Joint state and parameter estimation with an iterative ensemble Kalman smoother

Bocquet, M.; Sakov, P.

doi:https://doi.org/10.5194/npg-20-803-2013

Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic

Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Guillaume Boutin, and Einar Ólason

The Cryosphere, 18, 1791–1815, https://doi.org/10.5194/tc-18-1791-2024,https://doi.org/10.5194/tc-18-1791-2024, 2024

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Deep learning applied to CO₂ power plant emissions quantification using simulated satellite images

Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet

Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024,https://doi.org/10.5194/gmd-17-1995-2024, 2024

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Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology

Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau

The Cryosphere, 17, 2965–2991, https://doi.org/10.5194/tc-17-2965-2023,https://doi.org/10.5194/tc-17-2965-2023, 2023

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Segmentation of XCO₂ images with deep learning: application to synthetic plumes from cities and power plants

Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux

Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023,https://doi.org/10.5194/gmd-16-3997-2023, 2023

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Accounting for meteorological biases in simulated plumes using smarter metrics

Pierre J. Vanderbecken, Joffrey Dumont Le Brazidec, Alban Farchi, Marc Bocquet, Yelva Roustan, Élise Potier, and Grégoire Broquet

Atmos. Meas. Tech., 16, 1745–1766, https://doi.org/10.5194/amt-16-1745-2023,https://doi.org/10.5194/amt-16-1745-2023, 2023

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Bayesian transdimensional inverse reconstruction of the Fukushima Daiichi caesium 137 release

Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan

Geosci. Model Dev., 16, 1039–1052, https://doi.org/10.5194/gmd-16-1039-2023,https://doi.org/10.5194/gmd-16-1039-2023, 2023

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A fast, single-iteration ensemble Kalman smoother for sequential data assimilation

Colin Grudzien and Marc Bocquet

Geosci. Model Dev., 15, 7641–7681, https://doi.org/10.5194/gmd-15-7641-2022,https://doi.org/10.5194/gmd-15-7641-2022, 2022

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Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 ¹⁰⁶Ru event

Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan

Atmos. Chem. Phys., 21, 13247–13267, https://doi.org/10.5194/acp-21-13247-2021,https://doi.org/10.5194/acp-21-13247-2021, 2021

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On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments

Colin Grudzien, Marc Bocquet, and Alberto Carrassi

Geosci. Model Dev., 13, 1903–1924, https://doi.org/10.5194/gmd-13-1903-2020,https://doi.org/10.5194/gmd-13-1903-2020, 2020

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Diagnosing spatial error structures in CO₂ mole fractions and XCO₂ column mole fractions from atmospheric transport

Thomas Lauvaux, Liza I. Díaz-Isaac, Marc Bocquet, and Nicolas Bousserez

Atmos. Chem. Phys., 19, 12007–12024, https://doi.org/10.5194/acp-19-12007-2019,https://doi.org/10.5194/acp-19-12007-2019, 2019

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Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models

Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino

Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019,https://doi.org/10.5194/npg-26-143-2019, 2019

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Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model

Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino

Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136,https://doi.org/10.5194/gmd-2019-136, 2019

Revised manuscript not accepted

Short summary

Calibration of a multi-physics ensemble for estimating the uncertainty of a greenhouse gas atmospheric transport model

Liza I. Díaz-Isaac, Thomas Lauvaux, Marc Bocquet, and Kenneth J. Davis

Atmos. Chem. Phys., 19, 5695–5718, https://doi.org/10.5194/acp-19-5695-2019,https://doi.org/10.5194/acp-19-5695-2019, 2019

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Review article: Comparison of local particle filters and new implementations

Alban Farchi and Marc Bocquet

Nonlin. Processes Geophys., 25, 765–807, https://doi.org/10.5194/npg-25-765-2018,https://doi.org/10.5194/npg-25-765-2018, 2018

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Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error

Colin Grudzien, Alberto Carrassi, and Marc Bocquet

Nonlin. Processes Geophys., 25, 633–648, https://doi.org/10.5194/npg-25-633-2018,https://doi.org/10.5194/npg-25-633-2018, 2018

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Parametric covariance dynamics for the nonlinear diffusive Burgers equation

Olivier Pannekoucke, Marc Bocquet, and Richard Ménard

Nonlin. Processes Geophys., 25, 481–495, https://doi.org/10.5194/npg-25-481-2018,https://doi.org/10.5194/npg-25-481-2018, 2018

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Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother

Anthony Fillion, Marc Bocquet, and Serge Gratton

Nonlin. Processes Geophys., 25, 315–334, https://doi.org/10.5194/npg-25-315-2018,https://doi.org/10.5194/npg-25-315-2018, 2018

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Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013

Jiping Xie, Laurent Bertino, François Counillon, Knut A. Lisæter, and Pavel Sakov

Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017,https://doi.org/10.5194/os-13-123-2017, 2017

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A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes: L95-GRS (v1.0)

J.-M. Haussaire and M. Bocquet

Geosci. Model Dev., 9, 393–412, https://doi.org/10.5194/gmd-9-393-2016,https://doi.org/10.5194/gmd-9-393-2016, 2016

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Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation

M. Bocquet, P. N. Raanes, and A. Hannart

Nonlin. Processes Geophys., 22, 645–662, https://doi.org/10.5194/npg-22-645-2015,https://doi.org/10.5194/npg-22-645-2015, 2015

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Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

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

Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015,https://doi.org/10.5194/acp-15-5325-2015, 2015

Short summary

Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin

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

Atmos. Chem. Phys., 14, 12031–12053, https://doi.org/10.5194/acp-14-12031-2014,https://doi.org/10.5194/acp-14-12031-2014, 2014

Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign

Y. Wang, K. N. Sartelet, M. Bocquet, and P. Chazette

Atmos. Chem. Phys., 14, 3511–3532, https://doi.org/10.5194/acp-14-3511-2014,https://doi.org/10.5194/acp-14-3511-2014, 2014

An inverse modeling method to assess the source term of the Fukushima Nuclear Power Plant accident using gamma dose rate observations

O. Saunier, A. Mathieu, D. Didier, M. Tombette, D. Quélo, V. Winiarek, and M. Bocquet

Atmos. Chem. Phys., 13, 11403–11421, https://doi.org/10.5194/acp-13-11403-2013,https://doi.org/10.5194/acp-13-11403-2013, 2013

Estimation of volatile organic compound emissions for Europe using data assimilation

M. R. Koohkan, M. Bocquet, Y. Roustan, Y. Kim, and C. Seigneur

Atmos. Chem. Phys., 13, 5887–5905, https://doi.org/10.5194/acp-13-5887-2013,https://doi.org/10.5194/acp-13-5887-2013, 2013

Assimilation of ground versus lidar observations for PM₁₀ forecasting

Y. Wang, K. N. Sartelet, M. Bocquet, and P. Chazette

Atmos. Chem. Phys., 13, 269–283, https://doi.org/10.5194/acp-13-269-2013,https://doi.org/10.5194/acp-13-269-2013, 2013

Joint state and parameter estimation with an iterative ensemble Kalman smoother

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