Abstract
Using wavelet analysis, the variability and oscillations of November–January (NDJ) and January–March (JFM) rainfall (1974–2006) of Taiwan and seasonal sea surface temperature (SST) of the Pacific Ocean were analyzed. From the scale-average wavelet power (SAWP) computed for the seasonal rainfall, it seems that the data exhibit interannual oscillations at a 2–4-yr period. On the basis of correlation fields between decadal component removed wavelet PC (DCR-WPC1) of seasonal rainfall and decadal component removed scale-averaged wavelet power (DCR-SAWP) of SST of Pacific Ocean at one-season lead time, SST of some domains of the western Pacific Ocean (July–September SST around 0°–30°N, 120°–160°E; October–December SST around 0°–60°N, 125°E–160°W) were selected as predictors to predict seasonal NDJ and JFM rainfall of Taiwan at one-season lead time, respectively, using an Artificial Neural Network calibrated by the Genetic Algorithm (ANN-GA). The ANN-GA was first calibrated using the 1975–99 data and independently validated using 2000–06 data. In terms of summary statistics such as the correlation coefficient, root-mean-square error (RMSE), and Hanssen–Kuipers (HK) scores, the prediction of seasonal rainfall of northern and western Taiwan using ANN-GA are generally good for both calibration and validation stages, but not so for southeastern Taiwan because the seasonal rainfall of the former are much more significantly correlated to the SST of selected sectors of the Pacific Ocean than the latter.
Crop yield in India in relation to EI-Nino
