應用GSMaP全球降雨資料及颱風強度變化改善颱風降雨潛勢預報之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：20

、訪客IP：3.128.197.221

姓名

張怡鈴(Chang Yi-Ling) 查詢紙本館藏

畢業系所

大氣物理研究所

論文名稱

應用GSMaP全球降雨資料及颱風強度變化改善颱風降雨潛勢預報之研究

相關論文

★ 應用SSM/I衛星資料於西太平洋颱風特性之分析	★ 應用衛星資料於熱帶氣旋之環境場分析
★ 衛星資料反演海氣參數及其在梅雨期海上中尺度對流系統生成發展之應用	★ 應用SSM/I衛星資料分析桃芝與納莉颱風之降雨及海氣參數的變化
★ 利用Spot 4衛星的Vegetation資料比較NDVI, ARVI, 及AFRI植被指數與氣溶膠厚度之關係	★ 應用衛星資料分析颱風降雨與颱風強度變化之關係
★ 應用SSM/I衛星資料於颱風中心定位及最大風速估算	★ 應用衛星資料分析海氣參數與颱風強度變化之關係
★ MODIS在生質燃燒監測之應用研究	★ 應用SSM/I衛星觀測資料估算颱風定量降水
★ AMSU衛星資料反演大氣溫濕剖面及其在颱風強度估算上之應用	★ 利用HHT之EMD方法分析SSM/I資料估算之客觀指數與颱風強度年際變化關係
★ 模式和SSM/I客觀潛力指數在中尺度對流系統預報上之應用	★ SSM/I衛星資料估算之客觀潛力指數與颱風強度變化之關係
★ 應用SSM/I衛星資料分析颱風形成之激發機制	★ 衛星資料估算颱風旋轉及強度變化在熱帶氣旋定量降雨預測之研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

Kidder et al.(2005) 提出 TRaP 方法，將衛星反演之降雨分佈平移，迅速估算熱帶氣旋未來可能帶來的強降雨，但TRaP僅適用於地形較平坦之區域。陳(2010)修正TRaP方法，考量地形效應的影響，並根據測站降雨資料重新分配估算颱風降雨，以改善TRaP 方法之結果，稱為I-TRaP。
分析I-TRaP對2012年颱風個案的預報結果，發現颱風強度的改變、預報路徑的誤差、衛星反演雨量的準確性會造成I-TRaP預報結果的誤差，其中以衛星反演颱風完整雨帶影響最大。本研究使用多衛星反演降雨產品GSMaP進行I-TRaP颱風降雨潛勢預報，以便改善微波觀測颱風不完整時所造成的誤差。此外利用GSMaP逐時降雨資料統計2000到2011年所有颱風的降雨強度趨勢時，發現颱風登陸台灣後，因受地形影響，強度會逐漸減弱，颱風中心降雨強度也會逐漸降低，因此根據中央氣象局路徑分類統計各路徑在南、北台灣時的中心降雨強度趨勢，進行總降雨量之修正，期改善I-TRaP的預報能力。
將GSMaP資料放入I-TRaP估算2000~2012年間81個颱風個案之24小時累積降雨，並和測站觀測資料比較，對於SSM/I、SSMIS觀測缺乏完整雨帶的颱風個案，使用GSMaP資料相關係數由0.67提升至0.71，在Mean Error的部分由-17.23mm改善至-15.53mm，整體均方根誤差由81.89mm降低至61.66mm，減少24%的均方根誤差，有明顯的改善。而在降雨的趨勢方面，將GSMaP逐時降雨資料所獲之颱風降雨強度趨勢，加入I-TRaP颱風降雨潛勢中，整體結果並無明顯之改善，主要是由於強烈颱風之風速較強，因地形輻合及抬升造成大量的降雨，使I-TRaP原本就低估降雨，加入降雨強度趨後有更大的誤差。整體而言，結果顯示應用GSMaP全球降雨資料進行I-TRaP降雨預報，能提升SSM/I及SSMIS未觀測到颱風完整雨帶個案之預報精準度。

摘要(英)

