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• Main Authors: Yan-ming Shao, 邵彥銘
• Other Authors: 廖宇慶
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/77556699638254913736

碩士 === 國立中央大學 === 大氣物理研究所 === 103 === The Local Ensemble Transform Kalman Filter (LETKF) method, coupled with Weather Research and Forecast (WRF) model, is applied to assimilate data from five Doppler radars in Taiwan, with the purpose of investigating the improvement on short-term quantitative precipitation forecast (QPF) for rainfall events occurred during the Mei-Yu season. Two heavy precipitation cases from the 2008 SoWMEX IOP#8 field experiments are selected. The overall results demonstrate that by using WRF-LETKF to assimilate the radar data, the performance of model QPF for representing the Mei-Yu rainfall can be significantly improved. In the first case of June 14, 2008, it is found that by assimilating the 0 dBZ data, the spurious convection can be effectively suppressed. Extending the length of the radar data assimilation to two hours produces better rainfall forecast results. Generating initial perturbations from randomly selected, 6-hr apart data from the NCEP 1ox1o re-analysis data turns out to be a better way to capture the uncertainty related to the Mei-Yu frontal flow than the original NCEP NMC method does. The same model setup and assimilation method is applied to the second event on June 16, 2008. The pattern and amount of the forecasted rainfall pattern and over southwestern Taiwan indicates a very encouraging result. However, the rainfall prediction over eastern Taiwan becomes unrealistic strong, and this over-estimation cannot be mitigated due to the lack of radar data in this area. This indicates the importance of having a complete radar coverage over Taiwan and vicinity area.