Distribution of 6-h accumulated typhoon rainfall over Taiwan area is investigated through empirical orthogonal function (EOF) analysis. The data set used includes the rainfall of 20 Central Weather Bureau surface stations when typhoons were inside the domain between 18°N and 18°N, 116°E and 126°E, form 1961 to 1996. Analysis results show that the first three EOF modes are well separated from the other modes. Those three modes contain about 66% of the total rainfall variances. At some stationsk only those three modes can effectively represent the station rainfall. The first EOF mode not only reveals the in-phase increasing or decreasing rainfall at all stations of Taiwan but also shows that larger rainfall occurs at stations with higher elevation and larger slope. The enhancement of rainfall in mountainous areas is an indication of topography effect on redistributing rainfall. The second mode and the third mode both show out-of phase distribution of rainfall over Taiwan when typhoons are nearby. The rainfalls are enhanced on the up-wind sides of the mountain, and the rainfalls are suppressed on the down-wind sides. Those distributions again reveal the effects of redistributing rainfall by Central Mountain Range of Taiwan.
To demonstrate how the EOF modes may be used on typhoon rainfall forecasting, several forecasting methods are evaluated. Those methods include four basic methods and four ensemble forecasts. The basic methods are: Climatology Average Method, Deviation Persistence Method, regression equation forecast based on station rainfall, and regression equation forecast based on the amplitude of the first three EOF modes. The ensemble forecasts are the simple averages taken from the last three basic methods. The results show those basic methods are compatible with each other, except the Climatology Average Method that underestimates all heavier rains and is outperformed by the others. Since the amplitudes of EOF modes depend on how the rainfall is distributed spatially over all stations of Taiwan, a forecast method based on EOF modes is capable of providing different information from other methods that based on factors only related to a single station. The study also shows that the ensemble forecasts outperform their corresponding member forecasts.