TY - JOUR
T1 - Wavelet analysis on the variability, teleconnectivity, and predictability of the seasonal rainfall of Taiwan
AU - Kuo, Chun Chao
AU - Gan, Thian Yew
AU - Yu, Pao Shan
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010/1
Y1 - 2010/1
N2 - 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 08-308N, 1208-1608E; October-December SST around 08-608N, 1258E-1608W) 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 south-eastern 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.
AB - 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 08-308N, 1208-1608E; October-December SST around 08-608N, 1258E-1608W) 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 south-eastern 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.
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U2 - 10.1175/2009MWR2718.1
DO - 10.1175/2009MWR2718.1
M3 - Article
AN - SCOPUS:77953244365
SN - 0027-0644
VL - 138
SP - 162
EP - 175
JO - Monthly Weather Review
JF - Monthly Weather Review
IS - 1
ER -