Ensemble Model Output Statistics

Ensemble Model Output Statistics is a statistical post-processing method that corrects biases and refines probabilistic forecasts by calibrating the ensemble of numerical weather prediction models through a regression-based relationship between historical ensemble outputs and corresponding observations.

153

Publications

11K

Citations

530

Authors

278

Institutions

Publications per year

2017–2026

55

Authors

530

  1. TG
    Tilmann Gneiting

    University of Washington, United States

    8 publications · 2.1K citations

    8

    h-index

  2. SL
    Sebastian Lerch

    Karlsruhe Institute of Technology, Germany

    7 publications · 393 citations

    7

    h-index

  3. WA
    Willem A. Landman

    University of Pretoria, South Africa

    6 publications · 221 citations

    6

    h-index

  4. TM
    Thomas M. Hamill

    NOAA Earth System Research Laboratory, United States

    5 publications · 1.2K citations

    5

    h-index

  5. MS
    Michael Scheuerer

    Cooperative Institute for Research in Environmental Sciences, United States

    4 publications · 230 citations

    4

    h-index

1–5 of 530

Venues

  1. Monthly Weather Review

    16 publications · 2.8K citations

    15

    h-index

  2. 13

    h-index

  3. Weather and Forecasting

    9 publications · 756 citations

    9

    h-index

  4. Geophysical Research Letters

    6 publications · 427 citations

    6

    h-index

  5. International Journal of Climatology

    6 publications · 206 citations

    6

    h-index

1–5