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Skill improvement from increased ensemble size and model diversity
Author(s) -
DelSole Timothy,
Nattala Jyothi,
Tippett Michael K.
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 - 10.1002/2014gl060133
Subject(s) - ensemble average , diversity (politics) , ensemble forecasting , econometrics , oscillation (cell signaling) , forecast skill , ensemble learning , computer science , statistics , climatology , environmental science , mathematics , machine learning , geology , biology , sociology , anthropology , genetics
This paper proposes an objective procedure for deciding if the skill of a combination of forecasts is significantly larger than that of a single forecast, and for deciding if the observed improvement is dominated by reduction of noise associated with ensemble averaging, or by addition of new predictable signals. Information theory provides an attractive framework for addressing these questions. The procedure is applied to El Niño–Southern Oscillation hindcasts from the North American Multimodel Ensemble (NMME) and reveals that the observed skill advantage of the NMME compared to individual models is substantially greater than that expected from increased ensemble size alone and is more consistent with the addition of new signals.