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

Pirkka Ollinaho, Glenn D. Carver, Simon T. K. Lang, Lauri Tuppi, Madeleine Ekblom, Heikki Järvinen

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review


Ensemble prediction is an indispensable tool in modern numerical weather prediction (NWP). Due to its complex data flow, global medium-range ensemble prediction has almost exclusively been carried out by operational weather agencies to date. Thus, it has been very hard for academia to contribute to this important branch of NWP research using realistic weather models. In order to open ensemble prediction research up to the wider research community, we have recreated all 50 + 1 operational IFS ensemble initial states for OpenIFS CY43R3. The dataset (Open Ensemble 1.0) is available for use under a Creative Commons licence and is downloadable from an https server. The dataset covers 1 year (December 2016 to November 2017) twice daily. Downloads in three model resolutions (T(L)159, T(L)399, and T(L)639) 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 of major global weather forecasting centres.

TidskriftGeoscientific Model Development
Sidor (från-till)2143–2160
Antal sidor18
StatusPublicerad - 23 apr. 2021
MoE-publikationstypA1 Tidskriftsartikel-refererad


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