台灣地區在夏季期間常有劇烈午後熱對流發生，準確估計他們的降水量強度是非常重要的，因為可以減少對伴隨而來的中尺度暴雨所造成交通、農作物與地質等災害損，由於中尺度暴雨發展迅速形成在午後並在2-3小時內降下數十公厘甚至可達百公厘雨量，主要原因為充足的水氣供應中尺度暴雨生成，傳統的氣象觀測資料受到空間及時間的限制，無法提供預報地區性特大豪大雨，因此GPS衛星觀測資料所提供大氣對流層延遲特性、空間辨識率及時間序列之連續性可提供地區性特大豪大雨預測（約提前1-2小時）更顯得重要。
本研究利用GPS的對流層延遲特性之高時空分辨率及觀測時間的連續性，期望藉資料同化調整初始場，進一步改善短期降水預報，首先以台灣南部地區2010年7-9月GPS站觀測資料利用GAMIT軟體之大氣對流層延遲模式反演，初步分析了台灣南部的大氣可降水量（PWV）時空分佈特徵，其後利用WRF（Weather Research and Forecasting）模式的三維變分方法（3DVAR, Three-Dimensional Variational method），研究同化大氣可降水量反演降水資料的影響。
結果顯示，GPS反演的大氣可降水量其時空變化趨勢明顯，反映了台灣南部夏季降水和地區變化特徵並發現在相鄰的地理位置下，其環境水氣分布應相同，透過與降水之間關係分析發現，最大可降水量與最大雨量發生時間差有一定量關係，且受地形影響顯著，另對流性降水於降水初期具有高大氣可降水量，但在降水期間因水氣凝結降落，故其大氣可降水量顯著下降，其次在WRF模式同化大氣可降水量後，對於降水預報在降水強度與WRF模式未經同化預報降水上有顯著改善。

The rapid development of mesoscale rainstorms in summer, plenty of water vapor supply is generated based on mesoscale storms. The traditional ground-based observatories only sampled data from the ground, and are unable to extend the vertical height. Therefore, the high resolution and continuity of observation capabilities of the high-altitude satellites can make up for the shortcoming of the ground-based observatories. The propagation path of GPS signals from high-altitude contained tropospheric water vapor characterized with a vertical distribution, which makes it different from the conventional observation data.
In this study, we will use GAMIT to calculate the data observed by the Tainan station from Jul. to Sep. in 2010. Analyze PWV（precipitable water vapor）in Tainan spatial and temporal distributions by using WRF（weather research and forecasting） model of 3DVAR（three-dimensional variational method）assimilation PWV to predict precipitation data.
The results shows on the characteristics of the distribution of PWV in time and space aspects, when and where the rainfall takes place in the afternoon thundershower that water vapor changes with a high degree of correlation when GPS observatories are near in distance. There is also a fixed relationship between the maximum PWV and the time when maximum rainfall occurs. Affected by various terrains, it causes time difference also called delay effect. Furthermore, after WRF model assimilated PWV, it has great improvements of the intensity of the precipitation and WRF model in the forecast of the precipitation.

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GOOGLE EARTH, http：//earth.google.com
IGS Tracking Network, http：//igscb.jpl.nasa.gov/network/netindex.html
WRF, http://www.wrf-model.org/index.php