Bibliometry

Refereed publications

[129] M. Bocquet, A. Farchi, T. S. Finn, C. Durand, S. Cheng, Y. Chen, I. Pasmans, and A. Carrassi. Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble. Chaos, 29:091104, 2024. [ bib | DOI ]
[128] M. Bocquet, P. J. Vanderbecken, A. Farchi, J. Dumont Le Brazidec, and Y. Roustan. Bridging classical data assimilation and optimal transport: the 3D-Var case. Nonlin. Processes Geophys., 31:335--357, 2024. [ bib | DOI | http ]
[127] T. S. Finn, C. Durand, A. Farchi, M. Bocquet, and J. Brajard. Towards Diffusion Models for Large-Scale Sea-Ice Modelling, 2024. [ bib | DOI | arXiv | http ]
[126] T. S. Finn, L. Disson, A. Farchi, M. Bocquet, and C. Durand. Representation learning with unconditional denoising diffusion models for dynamical systems. Nonlin. Processes Geophys., 31:409--431, 2024. [ bib | DOI ]
[125] Y. Chen, P. Smith, A. Carrassi, I. Pasmans, L. Bertino, M. Bocquet, T. S. Finn, P. Rampal, and V. Dansereau. Multivariate state and parameter estimation with data assimilation on sea-ice models using a Maxwell-Elasto-Brittle rheology. The Cryosphere, 18:2381--2406, 2024. [ bib | DOI ]
[124] J. Dumont Le Brazidec, P. Vanderbecken, A. Farchi, M. Bocquet, G. Broquet, and G. Kuhlmann. Deep learning applied to CO2 power plant emissions quantification using simulated satellite images. Geosci. Model Dev., 17:1995--2014, 2024. [ bib | DOI | http ]
[123] E. Launay, V. Hergault, M. Bocquet, J. Dumont Le Brazidec, and Y. Roustan. Bayesian inversion of emissions from large urban fire using in situ observations. Atmos. Env., 323:120391, 2024. [ bib | DOI ]
[122] S. Driscoll, A. Carrassi, J. Brajard, L. Bertino, M. Bocquet, and E. Olason. Parameter sensitivity analysis of a sea ice melt pond parametrisation and its emulation using neural networks. J. Comput. Sci., 79:102231, 2024. [ bib | DOI ]
[121] C. Durand, T. S. Finn, A. Farchi, M. Bocquet, G. Boutin, and R. Ólason. Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic. The Cryosphere, 18:1791--1815, 2024. [ bib | DOI ]
[120] V. Eyring, W. D. Collins, P. Gentine, E. A. Barnes, M. Barreiro, T. Beucler, M. Bocquet, C. S. Bretherton, H. M. Christensen, K. Dagon, D. J. Gagne, D. Hall, D. Hammerling, S. Hoyer, F. Iglesias-Suarez, I. Lopez-Gomez, M. C. McGraw, G. A. Meehl, M. J. Molina, C. Monteleoni, J. Mueller, M. S. Pritchard, D. Rolnick, J. Runge, P. Stier, O. Watt-Meyer, K. Weigel, R. Yu, and L. Zanna. Pushing the frontiers in climate modelling and analysis with machine learning. Nat. Clim. Change, 14:916–928, 2024. [ bib | DOI ]
[119] M. Bocquet. Surrogate modelling for the climate sciences dynamics with machine learning and data assimilation. Front. Appl. Math. Stat., 9, 2023. [ bib | DOI ]
[118] S. Cheng, C. Quilodran-Casas, S. Ouala, A. Farchi, C. Liu, P. Tandeo, R. Fablet, D. Lucor, B. Iooss, J. Brajard, D. Xiao, T. Janjic, W. Ding, Y. Guo, A. Carrassi, M. Bocquet, and R. Arcucci. Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review. IEEE/CAA J. Autom. Sin., 10:1361--1387, 2023. [ bib | DOI ]
[117] C. Grudzien and M. Bocquet. A tutorial on Bayesian Data Assimilation. In Alik Ismail-Zadeh, Fabio Castelli, Dylan Jones, and Sabrina Sanchez, editors, Applications of Data Assimilation and Inverse Problems in the Earth Sciences, chapter 3, pages 27--48. Cambridge University Press, Cambridge, 2023. [ bib | DOI ]
[116] T. S. Finn, C. Durand, A. Farchi, M. Bocquet, Y. Chen, A. Carrassi, and V. Dansereau. Deep learning of subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology. The Cryosphere, 17:2965--2991, 2023. [ bib | DOI ]
[115] J. Dumont Le Brazidec, P. Vanderbecken, A. Farchi, M. Bocquet, J. Lian, G. Broquet, G. Kuhlmann, A. Danjou, and T. Lauvaux. Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants. Geosci. Model Dev., 16:3997--4016, 2023. [ bib | DOI ]
