The Application of CCA for the Assessment and Comparison of the Capability of SOI and Nion’s SST for the Prediction of Winter Precipitation over the Caspian Sea Coasts

• Main Authors: S. M. J. Nazemosadat, A. Shirvani
• Format: Article
• Language: fas
• Published: Isfahan University of Technology 2004-04-01
• Series: Tulīd va Farāvarī-i Maḥṣūlāt-i Zirā̒ī va Bāghī
• Subjects: cca; precipitation; iran; caspian sea; prediction; winter; enso; eof; nino; sst; soi.
• Online Access: http://jcpp.iut.ac.ir/article-1-401-en.html

In Iran, about 75% of national rice production is supplied in Gilan and Mazandaran proviences which have the highest amount of precipitation. Seasonal prediction of rainfall induces significant improvement on yield production and on preventing climate hazardz over these feritle areas. Canonical correlation analysis (CCA) model was carried out evaluates the possibility of the prediction of winter rainfall according to the states of ENSO events. The time series of (southern oscilation index (SOI) and SST (sea surface temperature) over Nino's area (Nino's SST) are used as the predictors, and precipitation in Bandar Anzali and Noushahr are used as the predictands. Emperical orthogonal functions (EOF) were applied for reducing the number of original predictors variables to fewer presumably essential orthogonal variables. Four modes of variations (EOF1, EOF2, EOF3, EOF4) which account for about 92% of total variance in predictors field were retained and the others were considered as noise. Based on the retained EOFs and precipitation time series, the canonical correlation analysis (CCA) was carried out to predict winter precipitation in Noushahr and Bandar Anzali. The results indicated that the predictors considered account for about 45% of total variance in the rainfall time series. The correlation coefficents between the simulated and observed time series were significant at 5% significant level. For 70% of events the anomalies of observed and simulated values have the same sign indicating the ability of the model for reasonable prediction of above or below normal values of precipitation. For rainfall prediction, the role of Nino's SST (Nino4 in particular) was found to be around 10% more influential than SOI. .