos, E. Kritsikis.

    A Quasi-Hamiltonian Discretization of the Thermal Shallow Water Equations, in: Journal of Computational Physics, February 2019, vol. 379, pp. 1-31. [ DOI : 10.1016/j.jcp.2018.10.038 ]

    https://hal.inria.fr/hal-01847698
  • 9C. Eldred, D. Le Roux.

    Dispersion analysis of compatible Galerkin schemes on quadrilaterals for shallow water models, in: Journal of Computational Physics, June 2019, vol. 387, pp. 539-568. [ DOI : 10.1016/j.jcp.2019.02.009 ]

    https://hal.archives-ouvertes.fr/hal-01916382
  • 10P. Etoré, C. Prieur, D. K. Pham, L. Li.

    Global sensitivity analysis for models described by stochastic differential equations, in: Methodology and Computing in Applied Probability, July 2019, pp. 1-29, https://arxiv.org/abs/1811.08101. [ DOI : 10.1007/s11009-019-09732-6 ]

    https://hal.archives-ouvertes.fr/hal-01926919
  • 11L. Gilquin, E. Arnaud, C. Prieur, A. Janon.

    Making best use of permutations to compute sensitivity indices with replicated orthogonal arrays, in: Reliability Engineering and System Safety, July 2019, vol. 187, pp. 28-39. [ DOI : 10.1016/j.ress.2018.09.010 ]

    https://hal.inria.fr/hal-01558915
  • 12F. Lemarié, H. Burchard, L. Debreu, K. Klingbeil, J. Sainte-Marie.

    Advancing dynamical cores of oceanic models across all scales, in: Bulletin of the American Meteorological Society, 2019, vol. 100, pp. ES109–ES115. [ DOI : 10.1175/BAMS-D-18-0303.1 ]

    https://hal.inria.fr/hal-01939057
  • 13L. Li, A. Vidard, F.-X. Le Dimet, J. Ma.

    Topological data assimilation using Wasserstein distance, in: Inverse Problems, January 2019, vol. 35, no 1, 015006 p. [ DOI : 10.1088/1361-6420/aae993 ]

    https://hal.inria.fr/hal-01960206
  • 14A. W. Moore, M. J. Martin, S. Akella, H. G. Arango, M. Balmaseda, L. Bertino, S. Ciavatta, B. D. Cornuelle, J. Cummings, S. Frolov, P. Lermusiaux, P. Oddo, P. R. Oke, A. Storto, A. Teruzzi, A. Vidard, A. T. Weaver.

    Synthesis of Ocean Observations Using Data Assimilation for Operational, Real-Time and Reanalysis Systems: A More Complete Picture of the State of the Ocean, in: Frontiers in Marine Science, March 2019, vol. 6, no 90, pp. 1-7. [ DOI : 10.3389/fmars.2019.00090 ]

    https://hal.inria.fr/hal-02421672
  • 15C. Prieur, L. Viry, E. Blayo, J.-M. Brankart.

    A global sensitivity analysis approach for marine biogeochemical modeling, in: Ocean Modelling, July 2019, vol. 139, no 101402, pp. 1-38. [ DOI : 10.1016/j.ocemod.2019.101402 ]

    https://hal.inria.fr/hal-01952797
  • 16L. Renault, F. Lemarié, T. Arsouze.

    On the implementation and consequences of the oceanic currents feedback in ocean-atmosphere coupled models, in: Ocean Modelling, September 2019, vol. 141, 101423 p. [ DOI : 10.1016/j.ocemod.2019.101423 ]

    https://hal.inria.fr/hal-02190847
  • 17V. Shutyaev, F.-X. Le Dimet, E. Parmuzin.

    Sensitivity of response functions in variational data assimilation for joint parameter and initial state estimation, in: Journal of Computational and Applied Mathematics, 2019, pp. 1-14. [ DOI : 10.1016/j.cam.2019.112368 ]

    https://hal.inria.fr/hal-02431701
  • 18K. Smetana, O. Zahm, A. T. Patera.

    Randomized residual-based error estimators for parametrized equations, in: SIAM Journal on Scientific Computing, March 2019, vol. 41, no 2, pp. A900-A926, https://arxiv.org/abs/1807.10489. [ DOI : 10.1137/18M120364X ]

    https://hal.archives-ouvertes.fr/hal-01851462
  • 19P. Tencaliec, A.-C. Favre, P. Naveau, C. Prieur, G. Nicolet.

