臺灣博碩士論文加值系統

　　近年來利用GPS計算大氣中的可降水量（Precipitable Water, PW）之技術趨於成熟，GPS訊號延遲反演計算的可降水量資料與水氣微波輻射儀（Water Vapor Radiometer, WVR）和探空氣球（Radiosonde）資料有極高相關性也是很好的替代資料，部分文獻中提到降雨量與可降水量之相關性佳；但另一部份文獻中則指出無明顯相關性，顯示降雨量可能因降雨機制不同而異；GPS觀測PW之平均值也受GPS測站位置的高程影響，一般研究中高程愈高所計算之PW愈低，常呈現反比關係，顯示PW在不同高程可能有特定之分布這些現象在前人研究中較少探究其原因。台灣因GPS連續站密集，地勢起伏大，且有豐富的降雨事件正好可供驗證。
　　本研究使用MIT（Massachusetts Institute of Technology）GAMIT/Globk 10.60版本，與中央氣象局提供之GPS連續站資料，比較在不加入溫度、大氣壓力的條件下，進行GPS-PW計算，與利用地面氣象資訊計算之理論值做比較，並探討其與降雨量之間關係。
研究結果顯示，(1)不加入地面氣象資訊（溫度、壓力）條件下解算的PW值，在花蓮站（高程46.6m）解算成果誤差約1 mm(1)，但在玉山站（高程3876.6m）誤差約5.4 mm(1)，其原因是GAMIT軟體使用全球大氣壓力數值過高（高度修正不足）所導致。(2)花蓮站周邊六個GPS連續站PW解算成果，所有平地站得到的GPS-PW有很好的重複性，誤差約1.7mm(1)，而影響PW值高低最大的因素是GPS高程。(3)在長期觀測上，梅雨季鋒面對PW平均值的影響比颱風大，但颱風行經時對PW短暫影響可達20-30mm左右的幅度變化。(4) 不同高程之GPS-PW，如果未進行乾延遲修正，GPS-PW只會反應實際天頂總延遲量而非濕延遲量，所以因相鄰站天頂總延遲量相似相關性良好，約在0.79-0.98間，未修正之PW平均值隨著GPS高程愈高而愈低(R2=0.97)，代表GPS高程愈高天頂總延遲量愈小。(5) 不同時間長度之平均降雨量與PW之相關性，顯示拉長平均時間由1小時至數十小時，PW與降雨量之間可能存在著一定的趨勢或相關性。

　In recent years, the technique of calculating Precipitable Water (PW) by using GPS signal delay has developed rapidly, the GPS-PW data is a good proxy of those of water vapor radiometer (WVR) and Radiosonde data. Some previous studies showed rainfall and PW have a good correlation; but others showed there is no correlation at all. This may indicate that the mechanism of rainfall is the key to the understanding of rainfall forecast. The higher of a GPS station elevation, the lower of its GPS-PW, this may result from the spatial distribution of PW is elevation-related and need further study. Taiwan has dense GPS stations either in low relief urban areas or high mountain areas and there are abundant rainfall events, therefore this area is a good test site for the previous two hypotheses of rainfall-PW and PW-elevation relationships.

　In this study, GAMIT / Globk 10.60 has been chosn to calculate GPS-PW using the GPS data of the IGS and the Central Meteorological Bureau, Taiwan. The GPS-PW calculation used either adding or without adding the temperature and pressure information in order to check the accuracy and precision of the GPS-PW result and its relationship with rainfall data.

　The results show that (1) without adding the temperature and pressure data, the error of GPS-PW is about 1mm (1) at Hualian station (GPS height of 46.6m) but the error at the Yushan station (GPS height of 3876.6m) is about 5.4 mm (1). This results from the GAMIT software adopted the average global atmospheric pressure value was too high. (2) 6 GPS stations around Hualien station have similar GPS-PW results, this indicated GPS-PW method has a good repeatability and precision (the error is about 1.7mm(1)). (3) In a 12 years GPS-PW observation of Hualien station, it shows that the impact of the rainy season/monsoon seasons on the GPS-PW are greater than those of typhoons, but the increase of GPS-PW during the typhoon event is larger (20-30mm). (4) GPS-PW at different elevations shows a correlation of 0.79-0.98 among neighboring stations. Although lower GPS height has higher GPS-PW (R2= 0.97), this uncorrected GPS-PW only reflect the neighboring stations have a similar Zenith total delay.(5) The correlation between the average rainfall and GPS-PW between 1 hour and 48 hours shows that there is a correlation between GPS-PW and rainfall with longer time span of 24 or 48 hours.

第一章 緒論 1
1-1 前言 1
1-2 研究動機 2
1-3 研究目的 4
第二章　文獻回顧與理論基礎 5
2-1 文獻回顧 5
2-2　GPS 衛星定位系統 10
2-2-1　GPS訊號結構 10
2-2-2　虛擬距離（Pseudorange）觀測 11
2-2-3　載波相位(Carrier Phase)觀測 13
2-3 GPS誤差來源 15
2-4 大氣延遲模型 17
2-5 濕延遲量轉換 22
2-6 映射函數（Mapping Function） 23
2-7 重力常數修正 24
第三章　研究材料與方法 27
3-1　研究流程 27
3-1-1　GPS資料與測站位置 28
3-1-2　氣象資料 30
3-2　GPS解算軟體 31
3-3　資料處理流程 32
3-3-1　GAMIT/GLOBK軟體介紹 33
3-3-2　GPS資料解算設定 35
3-4　資料整理與分析 37
3-5　乾延遲與濕延遲理論值計算 39
第四章　實驗結果與討論 41
4-1　GAMIT軟體解算成果 41
4-2　花蓮市周邊GPS連續站解算GPS-PW成果比較 46
4-2-1　花蓮站GPS-PW與降雨關係 48
4-2-2　強降雨（颱風）與GPS-PW關係 50
4-2-3　不同時間長度之平均降雨量與PW之相關性 58
4-3　 GPS高程對可降水量影響 59
4-4　討論 62
第五章　結論 65
參考文獻 67
附錄 72
GAMIT軟體解算設定 72
莫拉克颱風資料 75
蘇迪勒颱風資料 95
花蓮站資料 102
玉山站資料 293

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