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Potential use of an ensemble of analyses in the ECMWF Ensemble Prediction System
Author(s) -
Buizza Roberto,
Leutbecher Martin,
Isaksen Lars
Publication year - 2008
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.346
Subject(s) - ensemble forecasting , data assimilation , ensemble learning , probabilistic logic , computer science , ensemble average , statistical ensemble , statistics , meteorology , algorithm , statistical physics , environmental science , mathematics , artificial intelligence , canonical ensemble , climatology , geology , physics , monte carlo method
One of the crucial aspects of the design of an ensemble prediction system is the definition of the ensemble of initial states. This work investigates the use of singular vectors, an ensemble of analyses, and a combination of the two types of perturbations in the ECMWF operational ensemble prediction system. First, the similarity between perturbations generated using initial‐time singular vectors (SVs) and analyses from the ensemble data assimilation (EDA) system is assessed. Results show that the EDA perturbations are less localized geographically and have a better coverage of the Tropics. EDA perturbations have also smaller scales than SV‐based perturbations, and have a less evident upshear vertical tilt, which explains why they grow less with forecast time. Then, the use of EDA‐based perturbations in the ECMWF ensemble prediction system is studied. Results indicate that if used alone, EDA‐based perturbations lead to an under‐dispersive and less skilful ensemble then the one based on initial‐time SVs only. Combining the EDA and the initial‐time SVs gives a system with a better agreement between ensemble spread and the error of the ensemble mean, a smaller ensemble‐mean error and more skilful probabilistic forecasts than the current operational system based on initial‐time and evolved SVs. Copyright © 2008 Royal Meteorological Society