Analysis shows that the accuracy of the rainfall potentials from the Improved Tropical Rainfall Potential (I-TRaP) technique are influenced by the changes of typhoon intensity, the accuracy of the predicted typhoon paths, the satellite-retrieved rainfall algorithms, and so on. One of the non-negligible impacts is from the integrity of typhoon rain bands. Whereas many microwave radiometer images from the polar orbiting platforms (such as DMSP satellites) are often lack of data in some areas and periods because of the satellites’ orbits and revisiting rates. That poses a difficulty when using those microwave observation data to estimate a typhoon’s rainfall rate. In the other hand, the analysis result from the hourly GSMaP data shows that the mountainous terrains of Taiwan Island often cause typhoons’ weakening and further rainfall forecasting errors.
In this study, the Global Satellite Mapping of Precipitation (GSMaP) , a temporal-and-spatial-continuous precipitation data set from integrated microwave and infrared images , are used to improve I-TRaP for better typhoon rainfall predications over Taiwan. The results suggest that there is a better agreement to station-measured rainfalls than TRaP and I-TRaP results did. It gives us an opportunity to use the combined microwave and infrared data for getting more accurate typhoon rainfall predications in the future. Moreover, the accuracy of the forecasting rainfall rates can be further handled and improved with considering the change of typhoon intensity. Then, use the long-term trends to correct the I-TRaP-derived rainfall rates. For instance, due to the typhoon circulations for stronger typhoons generally interact with the mountains significantly and trend to bring more topographic rainfall, and then weaken themselves, making I-TRaP under estimate the rainfall by stronger typhoons. With the historical data, such rainfall estimation errors due to the topographic effect can be reduced again.

關鍵字(中)

★ 熱帶氣旋降雨潛勢
★ 颱風降雨強度趨勢
★ I-TRaP

關鍵字(英)

★ TRaP
★ I-TRaP
★ Tropcial cyclone
★ Rainfall potential

論文目次

摘要................................................................................................................................I
Abstract..........................................................................................................................II
致謝..............................................................................................................................III
目錄..............................................................................................................................IV
表目錄..........................................................................................................................VI
圖目錄.......................................................................................................................VIII
第一章緒論..................................................................................................................1
1.1前言......................................................................................................................1
1.2文獻回顧..............................................................................................................2
1.3研究目的..............................................................................................................4
第二章資料蒐集..........................................................................................................6
2.1 SSM/I與SSMI/S微波資料................................................................................6
2.2 全球衛星降雨資料(GSMaP)..............................................................................7
2.2.1 GSMaP降雨率反演.....................................................................................8
2.2.2使用卡曼濾波器結合微波及紅外線降雨資料...........................................8
2.2.3 GSMaP和雨量測站及雷達觀測的差異.....................................................9
2.2.4比較GSMaP及SSM/I完整雨帶的差異..................................................10
2.3地球同步為星紅外線資料................................................................................10
2.4中央氣象局自動測站雨量資料........................................................................11
2.5 JTWC佳路徑資料.............................................................................................12
2.6 QuikSCAT..........................................................................................................13
2.7 綜觀天氣圖.......................................................................................................13
第三章理論基礎與研究方法....................................................................................14
3.1 SSM/I及SSMIS降雨反演式...........................................................................14
3.2熱帶氣旋降雨潛勢(Tropical Rainfall Potential，TRaP)..................................15
3.3 I-TRaP (Improve-Tropical Rainfall Potential)...................................................15
3.3.1.考慮雨帶旋轉進行TRaP雨量估算..........................................................15
3.3.2.考慮地形的影響來做降雨分布修正.........................................................16
3.3.3.總降雨量的修正.........................................................................................17
3.4 I-TRaP降雨估算受颱風雨帶完整之影響 .....................................................17
3.4.1使用GSMaP降雨資料建構資料庫...........................................................18
3.5颱風強度改變對I-TRaP降雨估算之影響......................................................18
3.5.1估算颱風中心降雨強度.............................................................................19
3.5.2應用希爾伯特黃轉換頻譜分析.................................................................20
3.5.3颱風中心降雨強度趨勢.............................................................................22
3.5.4應用颱風中心降雨強度趨勢修正逐時雨量.............................................23
第四章結果分析與討論............................................................................................25
4.1應用GSMaP降雨資料估算I-TRaP颱風降雨潛勢........................................25
4.1.1個案分析.....................................................................................................25
4.1.2整體結果分析.............................................................................................26
4.1.3誤差分析.....................................................................................................26
4.2颱風中心總雨量頻譜分析結果........................................................................27
4.3考慮颱風降雨強度變化修正總雨量................................................................29
4.3.1 各路徑降雨強度趨勢迴歸結果................................................................29
4.3.2個案分析.....................................................................................................30
4.3.3整體結果分析.............................................................................................31
4.3.4誤差分析.....................................................................................................31
4.4獨立個案分析....................................................................................................33
第五章結論與未來展望............................................................................................35
參考文獻......................................................................................................................37
參考網站......................................................................................................................41
附表..............................................................................................................................42
附圖..............................................................................................................................47

