Preprints
https://doi.org/10.5194/gmd-2020-292
https://doi.org/10.5194/gmd-2020-292

Submitted as: development and technical paper 16 Nov 2020

Submitted as: development and technical paper | 16 Nov 2020

Review status: this preprint is currently under review for the journal GMD.

Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0

Pirkka Ollinaho1, Glenn D. Carver2, Simon T. K. Lang2, Lauri Tuppi3, Madeleine Ekblom3, and Heikki Järvinen3 Pirkka Ollinaho et al.
  • 1Finnish Meteorological Institute (FMI), Helsinki, Finland
  • 2European Centre for Medium-Range Weather Forecasts (ECMWF), Research Department, Reading, UK
  • 3Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Finland

Abstract. Ensemble prediction is an indispensable tool of modern numerical weather prediction (NWP). Due to its complex data flow, global medium-range ensemble prediction has so far remained exclusively as a duty of operational weather agencies. It has been very hard for academia therefore to be able to contribute to this important branch of NWP research using realistic weather models. In order to open up the ensemble prediction research for a wider research community, we have recreated all 50+1 operational IFS ensemble initial states for OpenIFS CY43R3. The dataset (OpenEnsemble 1.0) is available for use under a Creative Commons license and is downloadable from an https-server. The dataset covers one year (December 2016 to November 2017) twice daily. Downloads in three model resolutions (TL159, TL399 and TL639) are available to cover different research needs. An open-source workflow manager, called OpenEPS, is presented here and used to launch ensemble forecast experiments from the perturbed initial conditions. The deterministic and probabilistic forecast skill of OpenIFS (cycle 40R1) using this new set of initial states is comprehensively evaluated. In addition, we present a case study of typhoon Damrey from year 2017 to illustrate the new potential of being able to run ensemble forecasts outside major global weather forecasting centres.

Pirkka Ollinaho et al.

 
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Pirkka Ollinaho et al.

Model code and software

OpenEPS Pirkka Ollinaho https://doi.org/10.5281/zenodo.3759127

Executable research compendia (ERC)

OpenEPS post-processing Pirkka Ollinaho https://doi.org/10.5281/zenodo.4001495

OpenEPS skill score and plotting Python scripts Pirkka Ollinaho https://doi.org/10.5281/zenodo.4001516

Pirkka Ollinaho et al.

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Short summary
OpenEnsemble 1.0 is a novel dataset that aims to open up ensemble or probabilistic weather forecasting research for the academic community. The dataset contains atmospheric states that are required for running model forecasts of the atmospheric evolution. Our capacity to observe the atmosphere is limited, thus a single reconstruction of the atmospheric state contains some errors. Our dataset provides sets of 50 slightly different atmospheric states so that these errors can be taken into account.