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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/5032


    Title: FORMOSAT-3 GPS RO觀測資料之校驗及其對東亜地區梅雨季天氣分析與預報之系統性影響
    Authors: 吳家苓;Chia-Ling Wu
    Contributors: 大氣物理研究所
    Keywords: 梅雨季預報校驗;衛星資料校驗;GPS RO
    Date: 2008-06-30
    Issue Date: 2009-09-22 09:43:26 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 本研究之主要工作分成兩部分:其一為對FORMOSAT-3 GPS RO反演探空資料與無線電探空觀測之校驗,校驗時間從2006年12月至2007年11月為期一年的全球GPS RO觀測資料,目的在於校驗反演探空資料的可靠性;其二為探討FORMOSAT-3 GPS RO觀測資料對東亞地區梅雨季天氣分析與模式預報之系統性影響,使用客觀分析方法及模式預報誤差統計分析,以有無加入GPS RO觀測資料分析2007年梅雨季的模式預報誤差,目的在於評估引進GPS RO衛星觀測資料對東亜地區梅雨天氣系統研究的影響並探討其應用性。 GPS RO反演探空之垂直校驗結果顯示,基本上在對流層內等壓面高度場的平均差異幾近於零,標準偏差由地面往上增加,但都未超過100m,20hPa以上略為偏高,但差異都非常小。溫度場在20hPa 以下平均偏差值雖有正值與負值,但都小於0.5°C,標準偏差值約1.5-2°C。水氣壓場的校驗分析整體來說GPS RO的水氣量觀測都偏低,在熱帶地區低對流層尤其近地表在夏季平均偏差可高達3hPa,其他季節則少於2hPa。分析結果顯示GPS RO反演探空觀測品質是可以值得信賴的。 在GPS RO觀測應用在梅雨季天氣分析與模式預報方面,其一為經由最佳客觀分析方法直接將GPS RO反演探空資料融入氣象場分析,比較分析結果發現加入GPS RO反演探空資料在陸地上差異不大,但在海面上太平洋副高脊延伸地區確有明顯的差異,主要原因可能是做為客觀分析初始猜測場與實際大氣狀態不符所導致。其二為經由WRF模式及3DVAR資料同化技術將GPS RO觀測資料融入模式初始分析場,經模式初始化過程後差異並不明顯,即使使用update cycling run的方式,仍對東亞梅雨季天氣預報影響不大。同化GPS RO觀測資料後,一開始模式預報場的RMSE較大,但隨著模式積分時間到60小時後,RMSE開始小於未加入GPS RO觀測資料的實驗組。若只校驗海洋上之RMSE,則可發現在模式積分24小時之後,加入GPS RO觀測資料之RMSE將會比未加入小,這也表示GPS RO觀測資料在缺乏傳統觀測的海面上有正面的幫助。 The contents of this paper are divided into two parts. One is the verification of FORMOSAT-3 GPS radio occultation (RO) retrieved data. The time period of verification is from December 2006 to November 2007. The goal of this part is to verify the reliability of the GPS RO retrieved data as compared with radiosonde observations. The other is to examine whether there is improvement on weather analysis and model forecast during the Mei-Yu season in the East Asian area by the aid of FORMOSAT-3 GPS RO data. In this part, objective data analysis method and error statistics of model forecasts are adopted. The results of vertical profile verification show that the averaged differences of isobaric geopotential height between the GPS RO data and the radiosonde data are nearly zero in the troposphere, and the standard deviations of differences increase with height and have values less than 100m. The mean differences of temperature below 20hPa show positive and negative values with magnitude smaller than 0.5°C. The standard deviations of the temperature differences are approximately 1.5-2°C. The verification results of water vapor pressure indicate that the GPS RO observations are usually drier than the radiosonde data. The mean difference between them can reach 3hPa near the surface in the tropics during summer, while in other seasons the mean difference is usually smaller than 2hPa. The results of verification suggest that the quality of the GPS RO retrieved data is reliable. In the impact study of the GPS RO data on the diagnosis and model forecast of the Mei-Yu weather system, we use two methods in applying the GPS RO data. One of them is using optimum interpolation method with and without GPS RO data to investigate their influences on weather analysis. The results show very little differences over the land, but distinct differences in the location of Western Pacific subtropical ridge. The major cause may be probably due to the first guess used in the objective analysis not agreeing with real atmospheric condition. The other one is using WRF-3DVAR model to investigate the impact of applying the GPS RO data. The difference is not obvious after initialization process of the model. Even though we used method of update cycling run, it affected only slightly in the model forecast of Mei-Yu development in the East Asian area. At the beginning of the model run, the RMSE of forecasts with GPS RO data is somewhat larger than that without GPS RO data. But with the model integration time longer than 60 hours, the RMSE starts to be smaller than that without GPS RO data. If we only verify the forecast results over the sea, then we can detect that after integrating 24 hours the RMSE with the GPS RO data are smaller than that excluding the GPS RO data. It suggests that the GPS RO observational data are useful for weather prediction over the sea where the traditional observational data are almost absent.
    Appears in Collections:[大氣物理研究所 ] 博碩士論文

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