[114] J. Dumont Le Brazidec, M. Bocquet, O. Saunier, and Y. Roustan. Bayesian transdimensional inverse reconstruction of the 137Cs Fukushima-Daiichi release. Geosci. Model Dev., 16:1039--1052, 2023. [ bib | DOI ]
[113] A. Farchi, M. Chrust, M. Bocquet, P. Laloyaux, and M. Bonavita. Online Model Error Correction With Neural Networks in the Incremental 4D-Var Framework. J. Adv. Model. Earth Syst., 15:e2022MS003474, 2023. [ bib | DOI ]
[112] A. Carrassi, M. Bocquet, J. Demaeyer, C. Gruzien, P. N. Raanes, and S. Vannitsem. Data assimilation for chaotic dynamics. In Seon K. P. and Liang X., editors, Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV), pages 1--42. Springer International Publishing, Cham, 2022. [ bib | DOI ]
[111] Q. Malartic, A. Farchi, and M. Bocquet. State, global, and local parameter estimation using local ensemble Kalman filters: Applications to online machine learning of chaotic dynamics. Q. J. R. Meteorol. Soc., 148:2167--2193, 2022. [ bib | DOI ]
[110] C. Grudzien and M. Bocquet. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation. Geosci. Model Dev., 15:7641--7681, 2022. [ bib | DOI ]
[109] M. Bocquet, A. Farchi, and Q. Malartic. Online learning of both state and dynamics using ensemble Kalman filters. Found. Data Sci., 3:305--330, 2021. [ bib | DOI ]
[108] C. L. Defforge, B. Carissimo, M. Bocquet, R. Bresson, and P. Armand. Improving Numerical Dispersion Modelling in Built Environments with Data Assimilation Using the Iterative Ensemble Kalman Smoother. Boundary-Layer Meteor., 179:209--240, 2021. [ bib | DOI ]
[107] G. Evensen, J. Amezcua, M. Bocquet, A. Carrassi, A. Farchi, A. Fowler, P. Houtekamer, C. K. R. T. Jones, R. de Moraes, M. Pulido, C. Sampson, and F. C. Vossepoel. An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation. Found. Data Sci., 3:413--477, 2021. [ bib | DOI ]
[106] J. Brajard, A. Carrassi, M. Bocquet, and L. Bertino. Combining data assimilation and machine learning to infer unresolved scale parametrisation. Phil. Trans. R. Soc. A, 379:20200086, 2021. [ bib | DOI | arXiv ]
[105] A. Farchi, P. Laloyaux, M. Bonavita, and M. Bocquet. Using machine learning to correct model error in data assimilation and forecast applications. Q. J. R. Meteorol. Soc., 147:3067--3084, 2021. [ bib | DOI ]
[104] A. Farchi, M. Bocquet, P. Laloyaux, M. Bonavita, and Q. Malartic. A comparison of combined data assimilation and machine learning methods for offline and online model error correction. J. Comput. Sci., 55:101468, 2021. [ bib | DOI | .pdf ]
[103] A. Hutt, M. Bocquet, Carrassi A., L. Lei, and R. Potthast. Editorial: Data Assimilation of Nonlocal Observations in Complex Systems. Front. Appl. Math. Stat., 7:9, 2021. [ bib | DOI ]
[102] J. Dumont Le Brazidec, M. Bocquet, O. Saunier, and Y. Roustan. Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 106Ru event. Atmos. Chem. Phys., 2021:13247--13267, 2021. [ bib | DOI ]
[101] M. Bocquet, J. Brajard, A. Carrassi, and L. Bertino. Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Found. Data Sci., 2:55--80, 2020. [ bib | DOI | http ]
[100] J. Brajard, A. Carrassi, M. Bocquet, and L. Bertino. Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model. J. Comput. Sci., 44:101171, 2020. [ bib | DOI ]
[99] J. Dumont Le Brazidec, M. Bocquet, O. Saunier, and Y. Roustan. MCMC methods applied to the reconstruction of the autumn 2017 Ruthenium-106 atmospheric contamination source. Atmospheric Environment: X, 6:100071, 2020. [ bib | DOI ]
[98] M. Tondeur, A. Carrassi, S. Vannitsem, and M. Bocquet. On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter. J. Stat. Phys., 179:1161--1185, 2020. [ bib | DOI ]
[97] C. Grudzien, M. Bocquet, and A. Carrassi. On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments. Geosci. Model Dev., 13:1903--1924, 2020. [ bib | DOI | .pdf ]
