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During the latest several decades, there has been considerable interest in revealing the relationship between El Nino/Southern Oscillation (ENSO) and hydro-meteorological variables. The oscillation is characterized by a simple index, the Southern Oscillation Index (SOI). Various approaches have been proposed for detection of correlation between SOI and hydro-meteorological variables. However, some of the approaches present a limited applicability for the data, which cannot be normalized by usual method such as power transformation. When the relationship between pairs is strongly nonlinear, usual Pearson's correlation coefficient also results in the spurious evidence for the relationship.In the present study, we propose two nonparametric approaches with the overall purpose to obtain meaningful results from the relationship between SOI and precipitation. A data transformation is proposed for the monthly precipitation data in Fukuoka, Japan. The method transforms the precipitation data into nonexceedance probability time series. Also, Kendall's correlation coefficient, which is a rank-based procedure and can represent a nonlinear relationship between pairs, is used and verified through the comparison with the results by usual methods at the same station.

NONPARAMETRIC APPROACH FOR DETECTION OF CORRELATION BETWEEN SOI AND PRECIPITATION