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Wavelet Analysis on the Variability, Teleconnectivity, and Predictability of the Seasonal Rainfall of Taiwan

Wavelet Analysis on the Variability, Teleconnectivity, and Predictability of the Seasonal... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Wavelet Analysis on the Variability, Teleconnectivity, and Predictability of the Seasonal Rainfall of Taiwan

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Publisher
American Meteorological Society
Copyright
Copyright © 2008 American Meteorological Society
ISSN
1520-0493
DOI
10.1175/2009MWR2718.1
Publisher site
See Article on Publisher Site

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.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Jul 10, 2008

References