Short-term climate prediction of Mei-Yu rainfall for Taiwan using canonical correlation analysis
✍ Scribed by Chu, Pao-Shin
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 358 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0899-8418
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✦ Synopsis
Canonical correlation analysis (CCA) is used for predicting Mei-Yu (May±June) rainfall for eight major stations in Taiwan based on the antecedent November±December sea-surface temperatures (SSTs) over the Paci®c Ocean (50 N±40 S, 120 E± 90 W). To reduce the large dimensionality of the SST data set, an empirical orthogonal function analysis is ®rst performed and the leading nine eigenmodes of SST are retained as predictors. The root-mean-square error and the Pearson productmoment correlation coef®cient are used to serve as a yardstick in overall forecast evaluation.
Forecasts are made for the period 1986±1995, which is independent from the developmental data sets. A moderate skill is achieved for most stations. In particular, Mei-Yu rainfall is more predicable for the last 3 years (1993±1995), when the island experienced a long spell of de®cient rainfall. A cross-validation technique is used to estimate the overall hindcast skill of the CCA model for the period of 1956±1995 and results suggest that certain stations have more skill than others. Likewise, a CCA `climatological prediction' is conducted in a cross-validated mode.