IMPRINT

Improvement of sub-seasonal probabilistic forecasts of European high-impact weather events using machine learning techniques (IMPRINT)

Each year Europe is subject to drought, heatwaves and periods of persistent rainfall that could lead to the flooding of rivers. Although short-range weather forecasts have improved substantially over the last decennia, long-range weather forecasts have improved less. The goal of this project is to improve these long-term probabilistic forecasts of extreme weather. Warnings can then be given earlier and more reliably. Long meteorological datasets and newly developed statistical post-processing and machine learning methods enable us to better integrate the relevant information, and correct shortcomings of operational ensemble prediction systems.




Methods

Statistical post-processing, machine learning techniques, numerical weather prediction

Contributors

Collaborators

  • Maurice Schmeits (KNMI)
  • Kirien Whan (KNMI)

Papers

  • van Straaten, C., Whan, K., Coumou, D., van den Hurk, B., Schmeits, M. (2020) The influence of aggregation and statistical post-processing on the sub-seasonal predictability of European temperatures. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.5194/wcd-2020-40

Relevant links

 




post info

Get in touch with us



Dr. Dim Coumou

Department of Water & Climate Risk
Institute for Environmental Studies (IVM)
VU Amsterdam
W&N-building, Room C-515
De Boelelaan 1087
1081 HV Amsterdam

Department of Earth System Analysis
Potsdam Institute for Climate Impact Research
Telegraphenberg A62, room S16
D-14473, Potsdam, Germany

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