台灣北部出現豪大雨的梅雨鋒面系統個案 2012 年 6 月 11 日， 10小時累積雨量多處超過400毫米，北部地區許多測站降雨紀錄創下歷史新高，造成北部各地出現淹水災情。本研究選用此個案，從雷達觀測資料分析，反演三維風場與熱動力場分析系統在台灣北部造成強降雨的複雜因素。進一步探討系集同化系統同化雷達資料，同時加入反演三維空間高解析熱動力場對於極端強降雨事件的降雨預報可行性探討。 研究利用高時空解析亦可解析地形的多都卜勒雷達風場反演技術(WISSDOM)與熱動力反演與水氣調整技術(TPTRS)，從反演結果得到三維風場、溫度、壓力與水氣三維結構結構。透過垂直渦度收支分析強降雨期間，對流尺度上地形噴流移動和強度的變化。研究發現，在台灣北部移速慢的梅雨鋒面，冷池前緣發展新的對流，受地形影響和加強的地形噴流形成Y型回波，對流被地形噴流往北推移和主對流合併加強系統，造就此歷史性的短延遲強降雨個案。 研究使用 WRF-LETKF 雷達同化系統，探討多尺度天氣系統中，同化雷達觀測資料之外，加入同化 3D 溫度和水氣資訊，進行可行性的影響評估。設計觀測系統理想模擬實驗，同化的熱動力變數來自理想無偏差或熱動力反演技術得到的熱動力變數。首先，理想實驗中同化兩小時的雷達資料顯示出比一小時更好的結構和短期降雨預測。其次，從同化雷達觀測變數和完美無偏差的熱力變數(溫度與水氣)分析結果顯示，當背景場出現降水位置誤差時，同化雷達資料加入溫度和/或水氣資訊一起同化，可以修正雨帶位置，縮短同化週期，得到較佳的預報分析場，並顯著改善定量降水預報。第三，進行反演熱力變數同化可行性研究。由於反演溫度和水氣存在偏差。加入反演溫度的資料同化實驗，由於溫度存在暖偏差，分析場的結果顯示，提升對流區的垂直運動和層狀區的冰相變數結構，並在極短期強降雨的預報有明顯效益。同化水氣助於重建近地表冷池的範圍和強度，但對三小時降雨預報的改進有限。同時加入同化反演溫度場和水氣場時，此實驗結果取得了最佳的分析，並能顯示降雨預報效益至少維持六小時。綜上所述，同化複雜降水系統中的三維熱力變數，此效益能縮短同化時間，提升最終分析場結構和短期降雨預報。 ;A frontal system with extremely heavy rainfall was over Northern Taiwan on 11 June 2012. Through multiple analyses of three different Doppler radars, three-dimensional wind fields are retrieved over the ocean and the complex terrain of Taiwan by Wind Synthesis System using Doppler Measurements (WISSDOM). The pressure and temperature structure are derived from the retrieved wind fields by Terrain-Permitting Thermodynamic Retrieval Scheme (TPTRS). The migration and intensity of the barrier jets at convective scales are revealed by a vorticity budget analysis. It is found that, taken together, the stagnated Mei-Yu front, the location and the strength of the barrier jet and cold pool, as well as orographic blockage over northern Taiwan explain the formation of this quasi-stationary and extremely heavy rainfall case. This study examined the feasibility of assimilating 3D temperature and water-vapor information in addition to radar observations in a multiscale weather system. Using the WRF–LETKF Radar Assimilation System (WLRAS), we performed three sets of observing system simulation experiments to assimilate radar observations with or without thermodynamic variables obtained using different methods. First, assimilating the radar data for 2 h showed better structure and short-term forecast than 1 h. Second, we assimilated radar data and thermodynamic variables from a perfect model simulation. The results of the analysis revealed that when a precipitation position error was present in the background field, assimilating temperature and/or humidity information could correct the dynamic structure and shorten the spin-up assimilation period, resulting in substantial improvements to the quantitative precipitation forecast. Third, we applied a thermodynamics retrieval algorithm for a feasibility study. With a warm and wet bias of the retrieved fields, assimilating the temperature data had significant impact on the final analysis at the mid-level of stratiform areas and the forecast of the heavy rainfall was consequently improved. Assimilating the water vapor information helped reconstruct the range and intensity of the cold pool near the surface, but the improvement of 3-h rainfall forecast was limited. The optimal results of analysis and short-term forecast were achieved when both the retrieved temperature and water vapor fields were assimilated. In conclusion, assimilating thermodynamic variables in the precipitation system is feasible for shortening the spin-up period of data assimilation and improving the final analysis and short-term forecast.
