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

Autor: P. Ollinaho, G. D. Carver, S. T. K. Lang, L. Tuppi, M. Ekblom, H. Järvinen
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Geoscientific Model Development, Vol 14, Pp 2143-2160 (2021)
Druh dokumentu: article
ISSN: 1991-959X
1991-9603
DOI: 10.5194/gmd-14-2143-2021
Popis: 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 (OpenEnsemble 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 (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 of major global weather forecasting centres.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje