為探討加入FORMOSAT-3 GPS RO資料對東亞地區梅雨天氣系統診斷分析與模擬的影響，本文針對2007年梅雨季6月6-9日的豪大雨個案，透過客觀分析方法與WRF 3DVAR資料同化方法，將FORMOSAT-3 GPS RO觀測資料融入CWB GFS氣象場分析。其中客觀分析使用Gandin在1963年提出的最佳內插法，將FORMOSAT-3 GPS RO反演探空資料融入氣象場後進行診斷分析。WRF 3DVAR方法則是將FORMOSAT-3 GPS RO資料同化至氣象場，求得最佳初始場後，再以WRF模式進行72hr的模擬，分析初始場的差異量及隨時間模擬的變化情形。 客觀分析的結果在陸地上並沒有明顯的差異，顯示FORMOSAT-3 GPS RO資料與無線電探空相當協調，但在海面上卻有相當明顯的差異，最顯著的影響是等壓面的高度場分析中，太平洋副熱帶高壓脊的強度明顯的增強。由於一般氣象中心全球模式之客觀分析，大都以氣候場作為初始猜測場，因此，在海面上造成明顯差異的主要原因，可能是初始猜測的氣候場與2007年太平洋副熱帶高壓脊西伸的強度不符所導致。 經由WRF 3DVAR方法將FORMOSAT-3 GPS RO資料同化至模式的結果，發現初始分析場的差異量與資料的所在位置明顯相關，修正量呈現近似同心圓的分布。而差異量經模式積分後有擴大現象，最大值大多落在梅雨鋒面的強對流位置及太平洋副熱帶高壓脊前緣。模擬結果也顯示加入FORMOSAT-3 GPS RO資料後，WRF模式模擬能夠突顯對流發展的特徵，如中高層對流雲頂溫度的降低及位渦的增強等現象。 透過兩種方法將FORMOSAT-3 GPS RO資料融入氣象場，對各種氣象參數上皆有不同程度的影響，這些差異有些是可以透過其他觀測資料(如衛星雲圖)推斷是正面的，但有些則尚難推斷。因此，以目前FORMOSAT-3 GPS RO資料的分布狀況，對梅雨天氣分析與預報模擬尚難有明確且有系統性的正面提升。目前世界各先進國家也陸續規劃佈署更好的GPS RO觀測衛星，國內也提出了春雷衛星(福衛三號後續)計畫，期望可大幅提高觀測資料的量與品質，對未來整體氣象的發展及天氣分析與預報技術的提升，定會有明顯的貢獻。 In order to understand the impact of FORMOSAT-3 GPS RO measurements on the diagnosis and model simulation of Mei-Yu system in the East Asia region, this study investigates a heavy rain case occurring during 6-9 June 2007 by blending FORMOSAT-3 GPS RO data into CWB GFS analyses via objective analysis and WRF 3DVAR methods. The objective analysis method utilized is the optimum interpolation method. For the WRF 3DVAR method, FORMOSAT-3 GPS RO data are assimilated into the CWB GFS fields to get the best initial fields as the first step, then a 72hr simulation is conducted using the WRF model. Differences between analyses with and without FORMOSAT-3 GPS RO data are examined and compared. Comparison results of objective analyses show no obviously difference over the land. This suggests that the FORMOSAT-3 GPS RO data are highly comparable with the radiosonde observations. However, obvious differences can be found over the ocean with particularly noticeable enhancement of Pacific subtropical high ridge line. In a global model, climatic analysis is usually used as the first guess field during the initial objective analysis procedure. Thus, the differences could be due to the discrepancy of the intensity of Pacific subtropical high ridge between the first guess field and the real atmospheric state of 2007. After assimilating the FORMOSAT-3 GPS RO data into the model initial fields by the WRF 3DVAR method, results show that the differences of initial fields are highly correlated with the positions of the GPS RO data and the corrections exhibit like concentric circles. The differences will gradually expand during the model integration with the maximum corrections often found in areas of active convection along the Mei-Yu front and the front peripheries of Pacific subtropical high ridge. The results also show that model simulations with FORMOSAT-3 GPS RO data will enhance the characteristics of convective activities such as cloud top cooling and intrusion of high potential-vorticity air in the middle and upper troposphere. By blending of FORMOSAT-3 GPS RO data into the analysis fields in these two ways, results show different degrees of impact on different meteorological parameters. Some of the impacts are positive and can be verified from other measurements such as satellite images. However, some of them are hard to give precise conclusions. Therefore, with the current data amount and distribution of FORMOSAT-3 GPS RO data, a systematic improvement in the diagnosis and forecast of the Mei-Yu system seems unfeasible at present. Now, many countries are planning on launching more and advanced GPS RO satellites, and in Taiwan we also have a FORMOSAT-3 follow-up mission too. We hope that, in the future, even better quality and denser data will contribute to advanced evolution of meteorology and improvement of weather analysis and forecast.
