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Trends in the predictive performance of raw ensemble weather forecasts
Author(s) -
Hemri S.,
Scheuerer M.,
Pappenberger F.,
Bogner K.,
Haiden T.
Publication year - 2014
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2014gl062472
Subject(s) - ensemble forecasting , quantitative precipitation forecast , forecast skill , precipitation , consensus forecast , raw data , range (aeronautics) , environmental science , ensemble average , computer science , wind speed , meteorology , reliability (semiconductor) , statistics , climatology , mathematics , artificial intelligence , geology , power (physics) , physics , materials science , quantum mechanics , composite material
This study applies statistical postprocessing to ensemble forecasts of near‐surface temperature, 24 h precipitation totals, and near‐surface wind speed from the global model of the European Centre for Medium‐Range Weather Forecasts (ECMWF). The main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the postprocessed forecasts. Reliability and sharpness, and hence skill, of the former is expected to improve over time. Thus, the gain by postprocessing is expected to decrease. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations, we generate postprocessed forecasts by ensemble model output statistics (EMOS) for each station and variable. Given the higher average skill of the postprocessed forecasts, we analyze the evolution of the difference in skill between raw ensemble and EMOS. This skill gap remains almost constant over time indicating that postprocessing will keep adding skill in the foreseeable future.