World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
46
Citations
10333
World Ranking
5076
National Ranking
267

Craig H. Bishop 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 Craig H. Bishop 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: 115 publications — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Craig H. Bishop 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 Craig H. Bishop 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: 46 D-Index — 49th percentile

49% of scientists in this discipline score the same or lower.

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

Overview

Craig H. Bishop is affiliated with the University of Melbourne in Australia. Their research primarily focuses on Earth and Planetary Sciences alongside Environmental Science, with a significant emphasis on the subfields of Global and Planetary Change and Atmospheric Science. Additional work touches on Oceanography, Electrical and Electronic Engineering, and Earth-Surface Processes.

The scientist has contributed to the study of climate variability and models as well as meteorological phenomena and simulations. Other topics covered in their research include atmospheric and environmental gas dynamics, cryospheric studies and observations, oceanographic and atmospheric processes, tropical and extratropical cyclones research, and arctic and antarctic ice dynamics.

Bishop's recent scientific publications encompass:

  • The Navy's Earth System Prediction Capability: A New Global Coupled Atmosphere-Ocean-Sea Ice Prediction System Designed for Daily to Subseasonal Forecasting (2020) in Earth and Space Science
  • A Multiscale Local Gain Form Ensemble Transform Kalman Filter (MLGETKF) (2020) in Monthly Weather Review
  • Using Machine Learning to Cut the Cost of Dynamical Downscaling (2023) in Earth s Future
  • Using Analysis Corrections to Address Model Error in Atmospheric Forecasts (2020) in Monthly Weather Review
  • Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation (2023) in Environmental Research Letters

Frequent coauthors collaborating with Bishop include Yawen Shao, Neil P Barton, Carolyn A. Reynolds, Sergey Frolov, and Sanaa Hobeichi. These collaborations have contributed to advancing knowledge in their shared areas of expertise.

The scientist regularly publishes in several key scientific journals, including:

  • Monthly Weather Review (4 publications)
  • Quarterly Journal of the Royal Meteorological Society (2 publications)
  • Journal of Advances in Modeling Earth Systems (2 publications)
  • Earth and Space Science (1 publication)
  • Earth s Future (1 publication)

Best Publications

  • Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects

    Craig H. Bishop;Brian J. Etherton;Sharanya J. Majumdar

  • Ensemble Square Root Filters

    Michael K. Tippett;Jeffrey L. Anderson;Craig H. Bishop;Thomas M. Hamill

  • A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes

    Xuguang Wang;Craig H. Bishop

  • The THORPEX Interactive Grand Global Ensemble

    Philippe Bougeault;Zoltan Toth;Craig Bishop;Barbara Brown

  • Cloud-Resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter

    Fuqing Zhang;Yonghui Weng;Jason A. Sippel;Zhiyong Meng

  • Ensemble Transformation and Adaptive Observations

    Craig H. Bishop;Zoltan Toth

  • Which Is Better, an Ensemble of Positive–Negative Pairs or a Centered Spherical Simplex Ensemble?

    Xuguang Wang;Craig H. Bishop;Simon J. Julier

  • The North Pacific Experiment (NORPEX-98): Targeted Observations for Improved North American Weather Forecasts

    Rolf H. Langland;Z. Toth;R. Gelaro;I. Szunyogh

  • Climate model dependence and the replicate Earth paradigm

    Craig H. Bishop;Gab Abramowitz

  • The Effect of Targeted Dropsonde Observations during the 1999 Winter Storm Reconnaissance Program

    I. Szunyogh;Z. Toth;R. E. Morss;S. J. Majumdar

  • Adaptive Sampling with the Ensemble Transform Kalman Filter. Part II: Field Program Implementation

    S. J. Majumdar;C. H. Bishop;B. J. Etherton;Z. Toth

  • Comparison of Hybrid Ensemble/4DVar and 4DVar within the NAVDAS-AR Data Assimilation Framework

    David D. Kuhl;Thomas E. Rosmond;Craig H. Bishop;Justin McLay

  • A Comparison of Hybrid Ensemble Transform Kalman Filter–Optimum Interpolation and Ensemble Square Root Filter Analysis Schemes

    Xuguang Wang;Thomas M. Hamill;Jeffrey S. Whitaker;Craig H. Bishop

  • Eady Edge Waves and Rapid Development

    H. C. Davies;C. H. Bishop

  • Improvement of ensemble reliability with a new dressing kernel

    Xuguang Wang;Craig H. Bishop

  • Flow‐adaptive moderation of spurious ensemble correlations and its use in ensemble‐based data assimilation

    Craig H. Bishop;Daniel Hodyss

  • Ensemble covariances adaptively localized with ECO-RAP. Part 1: tests on simple error models

    Craig H. Bishop;Daniel Hodyss

  • Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP

    Mozheng Wei;Zoltan Toth;Richard Wobus;Yuejian Zhu

  • Resilience of Hybrid Ensemble/3DVAR Analysis Schemes to Model Error and Ensemble Covariance Error

    Brian J. Etherton;Craig H. Bishop

  • Potential vorticity and the electrostatics analogy: Quasi‐geostrophic theory

    Craig H. Bishop;Alan J. Thorpe

Frequent Co-Authors

Alan J. Thorpe
Alan J. Thorpe University of Reading
Roberto Buizza
Roberto Buizza Sant'Anna School of Advanced Studies
Karl W. Hoppel
Karl W. Hoppel United States Naval Research Laboratory
Michael K. Tippett
Michael K. Tippett Columbia University
Brian J. Hoskins
Brian J. Hoskins University of Reading
John Methven
John Methven University of Reading
Eugenia Kalnay
Eugenia Kalnay University of Maryland, College Park
James D. Doyle
James D. Doyle United States Naval Research Laboratory
Sim D. Aberson
Sim D. Aberson National Oceanic and Atmospheric Administration
Fuqing Zhang
Fuqing Zhang Pennsylvania State University

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