[96] A. Fillion, M. Bocquet, S. Gratton, S. Gürol, and P. Sakov. An iterative ensemble Kalman smoother in presence of additive model error. SIAM/ASA J. Uncertain. Quantif., 8:198--228, 2020. [ bib | DOI ]
[95] P. Tandeo, P. Ailliot, M. Bocquet, A. Carrassi, T. Miyoshi, M. Pulido, and Y. Zhen. A Review of Innovation-Based Approaches to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation. Mon. Wea. Rev., 148:3973--3994, 2020. [ bib | DOI ]
[94] M. Bocquet and A. Farchi. On the consistency of the perturbation update of local ensemble square root Kalman filters. Tellus A, 71:1--21, 2019. [ bib | DOI ]
[93] M. Bocquet, J. Brajard, A. Carrassi, and L. Bertino. Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models. Nonlin. Processes Geophys., 26:143--162, 2019. [ bib | DOI ]
[92] L. I. Díaz Isaac, T. Lauvaux, M. Bocquet, and K. J. Davis. Calibration of a multi-physics ensemble for estimating the uncertainty of a greenhouse gas atmospheric transport model. Atmos. Chem. Phys., 19:5695--5718, 2019. [ bib | DOI ]
[91] T. Lauvaux, L. I. Díaz-Isaac, M. Bocquet, and N. Bousserez. Diagnosing spatial error structures in CO2 mole fractions and XCO2 column mole fractions from atmospheric transport. Atmos. Chem. Phys., 19:12007--12024, 2019. [ bib | DOI | http ]
[90] P. N. Raanes, M. Bocquet, and A. Carrassi. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures. Q. J. R. Meteorol. Soc., 145:53--75, 2019. [ bib | DOI | arXiv | .pdf ]
[89] C. L. Defforge, B. Carissimo, M. Bocquet, R. Bresson, and P. Armand. Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother. J. Wind. Eng. Ind. Aerod., 189:243--257, 2019. [ bib | DOI ]
[88] A. Farchi and M. Bocquet. On the efficiency of covariance localisation of the ensemble Kalman filter using augmented ensembles. Front. Appl. Math. Stat., 5:3, 2019. [ bib | DOI ]
[87] S. Metref, A. Hannart, J. Ruiz, M. Bocquet, A. Carrassi, and M. Ghil. Estimating model evidence using ensemble-based data assimilation with localization - The model selection problem. Q. J. R. Meteorol. Soc., 145:1571--1588, 2019. [ bib | DOI ]
[86] I. Hoteit, X. Luo, M. Bocquet, A. Köhl, and B. Ait-El-Fquih. Data Assimilation in Oceanography: Current Status and New Directions. In New Frontiers in Operational Oceanography, chapter 17, pages 465--511. GODAE OceanView, 2018. [ bib | DOI ]
[85] C. L. Defforge, B. Carissimo, M. Bocquet, P. Armand, and R. Bresson. Data assimilation at local scale to improve CFD simulations of atmospheric dispersion: application to 1D shallow-water equations and method comparisons. Int. J. Environ. Pollut., 64:90--109, 2018. [ bib | DOI ]
[84] A. Farchi and M. Bocquet. Review article: Comparison of local particle filters and new implementations. Nonlin. Processes Geophys., 25:765--807, 2018. [ bib | DOI ]
[83] O. Pannekoucke, M. Bocquet, and R. Ménard. Parametric covariance dynamics for the nonlinear diffusive Burgers equation. Nonlin. Processes Geophys., 25:481--495, 2018. [ bib | DOI | .pdf ]
[82] C. Grudzien, A. Carrassi, and M. Bocquet. Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error. Nonlin. Processes Geophys., 25:633--648, 2018. [ bib | DOI ]
[81] C. Grudzien, A. Carrassi, and M. Bocquet. Asymptotic forecast uncertainty and the unstable subspace in the presence of additive model error. SIAM/ASA J. Uncertain. Quantif., 6:1335–1363, 2018. [ bib | DOI ]
[80] A. Carrassi, M. Bocquet, L. Bertino, and G. Evensen. Data Assimilation in the Geosciences: An overview on methods, issues, and perspectives. WIREs Climate Change, 9:e535, 2018. [ bib | DOI | http ]
[79] T. Janjić, N. Bormann, M. Bocquet, J. A. Carton, S. E. Cohn, S. L. Dance, S. N. Losa, N. K. Nichols, R. Potthast, J. A. Waller, and P. Weston. On the representation error in data assimilation. Q. J. R. Meteorol. Soc., 144:1257--1278, 2018. [ bib | DOI ]
[78] P. Sakov and M. Bocquet. Asynchronous data assimilation with the EnKF in presence of additive model error. Tellus A, 70:1414545, 2018. [ bib | DOI | http ]
