如何改進劇烈天氣系統之定量降水預報是數值預報持續關注的議題與挑戰。而水氣場在對流系統當中為相當重要的資訊。同化都卜勒風與回波資料對提升數值預報有相當程度的改善，但在調整水氣場以獲得最佳分析場與數值預報上有其限制。本論文旨在研究同化掃描式雷達所反演兩種不同類型的水氣資訊，包括 S-PolKa 雙波長反演的垂直剖面水氣與雷達折射指數反演的近地表二為水氣資訊。 本研究第一部分將S-PolKa反演得到低層降水系統周圍的垂直剖面水氣資訊，與雷達回波和都卜勒風一起進行同化。採用 WRF 局地系集轉換卡爾曼濾波資料同化系統，在 Dynamics of the Madden-Julian Oscillation Experiment (DYNAMO)觀測實驗的三個真實個案進行一系列實驗測試。同化時，濕度的垂直剖面被分為平均後之單點單一剖面和四個象限上分別提供水氣剖面資訊，並在 1) 與連續 2 小時的雷達資料和 2) 在第一小時單獨同化水氣資訊，然後在第二小時接著同化徑向風和回波。結果顯示，與僅同化雷達資料相比，額外同化水氣資訊顯著地改進了對流尺度的分析場，進而提升定量降水預報的表現。此外，策略上第一小時僅同化水氣資料，而後於第二小時同化徑向風和回波資料其結果呈現出最佳之分析場，且與其他實驗相比，定量降水預報的改進最大。而同化時若將水氣剖面分為四個象限在分析場和預報結果上會更加理想。 本論文的第二部分重點研究同化能代表地表附近水氣資訊的雷達折射指數。利用西南季風觀測實驗中的兩個真實個案，採用高解析度 WRF 局地系集轉換卡爾曼濾波器資料同化系統。測試兩組不同的實驗。在第一組實驗中，探討額外同化雷達折射指數反演水氣之影響。結果顯示，同化反演進地表之水氣除了調整水氣分布之外，也同時增強風場的輻合作用，改進定量降水預報的能力。此外，同化折射指數的影響，取決於背景場之情況，也就是在具有更廣泛折射指數分佈且偏乾背景水氣場中，效果尤為顯著。第二組實驗主要在測試天氣系統著陸前後同化折射指數的效益。結果顯示，降水系統登陸島上後，持續同化雷達折射率對短期預報具有優勢。此外，測試結果亦建議於天氣系統登陸之前便開始同化折射指數，能獲得最佳的定量降水預報表現，尤其在強降雨的區域更為顯著。 ;Improving the quantitative precipitation forecast is the key ongoing challenge in weather prediction. Despite the positive impact on the enhancement of numerical weather prediction, the assimilation of reflectivity and radial velocity cannot fully adjust the water vapor field to achieve an optimal short-term forecast. However, moisture information is proved to be critical for convection analysis and forecast. This thesis aims to investigate the additional assimilation of two different kinds of moisture data including the S-PolKa-retrieved water vapor and radar-retrieved refractivity. In the first part of this dissertation, the S-PolKa-retrieved water vapor data which represents the environmental information outside the precipitation at the low level was assimilated with reflectivity and radial wind. The WRF local ensemble transform Kalman filter data assimilation system was employed to examine a series of experiments in three real cases of the Dynamics of the Madden-Julian Oscillation Experiment. The vertical profiles of humidity were thinned into one averaged and four-quadrant profiles and assimilated 1) with radar data for the entire 2 h and 2) alone in the first hour, followed by radial wind and reflectivity assimilation in the second hour. The results revealed that assimilating additional water vapor data more markedly improved the analysis at the convective scale, leading to more significant improvements in the rain forecast compared with assimilating radar data only. In addition, the strategy of assimilating only retrieved water vapor data in the first hour and radial wind and reflectivity data in the second hour achieved the optimal analysis, resulting in the most improvement in rain forecast compared with other experiments. Furthermore, assimilating moisture profiles into four quadrants achieved more accurate analyses and forecasts. The second part of this dissertation focus on examining the assimilation of radar-retrieved refractivity which carries moisture information near the surface. Two real cases in the Southwest Monsoon Experiment were deployed with the high-resolution WRF local ensemble transform Kalman filter data assimilation system. Two different sets of experiments were investigated. In the first experimental group, the role of extra refractivity assimilation was investigated. The results indicated that additional refractivity assimilation improved the quantitative precipitation forecasting by generating the optimal moisture, temperature, and wind adjustment and enhancing the wind convergence. Moreover, the level impact of refractivity assimilation on the short-term forecast is markedly notable in dry-biased background moisture with broader refractivity distribution. The second experimental set was utilized for studying the refractivity assimilation before and after the weather system landed. The results revealed that assimilating radar refractivity continuously after the precipitation system landed on the island has advantages for the short-term forecast. Additionally, this study suggested starting assimilating refractivity before the weather system landed to obtain the optimal quantitative precipitation forecasting, particularly for heavy rainfall.
