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Impact of model resolution and ensemble size on the performance of an Ensemble Prediction System
Author(s) -
Buizza R.,
Petroliagis T.,
Palmer T.,
Barkmeijer J.,
Hamrud M.,
Hollingsworth A.,
Simmons A.,
Wedi N.
Publication year - 1998
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.49712455008
Subject(s) - ensemble forecasting , ensemble learning , resolution (logic) , ensemble average , statistics , computer science , mathematics , machine learning , artificial intelligence , geology , climatology
Ensemble integrations for 14 cases are described. These integrations test the relative impact of increase in ensemble size and in the resolution of the model used to integrate the ensemble. The ensembles are evaluated using a variety of statistical tests. Some of these indicate a relative advantage of an increase in ensemble size, whilst most tests suggest a relative advantage of an increase in model resolution. However, overall, the best performance was obtained by combining enhancement in model resolution (from T63L19 to T106L31) with an increase in ensemble size (from 32 to 50 members).