[77] P. Sakov, J.-M. Haussaire, and M. Bocquet. An iterative ensemble Kalman filter in presence of additive model error. Q. J. R. Meteorol. Soc., 144:1297--1309, 2018. [ bib | DOI | arXiv | .pdf ]
[76] A. Fillion, M. Bocquet, and S. Gratton. Quasi static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother. Nonlin. Processes Geophys., 25:315--334, 2018. [ bib | DOI | .pdf ]
[75] M. Pulido, P. Tandeo, M. Bocquet, A. Carrassi, and M. Lucini. Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods. Tellus A, 70:1442099, 2018. [ bib | DOI ]
[74] M. Bocquet and A. Carrassi. Four-dimensional ensemble variational data assimilation and the unstable subspace. Tellus A, 69:1304504, 2017. [ bib | DOI | http ]
[73] M. Bocquet, K. S. Gurumoorthy, A. Apte, A. Carrassi, C. Grudzien, and C. K. R. T. Jones. Degenerate Kalman filter error covariances and their convergence onto the unstable subspace. SIAM/ASA J. Uncertain. Quantif., 5:304--333, 2017. [ bib | DOI ]
[72] Y. Liu, J.-M. Haussaire, M. Bocquet, Y. Roustan, O. Saunier, and A. Mathieu. Uncertainty quantification of pollutant source retrieval: comparison of Bayesian methods with application to the Chernobyl and Fukushima-Daiichi accidental releases of radionuclides. Q. J. R. Meteorol. Soc., 143:2886--2901, 2017. [ bib | DOI ]
[71] A. Carrassi, M. Bocquet, A. Hannart, and M. Ghil. Estimating model evidence using data assimilation. Q. J. R. Meteorol. Soc., 143:866--880, 2017. [ bib | DOI ]
[70] Y. Wang, F. Counillon, I. Bethke, N. Keenlyside, M. Bocquet, and M.-L. Shen. Optimising assimilation of hydrographic profiles into isopycnal ocean models with ensemble data assimilation. Ocean Modelling, 114:33--44, 2017. [ bib | DOI ]
[69] M. Bocquet. Localization and the iterative ensemble Kalman smoother. Q. J. R. Meteorol. Soc., 142:1075--1089, 2016. [ bib | DOI ]
[68] A. Farchi, M. Bocquet, Y. Roustan, A. Mathieu, and A. Quérel. Using the Wasserstein distance to compare fields of pollutants: application to the radionuclide atmospheric dispersion of the Fukushima-Daiichi accident. Tellus B, 68:31682, 2016. [ bib | DOI | http ]
[67] A. Hannart, A. Carrassi, M. Bocquet, M. Ghil, P. Naveau, M. Pulido, J. Ruiz, and P. Tandeo. DADA: Data Assimilation for the Detection and Attribution of Weather-and Climate-related Events. Clim. Change, 136:155--174, 2016. [ bib | DOI | .pdf ]
[66] J.-M. Haussaire and M. Bocquet. A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes: L95-GRS (v1.0). Geosci. Model Dev., 9:393--412, 2016. [ bib | DOI | .pdf ]
[65] M. Bocquet, P. N. Raanes, and A. Hannart. Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation. Nonlin. Processes Geophys., 22:645--662, 2015. [ bib | DOI | .pdf ]
[64] M. Bocquet, H. Elbern, H. Eskes, M. Hirtl, R. Žabkar, G. R. Carmichael, J. Flemming, A. Inness, M. Pagowski, J. L. Pérez Camaño, P. E. Saide, R. San Jose, M. Sofiev, J. Vira, A. Baklanov, C. Carnevale, G. Grell, and C. Seigneur. Data Assimilation in Atmospheric Chemistry Models: Current Status and Future Prospects for Coupled Chemistry Meteorology Models. Atmos. Chem. Phys., 15:5325--5358, 2015. [ bib | DOI | .pdf ]
[63] M. Bocquet and P. Sakov. An iterative ensemble Kalman smoother. Q. J. R. Meteorol. Soc., 140:1521--1535, 2014. [ bib | DOI | http ]
[62] V. Winiarek, M. Bocquet, N. Duhanyan, Y Roustan, O. Saunier, and A. Mathieu. Estimation of the caesium-137 source term from the Fukushima Daiichi nuclear power plant using a consistent joint assimilation of air concentration and deposition observations. Atmos. Env., 82:268--279, 2014. [ bib | DOI ]
[61] M. Gray, C. Petit, S. Rodionov, M. Bocquet, L. Bertino, M. Ferrari, and T. Fusco. Local ensemble transform Kalman filter, a fast non-stationary control law for adaptive optics on ELTs: theoretical aspects and first simulation results. Optics Express, 22:20894--20913, 2014. [ bib | DOI | http ]
