依據106 年度科技部自然司防災科技學門專題研究計畫課題重點說明(氣象領域，學門代碼：M1710)之研究課題1-2(即時與極短期暴雨強風預報技術之建立與應用)，本計畫打算欲透過改善氣象模式系集預報在中至對流尺度之水氣場及其系集離散度改進雷達資料同化使用效能，以提高極短時降雨預報準確度。目前雷達系集資料同化系統在台灣已成功建置，並對颱風或梅雨期間之極短期降雨預報上有重要的貢獻。但因台灣複雜地形影響，強對流發生及豪大雨降雨機制複雜，且模式物理過程仍有許多不確定的模式誤差等，造成在豪大雨預報上之困難。因此本計劃預計透過兩個部分來執行，第ㄧ部分則是評估不同擾動法改進系集離散度特性，以代表模式誤差之不確定性，進而改進雷達資料同化效益，並期能改進如定量降水機率預報之系集預報產品。第二則是利用區域系集同化系統透過同化水氣相關之觀測資料，包含衛星溫濕剖線及地基GPS ZTD資料改進模式之濕度場準確度，以增強模式對強對流發生條件之描述以利提高後續雷達資料同化效益。前期進度已進行不同擾動法，包含使用梅雨鋒面環境場擾動，隨機動量(SKEB)及隨機物理參數化趨勢(SPPT)擾動對雷達資料同化系統效益及後續降雨預報影響。結果顯示，使用SPPT及梅雨鋒面環境場可有效改進六小時以上之豪大雨預報位置及強度。此外，雷達資料檢測對於正確描述水氣量分部對於極短期豪雨預報亦有重要且正面的影響。 ;The WRF-Local Ensemble Kalman Filter with Radar data Assimilation System (WLRAS) has been established for improving the very short-term heavy rainfall prediction in Taiwan. Although WLRAS can provide significant improvement on heavy rainfall prediction, there are still many challenging issues due to the complex terrain, complicated mechanisms for strong convections and heavy rainfall at different time scales and unknown errors in model physics. This proposal aims to improve the rainfall prediction by proving a better model moisture field before conducting WLRAS. This will be achieved by assimilating moisture-related observations, such as the satellite-derived temperature and moisture profiles and GPS-ZTD data, to improve the model condition in the parent domain. Also, we would like to improve the representation of model errors by using different types of perturbation generators, including the stochastic multi-scale perturbation, stochastic physical parameterization tendency and Meiyu front-related environmental perturbations in order to increase the ensemble spread for radar data assimilation. The optimal goal is to improve very short-term rainfall prediction and provide a reliable ensemble production, such as the PQPF. Preliminary results from the first year of this project show that applying SPPT or the additive Meiyu-front related environmental perturbations are useful for improving the performance of WLRAS and has a great impact on the 6th to 12th rainfall forecast. The improvements include the location and intensity of the heavy rainfall.
