English  |  正體中文  |  简体中文  |  Items with full text/Total items : 49378/84106 (59%)
Visitors : 7366718      Online Users : 65
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/111498


    Title: 利用SHIPS資料改進颱風強度統計預報模式之研究
    Other Titles: Improvement of the statistical typhoon intensity prediction model by using the SHIPS developmental data
    Authors: 林碩彥;Lin, Shuo-Yan
    Contributors: 淡江大學水資源及環境工程學系碩士班
    蔡孝忠;Tsai, Hsiao-Chung
    Keywords: 颱風強度預報;SHIPS;RI;統計迴歸模式;Statistical Hurricane Intensity Prediction Scheme Develoepment Data;Rapid Intensification;Typhoon Intensity Forecast;Statistics Regression Model
    Date: 2016
    Issue Date: 2017-08-24 23:54:24 (UTC+8)
    Abstract: 本研究嘗試改進颱風強度預報技術,採用Tsai and Elsberry(2014)颱風強度統計預報模式(Weighted Analog Intensity Prediction;WAIP),配合SHIPS資料(Statistical Hurricane Intensity Prediction Scheme Development Data)之垂直風切、颱風可能最大強度(Maximum Potential Intensity;MPI)、海表面溫度(Sea Surface Temperature)、海洋熱容量(Ocean Heat Content)等大氣及海洋環境因子,建立五天颱風強度預報迴歸模式,探討WAIP加入SHIPS預報因子之改進程度,模式簡稱為WAIPs,此外,本研究亦測試了僅使用SHIPS資料建立統計預報模式之預報技術,將模式命名為SHIPSa,並分析SHIPSa模式在去除海洋熱容量相關預報因子後之預報技術,模式簡稱為SHIPSb。
    本研究以2000~2007年之SHIPS資料為模式訓練組,透過逐步迴歸(Stepwise Regression),選取重要性較顯著之預報因子。分析結果顯示,垂直風切在12~60小時之迴歸變數選入次序較為優先,但72~120小時之重要性逐漸降低。海洋熱容量則是在所有預報時段皆被納入迴歸模式,在逐步挑選變數時亦佔有一定的重要性。
    本研究另以2008~2012獨立個案資料之進行預報校驗測試。相較於原始WAIP模式而言,在加入SHIPS預報因子後,WAIPs在各預報時段皆較WAIP有較優之表現。以60小時~120小時為例,校驗資料R2值之預報改進百分比約19%~39%。相較於SHIPSa,WAIPs在60小時~120小時之校驗資料R2值改進百分比可達16%~30%;相較於SHIPSb,WAIPs於60~120小時預報可改進30%~48%。以RMSE(Root Mean Square Error)而言,WAIPs於各預報時段可改進WAIP、SHIPSa及SHIPSb最高可達11%、12.5%及15%。
    本研究亦特別分析RI (Rapid Intensification)個案之改進程度。校驗結果顯示,WAIPs之R2值在72~120小時預報改進最為顯著,相較於其餘模式,R2增幅皆可達到0.1以上。以108小時預報為例,WAIPs相較於WAIP、SHIPSa及SHIPSb之R2增幅分別為0.21、0.14、0.07。以MAE(Mean Absolute Error)而言,WAIPs約可改進WAIP 3 kt~7 kt,WAIPs於各預報時段之MAE較SHIPSa減少1 kt~3.5 kt,亦較SHIPSb減少1 kt~5 kt。RI個案之校驗RMSE亦有改進,WAIPs可改進WAIP約2 kt~7 kt,WAIPs於各時段預報之RMSE可比SHIPSa減少約1 kt~4 kt, WAIPs於各時段預報之RMSE亦可比SHIPSb減少2 kt~11 kt。
    WAIPs於各個地理位置之改進結果顯示,東經150o~160o、北緯10o~20o的範圍在24小時預報MAE可減少5.8 kt,且RMSE可減少5.5 kt。在72小時預報部分,東經140o~150o、北緯10o~20o範圍之MAE及RMSE可分別減少10 kt及10.5 kt,於台灣附近海域亦可減少2.9 kt及3.4 kt。在120小時預報的部分,改進較顯著區域為日本附近之海域,MAE及RMSE可分別減少3.3 kt及4.5 kt,台灣附近區域亦有2.2 kt及4.4 kt的改進。
    針對RI個案區域改進而言,研究結果顯示,WAIPs在各個區域的改進程度相當顯著。24小時預報改進最為顯著的區域台灣附近之區域(東經120o~130o、北緯20o ~30o),MAE和RMSE可分別減少9.5 kt及9.9 kt,此外,東經150o ~160o、北緯10o ~20o的範圍,MAE和RMSE亦可分別減少8.2 kt及7.5 kt。以72小時預報而言,改進最為顯著的區域為東經120 o ~130 o、北緯30 o~40 o之範圍,MAE及RMSE分別可減少14.4 kt及13.6 kt。120小時之預報改進校驗顯示,台灣附近海域之改進效果較為顯著,MAE及RMSE可分別減少8 kt及10 kt,菲律賓西側海域之MAE及RMSE亦可減少7.2 kt及7.8 kt。
    The purpose of this study is to improve the typhoon intensity forecast skill. A statistical five-day typhoon intensity prediction model called WAIPs is developed by adapting the Weighted Analog Intensity Predict model (WAIP; Tsai and Elsberry, 2014), and the environmental factors (e.g., vertical wind shear, maximum potential intensity, sea surface temperature, ocean heat content, etc.) obtained from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) developmental data. The improvement of the forecast skill over the original WAIP model is investigated if the SHIPS predictors are included. In addition, a model that only uses the SHIPS predictors (named SHIPSa), and the SHIPSa without using the ocean heat content predictors (named SHIPSb) are also investigated.