[60] Y. Wang, K. Sartelet, M. Bocquet, and P. Chazette. Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign. Atmos. Chem. Phys., 14:3511--3532, 2014. [ bib | DOI | .pdf ]
[59] Y. Wang, K. N. Sartelet, M. Bocquet, P. Chazette, M. Sicard, G. D'Amico, J.-F. Léon, L. Alados-Arboledas, A. Amodeo, P. Augustin, J. Bach, L. Belegante, I. Binietoglou, X. Bush, A. Comerón, H. Delbarre, D. García-Vízcaino, J. L. Guerrero-Rascado, M. Hervo, M. Iarlori, P. Kokkalis, D. Lange, F. Molero, N. Montoux, A. Muñoz, C. Muñoz, D. Nicolae, A. Papayannis, G. Pappalardo, J. Preissler, V. Rizi, F. Rocadenbosch, K. Sellegri, F. Wagner, and F. Dulac. Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin. Atmos. Chem. Phys., 14:12031--12053, 2014. [ bib | DOI | http ]
[58] M. Bocquet and P. Sakov. Joint state and parameter estimation with an iterative ensemble Kalman smoother. Nonlin. Processes Geophys., 20:803--818, 2013. [ bib | DOI | .pdf ]
[57] A. Mathieu, I. Korsakissok, D. Quélo, O. Saunier, J. Groëll, D. Didier, D. Corbin, J. Denis, M. Tombette, V. Winiarek, M. Bocquet, E. Quentric, and J.-P. Benoit. État de la modélisation pour simuler l'accident nucléaire de la centrale Fukushima Daiichi. Pollution Atmosphérique, 217, 2013. [ bib | http ]
[56] O. Saunier, A. Mathieu, D. Didier, M. Tombette, D. Quélo, V. Winiarek, and M. Bocquet. An inverse modeling method to assess the source term of the Fukushima Nuclear Power Plant accident using gamma dose rate observations. Atmos. Chem. Phys., 13:11403--11421, 2013. [ bib | DOI | .pdf ]
[55] M. R. Koohkan, M. Bocquet, Y. Roustan, Y. Kim, and C. Seigneur. Estimation of volatile organic compound emissions for Europe using data assimilation. Atmos. Chem. Phys., 13:5887--5905, 2013. [ bib | DOI | .pdf ]
[54] Y. Wang, K. Sartelet, M. Bocquet, and P. Chazette. Assimilation of ground versus lidar observations for PM10 forecasting. Atmos. Chem. Phys., 13:269--283, 2013. [ bib | DOI | .pdf ]
[53] L. Wu, M. Bocquet, F. Chevallier, T. Lauvaux, and K. Davis. Hyperparameter Estimation for Uncertainty Quantification in Mesoscale Carbon Dioxide Inversions. Tellus B, 65:20894, 2013. [ bib | DOI | http ]
[52] M. Bocquet and P. Sakov. Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems. Nonlin. Processes Geophys., 19:383--399, 2012. [ bib | DOI | .pdf ]
[51] M. Bocquet. Parameter field estimation for atmospheric dispersion: Application to the Chernobyl accident using 4D-Var. Q. J. R. Meteorol. Soc., 138:664--681, 2012. [ bib | DOI | http ]
[50] P. Chazette, M. Bocquet, P. Royer, V. Winiarek, J.-C. Raut, P. Labazuy, M. Gouhier, M. Lardier, and J.-P. Cariou. Eyjafjallajökull ash concentrations derived from both lidar and modeling. J. Geophys. Res., 117:D00U14, 2012. [ bib | DOI | http ]
[49] V. Winiarek, M. Bocquet, O. Saunier, and A. Mathieu. Estimation of Errors in the Inverse Modeling of Accidental Release of Atmospheric Pollutant: Application to the Reconstruction of the Cesium-137 and Iodine-131 Source Terms from the Fukushima Daiichi Power Plant. J. Geophys. Res., 117:D05122, 2012. [ bib | DOI | http ]
[48] V. Winiarek, M. Bocquet, O. Saunier, and A. Mathieu. Correction to "Estimation of Errors in the Inverse Modeling of Accidental Release of Atmospheric Pollutant: Application to the Reconstruction of the Cesium-137 and Iodine-131 Source Terms from the Fukushima Daiichi Power Plant". J. Geophys. Res., 117:D18118, 2012. [ bib | DOI | http ]
[47] M. R. Koohkan, M. Bocquet, L. Wu, and M. Krysta. Potential of the International Monitoring System radionuclide network for inverse modelling. Atmos. Env., 54:557--567, 2012. [ bib | DOI ]
[46] F. Chevallier, T. Wang, P. Ciais, F. Maignan, M. Bocquet, A. Arain, A. Cescatti, J.-Q. Chen, H. Dolman, B. E. Law, H. A. Margolis, L. Montagni, and E. J. Moors. What eddy-covariance flux measurements tell us about prior errors in CO2-flux inversion schemes. Global Biogeochem. Cy., 26:GB1021, 2012. [ bib | DOI | http ]
