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Perturbation of initial and boundary conditions for a limited‐area ensemble: multi‐model versus single‐model approach
Author(s) -
Marsigli C.,
Montani A.,
Paccagnella T.
Publication year - 2013
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.2128
Subject(s) - downscaling , ensemble forecasting , ensemble average , computer science , range (aeronautics) , perturbation (astronomy) , scale (ratio) , meteorology , artificial intelligence , climatology , physics , geology , quantum mechanics , precipitation , materials science , composite material
With the aim of developing of an ensemble forecasting system for the short‐range, the validity of a multi‐analysis multi‐model approach for initial‐ and boundary‐condition perturbations has been explored by the Consortium for Small‐scale Modelling (COSMO). This approach has been adopted in the COSMO Short‐Range Ensemble Prediction System (COSMO–SREPS), in which members are driven by a few operational deterministic runs. In this work, we show the extent to which this approach is beneficial with respect to the downscaling of a single‐model global ensemble, adopted by the operational COSMO Limited‐Area Ensemble Prediction System (COSMO–LEPS). Both COSMO–SREPS and COSMO–LEPS are made up of 16 integrations of the COSMO model with 7 km horizontal mesh size. Results based on the 2010–2011 winter season show that the 16 member COSMO–LEPS outperforms the 16 member COSMO–SREPS, which has boundaries perturbed by three global model runs only, but the reverse is true when the three‐member versions of the two systems are compared. Hence, if only a small‐size Limited‐Area Model (LAM) ensemble is feasible, then the multimodel approach for boundaries gives better results. The dependence of the scores on ensemble size is also studied, showing that all the quality measures tend to saturate before the full size is reached. Finally, the impact of combining the two ensembles is assessed, showing that a positive impact results from adding multi‐model‐driven members to COSMO–LEPS.