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PEARP, the Météo‐France short‐range ensemble prediction system
Author(s) -
Descamps L.,
Labadie C.,
Joly A.,
Bazile E.,
Arbogast P.,
Cébron P.
Publication year - 2014
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.2469
Subject(s) - north american mesoscale model , data assimilation , predictability , ensemble forecasting , mesoscale meteorology , forecast skill , parametrization (atmospheric modeling) , meteorology , range (aeronautics) , numerical weather prediction , probabilistic logic , computer science , global forecast system , environmental science , mathematics , statistics , artificial intelligence , geography , physics , materials science , quantum mechanics , radiative transfer , composite material
Météo‐France has implemented a short‐range ensemble prediction system known as Prévision d'Ensemble ARPEGE (PEARP). This system is a global ensemble performing forecasts up to 4.5 days. It uses the operational global numerical weather prediction model Action de Recherche Petite Echelle Grande Echelle (ARPEGE) and benefits from variable horizontal resolution, so that it is comparable to some limited‐area mesoscale systems over France. Perturbations to the initial conditions are computed by combining an ensemble data assimilation system with singular vectors. Model uncertainties are represented through a ‘multiphysics’ approach with ten different physical parametrization sets. The article describes the set‐up of the system and provides an assessment of the approaches used to represent initial conditions and model uncertainties. The positive impact of the variable horizontal resolution of PEARP is also illustrated. As a global ensemble forecast system (EFS), PEARP is also used to forecast cyclone tracks. It is shown that it has correctly predicted the landfall of hurricane Sandy . The performance of PEARP as run operationally with these features in 2014 is assessed objectively and compared with that of four operational global EFSs using classical probabilistic scores. This comparison is based on The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data. This is one of the first evaluations of EFSs for short‐range forecasts. The reliability and global skill of the five EFSs are evaluated over a three‐month period with scores computed against observations. PEARP shows skill comparable to or better than the other EFSs.