[45] Y. Zhang, M. Bocquet, V. Mallet, C. Seigneur, and A. Baklanov. Real-Time Air Quality Forecasting, Part I: History, Techniques, and Current Status. Atmos. Env., 60:632--655, 2012. [ bib | DOI ]
[44] Y. Zhang, M. Bocquet, V. Mallet, C. Seigneur, and A. Baklanov. Real-Time Air Quality Forecasting, Part II: State of the Science, Current Research Needs, and Future Prospects. Atmos. Env., 60:656--676, 2012. [ bib | DOI ]
[43] M. R. Koohkan and M. Bocquet. Accounting for representativeness errors in the inversion of atmospheric constituent emissions: Application to the retrieval of regional carbon monoxide fluxes. Tellus B, 64:19047, 2012. [ bib | DOI | http ]
[42] C. Estournel, E. Bosc, M. Bocquet, C. Ulses, P. Marsaleix, V. Winiarek, I. Osvath, C. Nguyen, T. Duhaut, F. Lyard, H. Michaud, and F. Auclair. Assessment of the amount of cesium-137 released to the Pacific Ocean after the Fukushima accident and analysis of its dispersion in the Japanese coastal waters. J. Geophys. Res., 117:C11014, 2012. [ bib | DOI | http ]
[41] M. Bocquet. Ensemble Kalman filtering without the intrinsic need for inflation. Nonlin. Processes Geophys., 18:735--750, 2011. [ bib | DOI | .pdf ]
[40] M. Bocquet and L. Wu. Bayesian design of control space for optimal assimilation of observations. II: Asymptotics solution. Q. J. R. Meteorol. Soc., 137:1357--1368, 2011. [ bib | DOI | http ]
[39] M. Bocquet, L. Wu, and F. Chevallier. Bayesian design of control space for optimal assimilation of observations. I: Consistent multiscale formalism. Q. J. R. Meteorol. Soc., 137:1340--1356, 2011. [ bib | DOI | http ]
[38] L. Wu and M. Bocquet. Optimal Redistribution Of The Background Ozone Monitoring Stations Over France. Atmos. Env., 45:772--783, 2011. [ bib | DOI ]
[37] L. Wu, M. Bocquet, T. Lauvaux, F. Chevallier, P. Rayner, and K. Davis. Optimal representation of source-sink fluxes for mesoscale carbon dioxide inversion with synthetic data. J. Geophys. Res., 116:D21304, 2011. [ bib | DOI | http ]
[36] P. Saide, M. Bocquet, A. Osses, and L. Gallardo. Constraining surface emissions of air pollutants using inverse modeling: method intercomparison and a new two-step multiscale approach. Tellus B, 63:360--370, 2011. [ bib | DOI | http ]
[35] V. Winiarek, J. Vira, M. Bocquet, M. Sofiev, and O. Saunier. Towards the operational estimation of a radiological plume using data assimilation after a radiological accidental atmospheric release. Atmos. Env., 45:2944--2955, 2011. [ bib | DOI ]
[34] M. Bocquet. Modélisation inverse des sources de pollution atmosphérique accidentelle : progrès récents. Pollution Atmosphérique, numéro spécial:151--160, 2010. [ bib ]
[33] M. Bocquet, C. A. Pires, and L. Wu. Beyond Gaussian statistical modeling in geophysical data assimilation. Mon. Wea. Rev., 138:2997--3023, 2010. [ bib | DOI | http ]
[32] C. A. Pires, O. Talagrand, and M. Bocquet. Diagnosis and impacts of non-Gaussianity of innovations in data assimilation. Physica D, 239:1701--1717, 2010. [ bib | DOI ]
[31] L. Wu, M. Bocquet, and M. Chevallier. Optimal Reduction of the Ozone Monitoring Network over France. Atmos. Env., 44:3071--3083, 2010. [ bib | DOI ]
[30] M. Bocquet. Towards optimal choices of control space representation for geophysical data assimilation. Mon. Wea. Rev., 137:2331--2348, 2009. [ bib | DOI | http ]
[29] R. Abida and M. Bocquet. Targeting of observations for accidental atmospheric release monitoring. Atmos. Env., 43:6312--6327, 2009. [ bib | DOI ]
[28] O. Saunier, M. Bocquet, A. Mathieu, and O. Isnard. Model reduction via principal component truncation for the optimal design of atmospheric monitoring networks. Atmos. Env., 43:4940--4950, 2009. [ bib | DOI ]
