Physics – Geophysics
Scientific paper
May 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004georl..3109212k&link_type=abstract
Geophysical Research Letters, Volume 31, Issue 9, CiteID L09212
Physics
Geophysics
6
Oceanography: General: Ocean Prediction, Oceanography: General: Continental Shelf Processes, Meteorology And Atmospheric Dynamics: Ocean/Atmosphere Interactions (0312, 4504), Mathematical Geophysics: Modeling, Global Change: Climate Dynamics (3309)
Scientific paper
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.
Jhun Jong-Ghap
Kang In-Sik
Kug Jong-Seong
Lee June-Yi
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