    Flexible semiparametric Generalized Pareto modeling of the entire range of rainfall amount, in: Environmetrics, 2019, pp. 1-28, forthcoming. [ DOI : 10.1002/env.2582 ]

    https://hal.inria.fr/hal-01709061

Invited Conferences

  • 20E. Blayo.

    Vers une meilleure simulation du couplage océan-atmosphère, in: 2019 - Journées Tarantola : défis en géosciences, Paris, France, June 2019.

    https://hal.inria.fr/hal-02415133
  • 21F. Lemarié.

    An overview of the ocean-atmosphere coupling, in: 2019 - Physics-Dynamics Coupling in Earth System Models, Banff, Canada, October 2019.

    https://hal.inria.fr/hal-02418164

Conferences without Proceedings

  • 22E. Blayo, F. Lemarié, C. Pelletier, S. Théry.

    Toward an improved simulation of ocean-atmosphere interactions, in: 2019 - conférence Modélisation Océan-Atmosphère, Rennes, France, September 2019.

    https://hal.inria.fr/hal-02415136
  • 23F. Lemarié, G. Samson, X. Couvelard, G. Madec, R. Bourdallé-Badie.

    Recent developments in NEMO within the Albatross project, in: DRAKKAR 2019 - Drakkar annual workshop, Grenoble, France, January 2019.

    https://hal.inria.fr/hal-02418218
  • 24F. Lemarié, G. Samson, J.-L. Redelsperger, G. Madec, H. Giordani, R. Bourdallé-Badie.

    Toward an improved representation of air-sea interactions in high-resolution global oceanic forecasting systems, in: IMMERSE 2019 - IMMERSE Kick-Off Meeting, Grenoble, France, January 2019.

    https://hal.inria.fr/hal-02418229

Other Publications

References in notes
  • 37A. Beljaars, E. Dutra, G. Balsamo, F. Lemarié.

    On the numerical stability of surface-atmosphere coupling in weather and climate models, in: Geoscientific Model Development Discussions, 2017, vol. 10, no 2, pp. 977-989. [ DOI : 10.5194/gmd-10-977-2017 ]

    https://hal.inria.fr/hal-01406623
  • 38K. Bertin, N. Klutchnikoff, J. León, C. Prieur.

    Adaptive density estimation on bounded domains under mixing conditions, December 2018, working paper or preprint.

    https://hal.inria.fr/hal-01934913
  • 39P. Cattiaux, J. R. Leon, C. Prieur.

    Estimation for Stochastic Damping Hamiltonian Systems under Partial Observation. I. Invariant density, in: Stochastic Processes and their Applications, March 2014, vol. 124, no 3, pp. 1236-1260. [ DOI : 10.1016/j.spa.2013.10.008 ]

    https://hal.archives-ouvertes.fr/hal-00739136
  • 40P. Cattiaux, J. R. Leon, C. Prieur.

    Estimation for Stochastic Damping Hamiltonian Systems under Partial Observation. II Drift term, in: ALEA (Latin American Journal of Probability and Statistics), 2014, vol. 11, no 1, pp. 359-384.

    https://hal.archives-ouvertes.fr/hal-00877054
  • 41P. Cattiaux, J. R. Leon, C. Prieur.

    Recursive Estimation for Stochastic Damping Hamiltonian Systems, in: Journal of Nonparametric Statistics, 2015, vol. 27, no 3, pp. 401-424.

    https://hal.archives-ouvertes.fr/hal-01071252
  • 42P. Cattiaux, J. R. León, A. Pineda Centeno, C. Prieur.

    An overlook on statistical inference issues for stochastic damping Hamiltonian systems under the fluctuation-dissipation condition, in: Statistics, 2017, vol. 51, no 1, pp. 11-29. [ DOI : 10.1080/02331888.2016.1259807 ]

    https://hal.archives-ouvertes.fr/hal-01405427
  • 43M. R. El Amri, C. Helbert, O. Lepreux, M. Munoz Zuniga, C. Prieur, D. Sinoquet.

    Data-driven stochastic inversion under functional uncertainties, February 2018, working paper or preprint.

    https://hal.inria.fr/hal-01704189
  • 44F. Gamboa, A. Janon, T. Klein, A. Lagnoux, et al. .

    Sensitivity analysis for multidimensional and functional outputs, in: Electronic Journal of Statistics, 2014, vol. 8, no 1, pp. 575–603.
  • 45L. Gilquin, E. Arnaud, C. Prieur, H. Monod.

    Recursive estimation procedure of Sobol' indices based on replicated designs, January 2016, working paper or preprint.

    https://hal.inria.fr/hal-01291769
  • 46L. Gilquin.