[27] M. Bocquet. Inverse modelling of atmospheric tracers: Non-Gaussian methods and second-order sensitivity analysis. Nonlin. Processes Geophys., 15:127--143, 2008. [ bib | DOI | .pdf ]
[26] M. Krysta, M. Bocquet, and J. Brandt. Probing ETEX-II data set with inverse modelling. Atmos. Chem. Phys., 8:3963--3971, 2008. [ bib | DOI | .pdf ]
[25] R. Abida, M. Bocquet, N. Vercauteren, and O. Isnard. Design of a monitoring network over France in case of a radiological accidental release. Atmos. Env., 42:5205--5219, 2008. [ bib | DOI ]
[24] H. Boisgontier, V. Mallet, J. P. Berroir, M. Bocquet, I. Herlin, and B. Sportisse. Satellite data assimilation for air quality forecast. Simulation Modelling practice and theory, 16:1541--1545, 2008. [ bib | DOI ]
[23] L. Wu, V. Mallet, M. Bocquet, and B. Sportisse. A Comparison Study of Data Assimilation Algorithms for Ozone Forecasts. J. Geophys. Res., 113:D20310, 2008. [ bib | DOI | http ]
[22] M. Bocquet and B. Sportisse. Modélisation inverse pour la qualité de l'air: éléments de méthodologie et exemples. Pollution Atmosphérique, 196:395--404, 2007. [ bib | .pdf ]
[21] M. Bocquet. High resolution reconstruction of a tracer dispersion event. Q. J. R. Meteorol. Soc., 133:1013--1026, 2007. [ bib | DOI | http ]
[20] D. Quélo, M. Krysta, M. Bocquet, O. Isnard, Y. Minier, and B. Sportisse. Validation of the Polyphemus platform on the ETEX, Chernobyl and Algeciras cases. Atmos. Env., 41:5300--5315, 2007. [ bib | DOI ]
[19] X. Davoine and M. Bocquet. Inverse modelling-based reconstruction of the Chernobyl source term available for long-range transport. Atmos. Chem. Phys., 7:1549--1564, 2007. [ bib | DOI | .pdf ]
[18] M. Krysta and M. Bocquet. Source reconstruction of an accidental radionuclide release at European scale. Q. J. R. Meteorol. Soc., 133:529--544, 2007. [ bib | DOI | http ]
[17] M. Krysta, M. Bocquet, B. Sportisse, and O. Isnard. Data Assimilation for Short-range Dispersion of Radionuclides: an Application to Wind Tunnel Data. Atmos. Env., 40:7267--7279, 2006. [ bib | DOI ]
[16] Y. Roustan and M. Bocquet. Sensitivity analysis for mercury over Europe. J. Geophys. Res., 111:D14304, 2006. [ bib | DOI | http ]
[15] Y. Roustan and M. Bocquet. Inverse modelling for mercury over Europe. Atmos. Chem. Phys., 6:3085--3098, 2006. [ bib | DOI | .pdf ]
[14] Y. Roustan, M. Bocquet, L. Musson Genon, and B. Sportisse. Modélisation du mercure, du plomb et du cadmium à l'échelle européenne. Pollution Atmosphérique, 191:317--326, 2006. [ bib | .pdf ]
[13] M. Bocquet. Reconstruction of an atmospheric tracer source using the principle of maximum entropy. II: Applications. Q. J. R. Meteorol. Soc., 131:2209--2223, 2005. [ bib | DOI | http ]
[12] M. Bocquet. Reconstruction of an atmospheric tracer source using the principle of maximum entropy. I: Theory. Q. J. R. Meteorol. Soc., 131:2191--2208, 2005. [ bib | DOI | http ]
[11] M. Bocquet. Grid resolution dependence in the reconstruction of an atmospheric tracer source. Nonlin. Processes Geophys., 12:219--233, 2005. [ bib | DOI | .pdf ]
[10] O. Isnard, M. Krysta, M. Bocquet, P. Dubiau, and B. Sportisse. Data assimilation of radionuclides atmospheric dispersion at small scale: a tool to assess the consequences of radiological emergencies. In Proceedings of the IAEA Conference. Rio Conference., 2005. [ bib ]
[9] M. Bocquet and J.T. Chalker. Network models for chiral symmetry classes of Anderson localisation. Ann. Henri Poincaré, 4, Suppl. 2:S539--S557, 2003. [ bib ]
[8] M. Bocquet and J.T. Chalker. Network models for localisation problems belonging to the chiral symmetry classes. Physical Review B, 67:054204, 2003. [ bib | arXiv ]
[7] M. Bocquet. Finite-temperature perturbation theory for quasi-one-dimensional spin-1/2 Heisenberg antiferromagnets. Physical Review B, 65:184415, 2002. [ bib | arXiv ]
