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Use of medium‐range ensembles at the Met Office 2: Applications for medium‐range forecasting
Author(s) -
Young M V,
Carroll E B
Publication year - 2002
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1017/s135048270200302x
Subject(s) - probabilistic logic , computer science , variety (cybernetics) , range (aeronautics) , operations research , probabilistic forecasting , production (economics) , artificial intelligence , mathematics , materials science , economics , composite material , macroeconomics
The term ‘medium range’ is taken to refer to forecasts for lead times ranging from about 2 or 3 days ahead up to about 10 days ahead. A wide variety of numerical model products are available to the forecaster nowadays, and one of the most important of these is the ECMWF Ensemble Prediction System (EPS). This paper shows how forecasters at the Met Office use these products, in particular the EPS, in an operational environment in the production of medium‐range forecasts for a variety of customers, and illustrates some of the techniques involved. Particular reference is made to the PREVIN post‐processing system for the EPS which is described in the companion paper by Legg et al. (2002). Forecast products illustrated take the form of synoptic charts (produced primarily via Field Modification software), text guidance and other graphical formats. The probabilistic approach to forecasting is discussed with reference to various examples, in particular the application of the EPS in providing early warnings of severe weather for which risk assessment is increasingly important. A central theme of this paper is the vital role played by forecasters in interpreting the output from the models in terms of the likely weather elements, and using the EPS to help assess confidence levels for a particular forecast as well as possible alternative synoptic evolutions. Verification statistics are presented which demonstrate how the EPS helps the forecaster to add value to the wide range of individual deterministic model products and that furthermore, the forecaster can improve upon many probabilistic products derived directly from the ensemble. Copyright © 2002 Royal Meteorological Society.

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