PREDICTION OF WINTER RAINFALL OF SOUTHERN IRAN USING PERSIAN GULF SEA SURFACE TEMPERATURE: CANONICAL CORRELATION ANALYSIS APPLICATION

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NAZEM ALSADAT S.M.J.; SHIRVANI AMIN

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Journal: THE SCIENTIFIC JOURNAL OF AGRICULTURE (SJA) Year:2006 | Volume:29 | Issue:2 Page(s): 65-77

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Title

PREDICTION OF WINTER RAINFALL OF SOUTHERN IRAN USING PERSIAN GULF SEA SURFACE TEMPERATURE: CANONICAL CORRELATION ANALYSIS APPLICATION

Author(s)

NAZEM ALSADAT S.M.J. | SHIRVANI AMIN | Issue Writer Certificate

Keywords

PERSIAN GULFQ3

SSTQ2

CCAQ3

EOFQ3

RAINFALLQ3

PREDICTIONQ3

Abstract

Since drought and flood events have frequently hit various Iranian communities, PREDICTION of precipitation plays and influential role for sustainable development. The canonical correlation analysis (CCA) is an elegant statistical model for investigating temporal and spatial variations of meteorological, hydrological and ocean graph variables. This model is extensively used for the seasonal PREDICTION of RAINFALL and sea surface temperature (SST) over tropical regions. In this study, the monthly RAINFALL data in nine synoptic stations speared over different parts of southern Iran as well as the PERSIAN GULF (pG) sea surface temperature data for the period 1959-1993 were considered for developing CCA model for PREDICTION of precipitation in the south west of Iran. The empirical orthogonal function (EOF) was carried out for the purpose of data reduction and extracting the principal components of SST such that two principal components of the precipitation data and four principal components of SST were considered. The retained EOFs of the RAINFALL and SST data are accounted for about 78% and 73% of total variance in the observed values, respectively. The first rotated EOF of RAINFALL data has a high loading over Fars, Bousher and Khuzestan provinces. The 2nd rotated EOF was dominant over Hormozgan province. The rotation is generally employed for the detection of the dependence of each PC with the raw data set. The amplitude score of predict and (precipitation) and predictors (SST) are used as the CCA input files. The results hav indicated that the fluctuations of SST in central part of the PERSIAN GULF, off the coast of Bousher, are accounted for a large portion of the total variance in SST data. These fluctuations during winter time play an essential role for explaining the variance of considered RAINFALL data. The four retained EOFs of the PG SSTs are accounted for about 27% of total variance of winter RAINFALL. The influential role of the PG SST was more evident over Fars and Bousher rather than Khuzestan province. The SST process during spring and summer was found to be a considerable element influencing winter RAINFALL in Khuzestan.

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APA: Copy

NAZEM ALSADAT, S.M.J., & SHIRVANI, AMIN. (2006). PREDICTION OF WINTER RAINFALL OF SOUTHERN IRAN USING PERSIAN GULF SEA SURFACE TEMPERATURE: CANONICAL CORRELATION ANALYSIS APPLICATION. THE SCIENTIFIC JOURNAL OF AGRICULTURE (SJA), 29(2), 65-77. SID. https://sid.ir/paper/24837/en

Vancouver: Copy

NAZEM ALSADAT S.M.J., SHIRVANI AMIN. PREDICTION OF WINTER RAINFALL OF SOUTHERN IRAN USING PERSIAN GULF SEA SURFACE TEMPERATURE: CANONICAL CORRELATION ANALYSIS APPLICATION. THE SCIENTIFIC JOURNAL OF AGRICULTURE (SJA)[Internet]. 2006;29(2):65-77. Available from: https://sid.ir/paper/24837/en

IEEE: Copy

S.M.J. NAZEM ALSADAT, and AMIN SHIRVANI, “PREDICTION OF WINTER RAINFALL OF SOUTHERN IRAN USING PERSIAN GULF SEA SURFACE TEMPERATURE: CANONICAL CORRELATION ANALYSIS APPLICATION,” THE SCIENTIFIC JOURNAL OF AGRICULTURE (SJA), vol. 29, no. 2, pp. 65–77, 2006, [Online]. Available: https://sid.ir/paper/24837/en

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