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A statistical approach to Indian Ocean sea surface temperature prediction using a dynamical ENSO prediction
Author(s) -
Kug JongSeong,
Kang InSik,
Lee JuneYi,
Jhun JongGhap
Publication year - 2004
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 - 10.1029/2003gl019209
Subject(s) - sea surface temperature , el niño southern oscillation , climatology , environmental science , forecast skill , indian ocean , linear regression , boreal , monsoon , statistical model , meteorology , oceanography , geology , mathematics , statistics , geography , paleontology
In this study, a statistical prediction model has been developed to forecast monthly Sea Surface Temperature (SST) in the Indian Ocean. It is a linear regression model based on a lagged relationship between the Indian Ocean SST and the NINO3 SST. A new approach to the statistical modeling has been tried out, in which the model predictors are obtained from not only observed NINO3 SST but also predicted results produced by a dynamical El Niño model. The forecast skill of the present model is better than that of persistence prediction. In particular, the present model has a significantly improved predictive skill during the spring and summer seasons when the boreal summer Indian monsoon is affected by the Indian Ocean SST.