Ensemble Postprocessing

Ensemble Postprocessing is a statistical methodology that refines raw ensemble model outputs by correcting systematic biases and quantifying forecast uncertainty to yield calibrated probabilistic forecasts.

158

Publications

12.6K

Citations

371

Authors

180

Institutions

Publications per year

2017–2026

71

Authors

371

  1. TG
    Tilmann Gneiting

    University of Washington, United States

    12 publications · 4.2K citations

    12

    h-index

  2. JW
    Jakob W. Messner

    Universität Innsbruck, Austria

    8 publications · 357 citations

    8

    h-index

  3. SL
    Sebastian Lerch

    Karlsruhe Institute of Technology, Germany

    8 publications · 325 citations

    8

    h-index

  4. GJ
    Georg J. Mayr

    Universität Innsbruck, Austria

    8 publications · 357 citations

    8

    h-index

  5. QJ
    Quan J. Wang

    CSIRO Land and Water, Australia

    8 publications · 700 citations

    8

    h-index

1–5 of 371

Venues

  1. Monthly Weather Review

    33 publications · 5.4K citations

    25

    h-index

  2. 18

    h-index

  3. Weather and Forecasting

    10 publications · 509 citations

    10

    h-index

  4. Water Resources Research

    8 publications · 398 citations

    8

    h-index

  5. Journal of Hydrometeorology

    6 publications · 170 citations

    6

    h-index

1–5