參考文獻

蔡清彥、王時鼎、林民生、鄭明典等人: 臺灣地區颱風預報輔助系統建立之研究(二)~ (五)。中央氣象局專題研究報告CWB82-1M-03，1-263。
李清勝與蔡德攸, 1995: 利用CAA都卜勒雷達資料分析四個侵台颱風伴隨雨帶之特徵。大氣科學，23，209-235。
邱清安、林博雄、謝旻耕，2005︰台灣地區氣象測站之詮釋資料與日氣溫、日降水之資料檢定。氣象學報，第 45 卷第 3 期，33-45。
陳嬿如, 2007: 衛星資料估算颱風旋轉及強度變化在熱帶氣旋定量降雨預測之研究。國立中央大學大氣物理研究所碩士論文，台灣中壢，90頁。
陳冠儒, 2010。考量地形與環境風場輻合效應改進TRaP估算侵台颱風降雨預報之研究。國立中央大學大氣物理研究所碩士論文，台灣中壢，94頁。
陳振雄, 2010: 應用希爾伯特-黃轉換之訊號濾波研究, 科學與工程技術期刊, Vol. 6,No. 1, 75-84。
鄭承衡, 2012: 旋轉雨強度變化效應於估算熱帶氣旋登陸後之降雨潛勢。國立中央大學大氣物理研究所碩士論文，台灣中壢，81頁。
Aonashi, K., A. Shibata and G. Liu, 1996: An over-ocean precipitation retrieval using SSM/I multichannel brightmess temperature. J. Meteor. Soc. Japan, 74, 617-637.
Aonashi, K., J. Awaka, M. Hirose, T. Kozu, T. Kubota, G. Liu, S. Shige, S., Kida, S. Seto, N. Takahashi, and Y. N. Takayabu, 2009: GSMaP passive microwave precipitation retrieval algorithm: Algorithm description and validation. J. Meteor. Soc. Japan, 87A, 119-136.
Aonashi, K. and G. Liu, 2000: Passive Microwave Precipitation Retrievals Using TMI during the Baiu Period of 1998. Part I: Algorithm Description and Validation. J. Appl. Meteor., 39, 2024–2037.
Bergeron, T., 1968: Studies of the oreigenic effect on the areal fine structure of rainfall distribution. Meteorological Institute, Uppsala Univ., Report No. 6.
Bergeron, T., 1973: Meteorological studies of precipitation. V. Monthly rainfall in the Uppsala field. Meteorological Institute, Uppsala Univ., Report No.38.
Chang, C. P., T. C. Yeh, and J. M. Chen, 1993: Effects of terrain on the surface structure of typhoons over Taiwan. Monthly Weather Review, 121,734-752.
DeMaria, M. , Mainelli, L. K. Shay, J. Knaff, and J. Kaplan, 2005: Further improvements to the updated Statistical Hurricane Intensity Prediction Scheme (SHIPS). Wea. Forecasting, 20, 531–543.
Ebert, E.E., M. Turk, S.J. Kusselson, J. Yang, M. Seybold, P.R. Keehn, R.J. Uligowski, 2011: Ensemble tropical rainfall potential (eTRaP) forecasts. Wea. Forecasting, 26, 213-224.
Ebert, E.E., A. E. Salemi, M. Turk, M. Spampata, and S. Kusselson, 2009: Validation of the ensemble tropical rainfall potential (e-TRaP) for landfalling tropical cyclones. 16th Conference on Satellite Meteorology and Oceanography, AMS 89th Annual Meeting, Phoenix, AZ, USA, 12-15 January 2009.
Ebert, E. E., 2011: Radius of reliability: A distance metric for interpreting and verifying spatial probabilistic warnings. CAWCR Research Letters, 6, 4-10.
Ferraro, R. R., 1997: SSM/I derived global rainfall estimates for climatological applications. J. Geophys. Res., 102, 16715-16735.
Hashizume, H., S. Shige, T. Kubota, K. Aonashi, and K. Okamoto, 2006: Development of over-ocean SSM/I rain retrieval algorithm in the GSMaP project, IGARSS 2006 Proceedings.
Hilburn, K. A. and F. J. Wentz,2008: Motogating the impact of RADCAL beacon contamination on F15 SSM/I ocean retrievals. Geophys. Res. Lett., 35.L18806.