[6] M. Bocquet, F.H.L Essler, A.M. Tsvelik, and A.O. Gogolin. Finite-temperature dynamical magnetic susceptibility of quasi-one-dimensional, frustrated spin-1/2 Heisenberg antiferromagnets. Physical Review B, 64:094425, 2001. [ bib | arXiv ]
[5] M. Bocquet and Th. Jolicoeur. Generalized nonlinear sigma model approach to alternating spin chains and ladders. European Physical Journal B, 14:47--52, 2000. [ bib | arXiv ]
[4] M. Bocquet, D. Serban, and M.R. Zirnbauer. Disordered 2d quasiparticles in class D: Dirac Fermions with random mass, and dirty superconductors. nucB, 578 [FS]:628--680, 2000. [ bib | arXiv ]
[3] V. Brunel, M. Bocquet, and Th. Jolicoeur. Edge Logarithm Corrections Probed by Impurity NMR. Physical Review Letters, 83:2821--2824, 1999. [ bib | arXiv ]
[2] M. Bocquet. Some spectral properties of the one-dimensional disordered Dirac equation. nucB, 546 [FS]:621--646, 1999. [ bib | arXiv ]
[1] M. Bocquet, S. Bonazzola, É. Gourgoulhon, and J. Novak. Rotating neutron star models with magnetic field. Astron. Astrophys., 301:757, 1995. [ bib | arXiv ]

Unrefereed publications

[10] T. Shibata, T. Nakajima, Y. Igarashi, H. Tsuruta, M. Ebihara, T. Hattori, M. Hoshi, T. Ishimaru, K. Masumoto, P. Bailly du Bois, M. Bocquet, D. Boust, I. Brovchenko, I. Choe, T. Christoudias, D. Didier, H. Dietze, P. Garreau, H. Higashi, K. T. Jung, S. Kida, P. Le Sager, J Lelieveld, V. Maderich, Y. Miyazawa, S. U. Park, D. Quélo, K. Saito, T. Shimbori, Y. Uchiyama, P. van Velthoven, V. Winiarek, and S. Yoshida. A review of the model comparison of transportation and deposition of radioactive materials released to the environment as a result of the Tokyo Electric Power Company's Fukushima Daiichi Nuclear Power Plant accident. Technical report, Sectional Committee on Nuclear Accident Committee on Comprehensive Synthetic Engineering, Science Council of Japan, September 2014. [ bib | .pdf ]
[9] M. Bocquet. La prévision numérique du temps. Technologie, 192:48--51, May-June 2014. [ bib ]
[8] V. Winiarek and M. Bocquet. Fukushima : de la radioactivité dans l'air, 2014. [ bib | http ]
[7] M. Bocquet and M. R. Koohkan. Quand modèles numériques et mesures ne sont pas sur la même longueur d’onde, 2014. [ bib | http ]
[6] M. Bocquet. Modélisation numérique de la dispersion atmosphérique accidentelle des radionucléides : l'état de l'art de la recherche. Revue du centre de défense NBC, 3:47--49, 2013. [ bib | .pdf ]
[5] M. Gray, C. Petit, S. Rodionov, L. Bertino, M. Bocquet, and T. Fusco. Local ensemble transform Kalman filter: a non-stationary control law for complex adaptive optics systems on ELTs. In Proceedings of the AO4ELT3 conference, 2013. [ bib | DOI | arXiv ]
[4] M. Bocquet. Modélisation inverse et assimilation de données non-gaussiennes pour les traceurs atmosphériques. Application à ETEX, Algésiras et Tchernobyl. Habilitation à diriger des recherches en science de l'univers, Université Pierre et Marie Curie, December 2007. 180 pages. [ bib | .pdf ]
[3] M. Krysta, M. Bocquet, and D. Quélo. Source reconstruction for accidental releases of radionuclides. In A. Ebel and T. Davitashvili, editors, Air, Water and Soil Quality Modelling for Risk and Impact Assessment, NATO Security Through Science Series, pages 153--161. Springer Netherlands, 2007. [ bib | DOI ]
[2] M. Krysta, M. Bocquet, O. Isnard, J.-P. Issartel, and B. Sportisse. Data assimilation of radionuclides at short and regional scales. In Advanced Research Workshop on Advances in Air Pollution Modeling for Environmental security, volume 54 of NATO Science Series, pages 275--285. Springer Netherlands, 2005. Proceedings of the NATO Advanced Research Workshop on Advances in Air Pollution Modeling for Environmental Security Borovetz, Bulgaria 8–12 May 2004. [ bib | DOI ]
[1] M. Bocquet. Chaînes de Spins, Fermions de Dirac, et Systèmes Désordonnés. PhD thesis, École Polytechnique, January 2000. 214 pages. [ bib | http ]