    In this study, the SHIPS data from 2000 to 2007 are used as the training samples, and the predictors are selected by using the stepwise regression method. The analysis results show that the vertical wind shear should be used from 12 to 120 hours, but the importance decreases after 72 hours. Also, the ocean heat content is used in all forecast periods as revealed by the stepwise regression.
    The SHIPS data from 2008 to 2012 are used as the independent testing samples. As compared to the original WAIP model, the forecast skill is improved at every forecast period if the SHIPS predictors are included. For example, the R2 values are increased by 19-39% from 60 to 120 h. The WAIPs can also outperform the SHIPSa and SHIPSb by 16-30% and 30-48%, respectively. The RMSE of the WAIPs is also smaller than that of the WAIP, SHIPSa, and SHIPSb by about 11%, 12.5%, and 15%, respectively.
    The cases that undergo RI (rapid intensification) are also investigated. It is shown that the skills are improved during the 72-120 h forecast periods. For example, the R2 improvement of the WAIPs over the WAIP, SHIPSa, and SHIPSb are 0.21, 0.14, and 0.07, respectively. As for the MAE, the WAIPs is 3-7 kt smaller than the WAIP. The RMSE of the WAIPS is also 1-4 kt and 2-11 kt smaller than that of the SHIPSa and the SHIPSb.
    The forecast skill is also evaluated according to the geographical distributions. At 24 h, the MAE and RMSE over the area ranging from 150-160o E and 10-20o N can be reduced by 5.8 kt and 5.5 kt, respectively. At 72 h, the MAE and RMSE over the area ranging from 140-150o E and 10-20o N can be reduced by 10 kt and 10.5 kt. For the Taiwan area, the MAE and the RMSE can be reduced by about 2.9 kt and 3.4 kt, respectively. At 120 h, the forecast improvement over the area near Japan is significant, and the MAE and RMSE can be reduced by about 3.3 kt and 4.5 kt. The MAE and RMSE near Taiwan area can also be reduced by about 2.2 kt and 4.4 kt, respectively.
    The improvement of the RI cases is quite significant, especially the area near Taiwan. The MAE and RMSE over the area ranging from 150-160o E and 10-20o N are reduced by 8.2 kt and 7.5 kt, respectively. At 72 h, the area ranging from 120-130o E and 30-40o N has the most significant improvement. The MAE and the RMSE can be reduced by 14.4 kt and 13.6 kt. At 120 h, the most significant area is the region near Taiwan. The MAE and the RMSE can be reduced by 8 kt and 10 kt. Also, the MAE and RMSE for the area over the west of the Philippines are reduced by 7.2 kt and 7.8 kt, respectively.
    Appears in Collections:[水資源及環境工程學系暨研究所] 學位論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML17View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback