雷達觀測資料具有高時空解析度的特性，對於在四面環海、地勢複雜的臺灣進行天氣監控便相當重要。近年來，雷達資料也經常用於數值模式在對流尺度下的資料同化中，因此本研究應用臺灣地區多座都卜勒雷達資料，取得接近當時真實大氣的模式初始條件，改善模式的定量降水預報(Quantitative Precipitation Forecast，簡稱QPF)。本文所採用的資料同化方法主要包含：(1)多都卜勒雷達風場合成、(2)熱動力反演方法以及(3)溫度場與水氣場調整方法。在此以2008年西南氣流實驗(SoWMEX) 6月14日1200UTC作為選定的真實個案，雷達資料來自於中央氣象局的七股(RCCG)、墾丁(RCKT)、五分山(RCWF)、中央大學C-POL雷達以及美國NCAR的SPOL共5座雷達之回波與徑向風，搭配模式來源為ECMWF 再分析資料與探空、地面測站經客觀分析得到的模擬背景場，接著反演出三維風場、熱動力場(溫度與壓力)以及水氣場，最後放入數值模式WRF，進行6小時預報。本研究先以雷達回波資料在空間分布的性質設計實驗，設法對回波資料的無效值做區分，使資料的使用程度提高。另外，也使用大氣條件性不穩定的判別更新水氣場的門檻值，條件性不穩定以飽和相當位溫垂直遞減率為負值判定，同化結果顯示對於降水預報有一定程度的改善。6月14日1200UTC個案指出，在雷達網包含整個臺灣的狀態下，模式的預報能力可達2小時。最後吾人另擇同年6月16日0600UTC個案，以利用大氣條件性不穩定判別水氣場之實驗進行模式定量降水預報，顯示預報能力可達3小時。經過本研究測試，雷達資料同化在3小時內的QPF有其必要性，且反演方法僅需要少量雷達觀測資料便可進行資料同化，以達到改善模式定量預報之目的，相對於其他類型的資料同化系統而言(例如：4DVAR或EnKF)，能降低計算成本。未來可將此方法結合以氣象衛星反演的垂直探空資料、多樣的個案測試、或是加密雷達監測網，提高本方法對於臺灣梅雨季之定量降水預報的穩定性。

There are high special and temporal characteristics in radar observations. Thus, it is important in weather survillence around the world. Recently, radar observations are also usually used in data assimilation for convective-scale systems. In the research, we attempt to use the Doppler radar data in Taiwan, then acquire the model initial condition to improve the Quantitative Precipitation Forecast (QPF). In this paper, the radar data assimilation mainly involves in the multiple-Doppler radar wind synthesis system, thermodynamic retrieval method, and temperature/moisture adjustment. A real case of 1200 UTC on 14 June 2008 during Southwest Monsoon Experiment (SoWMEX) is selected. Observational data from 5 radars are utilized, including S-POL (operated by NCAR), RCWF, RCCG and RCKT (operated by the Central Weather Bureau of Taiwan), and National Central University C-POL. The ECMWF reanalysis data, radiosondes, and surface mesonet stations are prepared to simulate a background field, and they are instantaneously applied to update the three-dimentional wind field, thermodynamic fields (pressure and temperature), and moisture fields. Therefore, we can regard them as initial fields for 6-hour model forecast. There are two tests in our experiments. The one is trying to classify the missing value of radar reflectivity. In this way, we can increase the data usage of radar observations. The other is using the conditional instability to modulate the saturation thresholds. It allows the model to forecast the precipitation much accuratly over the southwestern area in Taiwan. Similarly, another real case of 0600 UTC on 16 June 2008 is selected to use this modified saturation thresholds. The results show that the strategy in temperature/moisture adjustment, to a certain extent, improve the model QPF skill. To conclude, the above-mentioned experimental results imply that the model QPF skill in the convective-scale can be significantly improved about 2-3 hours using fewer radar data in these real case studies.

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