Variational Data Assimilation

Variational Data Assimilation is a data assimilation method that belongs to the class of optimization-based techniques, distinguished by its formulation of the state estimation problem as a variational problem where an objective functional, measuring the discrepancy between observations and model predictions weighted by their uncertainties, is minimized to obtain the optimal initial conditions for a numerical model.

1.2K

Publications

80.3K

Citations

2.5K

Authors

725

Institutions

Publications per year

2017–2026

218

Authors

2.5K

  1. PC
    Philippe Courtier

    European Centre for Medium-Range Weather Forecasts, United Kingdom

    26 publications · 5.4K citations

    22

    h-index

  2. IM
    I. M. Navon

    Florida State University, United States

    24 publications · 1.4K citations

    21

    h-index

  3. JS
    Juanzhen Sun

    NSF National Center for Atmospheric Research, United States

    21 publications · 2K citations

    19

    h-index

  4. NN
    Nancy Nichols

    University of Reading, United Kingdom

    23 publications · 1.2K citations

    18

    h-index

  5. MX
    Ming Xue

    University of Oklahoma, United States

    16 publications · 956 citations

    16

    h-index

1–5 of 2.5K

Venues

  1. Monthly Weather Review

    220 publications · 17K citations

    72

    h-index

  2. 65

    h-index

  3. Tellus A Dynamic Meteorology and Oceanography

    77 publications · 5.8K citations

    37

    h-index

  4. Journal of Geophysical Research Atmospheres

    JGR · 60 publications · 4.5K citations

    34

    h-index

  5. Weather and Forecasting

    28 publications · 2K citations

    24

    h-index

1–5