EGU2020-18197
https://doi.org/10.5194/egusphere-egu2020-18197
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Ensemble prediction with OpenIFS

Pirkka Ollinaho
Pirkka Ollinaho
  • Finnish Meteorological Institute, Helsinki, Finland (pirkka.ollinaho@fmi.fi)
Probabilistic forecasts provide information on how predictions of the atmospheric evolution may differ from the best guess solution provided by a deterministic forecast. Ensemble prediction systems generate this information through assessing uncertainties in both the model initial state and the model itself. In order to open up ensemble prediction research for a wider research community, we have recreated all 50+1 operational ECMWF ensemble initial states for OpenIFS. The data set covers one year (December 2016 to November 2017) twice a day. A range of model resolutions are provided to cover different research needs (TL159, TL399 and TL639). The probabilistic skill of OpenIFS ensembles using these initial states is showcased. A case study of typhoon Damrey, which severely affected Vietnam in 2017, will also be presented.

How to cite: Ollinaho, P.: Ensemble prediction with OpenIFS, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18197, https://doi.org/10.5194/egusphere-egu2020-18197, 2020

This abstract will not be presented.