| dc.description.abstract | The purpose of this study is to improve statistical downscaling of quantitative precipitation forecast and use these daily rainfall simulation models to project changes in frequency and magnitude of future daily rainfall. Therefore, an automated synoptic weather typing and stepwise cumulative logit/nonlinear regression analyses were employed to estimate the occurrence and quantity of daily rainfall events in Taipei station.
Taipei station hourly and daily observed and NCEP reanalysis weather data for each year of 1992-2012 without 1997 are used in this study. The analyses are divided into four steps: (i) automatic synoptic weather typing, (ii) identification of weather types associated with rainfall events, (iii) development of within-weather-type rainfall simulation models, and (iv) validation of the rainfall simulation models using an independent dataset. The 7 rainfall-related weather types are cold front, quasi-stationary front I, quasi-stationary front II in cold season (November- April), quasi-stationary front III, tropical low, local convection and typhoon in warm season (May-October). The results show that within-weather-type rainfall simulation models demonstrated significant skill in the discrimination and prediction of the occurrence and quantity of daily rainfall events with exceptions of localized convective storms. The percentage of excellent and good simulations for the light rainfall events improved by 5~10%, but the heavy rainfall simulations have no significant improvement because of greater variability. At the time of raining, the weather variables at the station didn’t reflect the characteristics of the moving convective system, so statistical downscaling models cannot capture moving and mesoscale convective rainfall. Typhoon is not suitable for automatic synoptic weather typing and it is easily mix up with tropical low. Then we use typhoon path classification methods and the simulation results will be better.
The results from this study show that a combination of synoptic weather typing and cumulative logit/nonlinear regression procedures can be useful to simulate historical daily rainfall occurrence and quantity in Taipei station. If there are more improvements in these models, the statistical downscaling method can be applied to study occurrence and quantity of daily rainfall events under climate change in the future.
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