World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
52
Citations
9641
World Ranking
3652
National Ranking
64

Marc Bocquet publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Marc Bocquet sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 180 publications — 40th percentile

40% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Marc Bocquet D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Marc Bocquet sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 52 D-Index — 64th percentile

64% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Marc Bocquet is affiliated with the École des Ponts ParisTech in France. Their research spans multiple fields of study, with a focus on Earth and Planetary Sciences and Environmental Science. Within these broad areas, they concentrate on subfields including Atmospheric Science, Global and Planetary Change, Electrical and Electronic Engineering, Environmental Engineering, and Artificial Intelligence.

The main topics in their work include Meteorological Phenomena and Simulations, Climate Variability and Models, Atmospheric and Environmental Gas Dynamics, Model Reduction and Neural Networks, Arctic and Antarctic Ice Dynamics, Advanced Memory and Neural Computing, and Wind and Air Flow Studies.

Marc Bocquet's recent publications reflect these research interests. Notable papers include:

  • "Pushing the frontiers in climate modelling and analysis with machine learning," 2024, published in Nature Climate Change
  • "Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review," 2023, published in IEEE/CAA Journal of Automatica Sinica
  • "Combining data assimilation and machine learning to infer unresolved scale parametrization," 2021, published in Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
  • "Using machine learning to correct model error in data assimilation and forecast applications," 2021, published in Quarterly Journal of the Royal Meteorological Society
  • "A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation," 2020, published in Monthly Weather Review

The venues where Marc Bocquet frequently publishes demonstrate their engagement with leading journals and repositories in their fields. These include arXiv (Cornell University), Geoscientific Model Development, Quarterly Journal of the Royal Meteorological Society, The Cryosphere, and Journal of Advances in Modeling Earth Systems.

Collaborative work is a significant part of their career, with frequent co-authors including Alban Farchi, Alberto Carrassi, Tobias Sebastian Finn, Joffrey Dumont Le Brazidec, and Julien Brajard.

Best Publications

  • Data assimilation in the geosciences: An overview of methods, issues, and perspectives

    Alberto Carrassi;Marc Bocquet;Laurent Bertino;Geir Evensen

  • Real-time air quality forecasting, part I: History, techniques, and current status

    Yang Zhang;Yang Zhang;Marc Bocquet;Marc Bocquet;Vivien Mallet;Vivien Mallet;Christian Seigneur

  • On the representation error in data assimilation

    T. Janjic;N. Bormann;M. Bocquet;J. A. Carton

  • Data Assimilation: Methods, Algorithms, and Applications

    Mark Asch;Marc Bocquet;Maëlle Nodet

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

    M. Bocquet;M. Bocquet;H. Elbern;H. Eskes;M. Hirtl

  • Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation

    Marc Bocquet;Carlos A. Pires;Lin Wu

  • 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;Laurent Bertino

  • Rotating neutron star models with a magnetic field.

    M. Bocquet;S. Bonazzola;E. Gourgoulhon;J. Novak

  • Real-time air quality forecasting, part II: State of the science, current research needs, and future prospects

    Yang Zhang;Marc Bocquet;Vivien Mallet;Christian Seigneur

  • An iterative ensemble Kalman smoother

    Marc Bocquet;Marc Bocquet;Pavel Sakov

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

    Olivier Saunier;Anne Mathieu;Damien Didier;Marilyne Tombette

  • Combining data assimilation and machine learning to infer unresolved scale parametrization

    Julien Brajard;Alberto Carrassi;Marc Bocquet;Laurent Bertino

  • 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

    Victor Winiarek;Victor Winiarek;Marc Bocquet;Marc Bocquet;Olivier Saunier;Anne Mathieu

  • Disordered 2d quasiparticles in class D: Dirac fermions with random mass, and dirty superconductors

    M. Bocquet;D. Serban;M.R. Zirnbauer

  • Using machine learning to correct model error in data assimilation and forecast applications

    Alban Farchi;Patrick Laloyaux;Massimo Bonavita;Marc Bocquet

  • What eddy-covariance measurements tell us about prior land flux errors in CO2-flux inversion schemes

    Frédéric Chevallier;Tao Wang;Philippe Ciais;Fabienne Maignan

  • Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization

    Marc Bocquet;Julien Brajard;Alberto Carrassi;Laurent Bertino

  • Finite-temperature dynamical magnetic susceptibility of quasi-one-dimensional frustrated spin- 1 2 Heisenberg antiferromagnets

    Marc Bocquet;Fabian H. L. Essler;Alexei M. Tsvelik;Alexander O. Gogolin

  • A review of innovation-based methods to jointly estimate model and observation error covariance matrices in ensemble data assimilation

    Pierre Tandeo;Pierre Ailliot;Marc Bocquet;Alberto Carrassi;Alberto Carrassi

  • A Comparison Study of Data Assimilation Algorithms for Ozone Forecasts

    Lin Wu;Lin Wu;Vivien Mallet;Vivien Mallet;Marc Bocquet;Marc Bocquet;Bruno Sportisse;Bruno Sportisse

  • 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

    Victor Winiarek;Marc Bocquet;Nora Duhanyan;Yelva Roustan

  • 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: ESTIMATION OF ERRORS IN INVERSE MODELING

    Victor Winiarek;Marc Bocquet;Olivier Saunier;Anne Mathieu

  • Data Assimilation in the Geosciences - An overview on methods, issues and perspectives.

    Alberto Carrassi;Marc Bocquet;Laurent Bertino;Geir Evensen

Frequent Co-Authors

Laurent Bertino
Laurent Bertino Bjerknes Centre for Climate Research
Michael Ghil
Michael Ghil École Normale Supérieure
Thomas Lauvaux
Thomas Lauvaux Pennsylvania State University
F. Chevallier
F. Chevallier University of Paris-Saclay
Patrick Chazette
Patrick Chazette French Alternative Energies and Atomic Energy Commission (CEA)
Kenneth J. Davis
Kenneth J. Davis Pennsylvania State University
Christopher K. R. T. Jones
Christopher K. R. T. Jones University of North Carolina at Chapel Hill
Geir Evensen
Geir Evensen NORCE Research
Mikhail Sofiev
Mikhail Sofiev Finnish Meteorological Institute
Alexander Baklanov
Alexander Baklanov University of Copenhagen

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