Statistical Post-processing

Statistical Post-Processing is a methodological framework within medium-range forecasting that corrects and refines raw numerical weather predictions through statistical techniques to reduce systematic biases and improve forecast accuracy.

295

Publications

18.1K

Citations

850

Authors

393

Institutions

Publications per year

2017–2026

112

Authors

850

  1. SL
    Sebastian Lerch

    Karlsruhe Institute of Technology, Germany

    9 publications · 877 citations

    9

    h-index

  2. JW
    Jakob W. Messner

    Universität Innsbruck, Austria

    5 publications · 218 citations

    5

    h-index

  3. OM
    Olivier Mestre

    Météo-France, France

    5 publications · 143 citations

    5

    h-index

  4. QJ
    Quan J. Wang

    CSIRO Land and Water, Australia

    5 publications · 286 citations

    5

    h-index

  5. SV
    Stéphane Vannitsem

    Royal Meteorological Institute of Belgium, Belgium

    5 publications · 164 citations

    5

    h-index

1–5 of 850

Venues

  1. Monthly Weather Review

    22 publications · 1.4K citations

    18

    h-index

  2. Hydrology and earth system sciences

    10 publications · 1.6K citations

    10

    h-index

  3. Weather and Forecasting

    9 publications · 475 citations

    9

    h-index

  4. Nonlinear processes in geophysics

    7 publications · 206 citations

    7

    h-index

  5. 7

    h-index

1–5