    Monte Carlo and quasi-Monte Carlo sampling methods for the estimation of Sobol' indices. Application to a LUTI model, Université Grenoble Alpes, October 2016.

    https://hal.inria.fr/tel-01403914
  • 47D. P. Kingma, J. Ba.

    Adam: A Method for Stochastic Optimization, 2014.
  • 48M. Lamboni, H. Monod, D. Makowski.

    Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models, in: Reliability Engineering & System Safety, 2011, vol. 96, no 4, pp. 450–459.
  • 49F. Lemarié, E. Blayo, L. Debreu.

    Analysis of ocean-atmosphere coupling algorithms : consistency and stability, in: Procedia Computer Science, 2015, vol. 51, pp. 2066–2075. [ DOI : 10.1016/j.procs.2015.05.473 ]

    https://hal.inria.fr/hal-01174132
  • 50F. Lemarié.

    Numerical modification of atmospheric models to include the feedback of oceanic currents on air-sea fluxes in ocean-atmosphere coupled models, Inria Grenoble - Rhône-Alpes ; Laboratoire Jean Kuntzmann ; Universite de Grenoble I - Joseph Fourier ; Inria, August 2015, no RT-0464.

    https://hal.inria.fr/hal-01184711
  • 51F. Lemarié.

    On the discretization of vertical diffusion in the turbulent surface and planetary boundary layers, in: PDC 2018 - 3rd workshop on Physics Dynamics Coupling, Reading, United Kingdom, July 2018.

    https://hal.inria.fr/hal-01947691
  • 52J. R. Leon, A. Samson.

    Hypoelliptic stochastic FitzHugh-Nagumo neuronal model: mixing, up-crossing and estimation of the spike rate, in: Annals of Applied Probability, 2017.

    https://hal.archives-ouvertes.fr/hal-01492590
  • 53V. Oerder, F. Colas, V. Echevin, S. Masson, F. Lemarié.

    Impacts of the Mesoscale Ocean-Atmosphere Coupling on the Peru-Chile Ocean Dynamics: The Current-Induced Wind Stress Modulation, in: Journal of Geophysical Research. Oceans, February 2018, vol. 123, no 2, pp. 812-833. [ DOI : 10.1002/2017JC013294 ]

    https://hal.inria.fr/hal-01661645
  • 54A. B. Owen.

    Sobol' indices and Shapley value, in: Journal on Uncertainty Quantification, 2014, vol. 2, pp. 245–251.
  • 55A. B. Owen, C. Prieur.

    On Shapley value for measuring importance of dependent inputs, in: SIAM/ASA Journal on Uncertainty Quantification, September 2017, vol. 51, no 1, pp. 986–1002. [ DOI : 10.1137/16M1097717 ]

    https://hal.archives-ouvertes.fr/hal-01379188
  • 56C. Pelletier, F. Lemarié, É. Blayo.

    A theoretical study of a simplified air-sea coupling problem including turbulent parameterizations, in: COUPLED PROBLEMS 2017 - VII International Conference on Computational Methods for Coupled Problems in Science and Engineering, Rhodes, Greece, M. Papadrakakis, E. Oñate, B. Schrefler (editors), International Center for Numerical Methods in Engineering (CIMNE) , June 2017, pp. 38-49.

    https://hal.archives-ouvertes.fr/hal-01659443
  • 57C. Pelletier, F. Lemarié, É. Blayo.

    Sensitivity analysis and metamodels for the bulk parameterization of turbulent air-sea fluxes, in: Quarterly Journal of the Royal Meteorological Society, December 2017. [ DOI : 10.1002/qj.3233 ]

    https://hal.inria.fr/hal-01663668
  • 58E. Plischke, E. Borgonovo, C. L. Smith.

    Global sensitivity measures from given data, in: European Journal of Operational Research, 2013, vol. 226, no 3, pp. 536–550.
  • 59E. Plischke.

    An effective algorithm for computing global sensitivity indices (EASI), in: Reliability Engineering & System Safety, 2010, vol. 95, no 4, pp. 354–360.
  • 60E. Song, B. L. Nelson, J. Staum.

    Shapley Effects for Global Sensitivity Analysis: Theory and Computation, Northwestern University, 2015.
  • 61S. Théry.

    Algorithmes de Schwarz et conditions absorbantes pour le couplage océan-atmosphère, in: CANUM 2018 - 44e Congrès National d'Analyse Numérique, Cap d'Agde, France, May 2018.

    https://hal.inria.fr/hal-01947885