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PRET, the Probability of RETurn: a new probabilistic product based on generalized extreme‐value theory
Author(s) -
Prates Fernando,
Buizza Roberto
Publication year - 2011
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.759
Subject(s) - probabilistic logic , extreme value theory , return period , generalized extreme value distribution , product (mathematics) , probability distribution , forecast verification , event (particle physics) , statistics , meteorology , environmental science , climatology , probabilistic forecasting , econometrics , forecast skill , mathematics , geography , physics , geology , geometry , archaeology , quantum mechanics , flood myth
A new probabilistic forecast product, the Probability of RETurn (PRET), is introduced. PRET, the probability of occurrence of an event that corresponds to a specific return period, is computed from forecasts given by the ECMWF Ensemble Prediction System. It has been designed to provide easy‐to‐interpret and valuable information on the intensity and rarity of the expected severe weather, especially when the ensemble‐based forecast distribution falls outside the model climate distribution. PRET definition relies on the Generalized Extreme Value family of distributions, which has been applied to study the statistics of the extremes in the model forecasts and observed datasets, and to estimate the levels corresponding to return periods not included in the datasets. PRET forecasts for the 2‐metre maximum and minimum temperatures over Europe have been generated for six summer and six winter seasons (2003 to 2009). Case‐studies have been used to illustrate that the new product is easier to interpret than products that are now commonly used, such as probability forecasts and maps of Extreme Forecast Indices. Average diagnostics of PRET forecasts indicate that the skill in predicting extremely hot temperatures in the warm season is higher than the skill in predicting extremely cold temperatures in the cold season. Copyright © 2011 Royal Meteorological Society