Hill F. F. 1983. The use of average annual rainfall to derive estimates of orographic enhancement of frontal rain over England and Wales for different wind directions.J. Climatol. , 3, 113–129.
Hollinger, J., R. Lo, G. Poe, R. Savage, and J. Pierce, 1987：Special Sensor Micro- wave/Imager user’s guide. Naval Research Laboratory Washington, D.C., 120 pp.
Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung and H. H. Liu, 1998: “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non- stationary Time Series Analysis”, Proc. R. Soc. Lond. A, 454, 903- 995.
Huang, N. E., M. C. Wu, S. R. Long, S. S. P. Shen, W. Qu, P. Gloersen and K. L. Fan, 2003: “A Confidence Limit for the Empirical Mode Decomposition and Hilbert Spectrum Analysis”, Proc. R. Soc. Lond. A, 59, 2317- 2345.
Kidder, S. Q., J. A. Knaff, S. J. Kusselson, M. Turk, R. R. Ferraro , and R. J. Kuligowski, 2005: The tropical rainfall potential (TRaP) technique. Part I: Description and examples. Wea. Forecasting, 20, 456-464.
Knaff, J. A., C. R. Sampson, and M. DeMaria, 2005: An operational Statistical Typhoon Intensity Prediction Scheme for the western North Pacific. Wea. Forecasting, 20, 688–699.
Kubota ,T., T. Ushio, S. Shige, S. Kida, M. Kachi, and K. Okamoto, 2009: Verification of high resolution satellite-based rainfall estimates around Japan using gauge-calibrated ground radar dataset. J. Meteor. Soc. Japan, 87A, 203-222.
Liu, G. R., C. C. Chao, and C. Y. Ho, 2008: Applying satellite-estimated storm rotation speed to improve typhoon rainfall potential technique. Wea. Forecasting, 23, 259-269
Liu, C.C., 2009: The influence of terrain on the tropical rainfall potential technique in Taiwan. Wea. Forecasting, 24, 785-799.
Liu, C.C., G.R. Liu, T.H. Lin, and C.C. Chao, 2010：Accumulated Rainfall Forecast of Typhoon Morakot(2009) in Taiwan Using Satellite Data. J. Meteor . Soc. of Japan, 88, 785-798.
Pandey, G. R., D. R. Cayan, K. P. Georgakakos, 1999: Precipitation structure in the Sierra Nevada of California during winter. J. Geophys. Res. 104, 12019–12030.
Shige, S. et al , 2009: The GSMaP precipitation retrieval algorithm for microwave sounders. Part I: Over-ocean algorithm. IEEE Trans. Geosci. Remote Sens, 47, 3084-3097.
Smith, R. B.,1979: The influence of Mountains on atmosphere. Adv. Geophys, 21, 87-230.
Ushio, T et al, 2009: A Kalman Filter Approach to Global Satellite Mapping of Precipitation (GSMaP) from combined Passive Microwave and Infrared Radiometric Data, J. Meteor. Soc. Japen, 87A,137-151.
Wilson, J., and M. Atwater, 1972: Storm rainfall variability over connecticut. J Geophys. Res, 77, 3950-3956.
Yan, B. and Weng, 2009: Assessment of F16 special sensor microwave imager and sounder antenna temperature at lower atmospheric sounding cahaanels. Advances in Meteorology.

指導教授

劉振榮

審核日期

2013-8-23

推文