Ensemble Data Assimilation

Ensemble Data Assimilation is a data assimilation framework that belongs to the class of ensemble-based methods, distinguished by its use of an ensemble of model states to estimate flow-dependent background error covariances for optimally integrating observational data into numerical forecasts.

1.7K

Publications

122.7K

Citations

3.3K

Authors

939

Institutions

Publications per year

2017–2026

570

Authors

3.3K

  1. JG
    J. G. Anderson

    NSF National Center for Atmospheric Research, United States

    70 publications · 8.5K citations

    39

    h-index

  2. TM
    Takemasa Miyoshi

    University of Maryland, College Park, United States

    59 publications · 4K citations

    34

    h-index

  3. EK
    Eugenia Kalnay

    University of Maryland, College Park, United States

    52 publications · 4.5K citations

    33

    h-index

  4. FZ
    Fuqing Zhang

    Pennsylvania State University, United States

    46 publications · 4.7K citations

    33

    h-index

  5. CS
    Chris Snyder

    NSF National Center for Atmospheric Research, United States

    34 publications · 4.8K citations

    30

    h-index

1–5 of 3.3K

Venues

  1. Monthly Weather Review

    346 publications · 43.2K citations

    96

    h-index

  2. 52

    h-index

  3. Tellus A Dynamic Meteorology and Oceanography

    97 publications · 6.6K citations

    44

    h-index

  4. Water Resources Research

    53 publications · 4.1K citations

    36

    h-index

  5. Journal of Hydrology

    41 publications · 1.9K citations

    29

    h-index

1–5