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Ensemble canonical correlation prediction of precipitation over the Sahel
Author(s) -
Mo Kingtse C.,
Thiaw Wassila M.
Publication year - 2002
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 - 10.1029/2002gl015075
Subject(s) - canonical correlation , climatology , precipitation , sea surface temperature , forecast skill , ensemble forecasting , environmental science , ensemble average , correlation , mathematics , canonical ensemble , meteorology , statistics , geology , geography , geometry , monte carlo method
Ensemble canonical correlation (ECC) prediction method is applied to predict summer rainfall over the Sahel. The predictors are the global sea surface temperature (SST), 200‐hPa streamfunction with zonal means removed (PSI), and forecasts or simulations from the climate models. The canonical correlation analysis (CCA) is performed for each variable separately. These predicted precipitation fields form an ensemble. The ensemble mean is the equal weighted average of its members. For both the multi‐model simulations and seasonal hindcasts, the CCA correction improves forecast skill. The ensemble, which consists of the CCA precipitation forecasts based on SST, PSI and the CCA corrected model simulations gives the highest skill. Each member has forecast skill over the different parts of the Sahel. Therefore, the ensemble mean has higher skill than its individual members.