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
Parents
295
Lukas Gudmundsson, John Bjørnar Bremnes, Jan Erik Haugen et al. · Hydrology and earth system sciences · 2012 · 1.2K citations · Full text
Economic and Social Commission for Asia and the Pacific
Un. Escap · 1986 · 1.1K citations
Neural Networks for Postprocessing Ensemble Weather Forecasts
Stephan Rasp, Sebastian Lerch · Monthly Weather Review · 2018 · 519 citations · Full text
Multi-document summarization by sentence extraction
Jade Goldstein, Vibhu O. Mittal, Jaime Carbonell et al. · 2000 · 357 citations · Full text
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393
Karlsruhe Institute of Technology
Karlsruhe, Germany
11 h-index
28 publications
2K citations

European Centre for Medium-Range Weather Forecasts
Reading, United Kingdom
10 h-index
24 publications
1.1K citations

Centre National de la Recherche Scientifique
Paris, France
10 h-index
26 publications
1.1K citations
Heidelberg Institute for Theoretical Studies
Heidelberg, Germany
10 h-index
12 publications
1.6K citations
Météo-France
Toulouse, France
9 h-index
20 publications
594 citations
Met Office
Exeter, United Kingdom
8 h-index
20 publications
1.1K citations

NSF National Center for Atmospheric Research
Boulder, United States
7 h-index
13 publications
1.5K citations
University of Colorado Boulder
Boulder, United States
7 h-index
16 publications
588 citations
1–8 of 393
22 publications · 1.4K citations
18
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10 publications · 1.6K citations
10
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9 publications · 475 citations
9
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7 publications · 206 citations
7
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8 publications · 273 citations
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