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  1. NTU Theses and Dissertations Repository
  2. 公共衛生學院
  3. 流行病學與預防醫學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91899
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dc.contributor.advisor蕭朱杏zh_TW
dc.contributor.advisorChuhsing Kate Hsiaoen
dc.contributor.author李宜芸zh_TW
dc.contributor.authorYi-Yun Leeen
dc.date.accessioned2024-02-26T16:21:24Z-
dc.date.available2024-02-27-
dc.date.copyright2024-02-26-
dc.date.issued2023-
dc.date.submitted2023-11-11-
dc.identifier.citationChang, H.-L., Yang, S.-C., Yuan, H., Lin, P.-L., & Liou, Y.-C. (2015). Analysis of the relative operating characteristic and economic value using the laps ensemble prediction system in Taiwan. Monthly Weather Review, 143(5), 1833–1848. https://doi.org/10.1175/mwr-d-14-00189.1
Chen, C.-J., Lee, T.-Y., Chang, C.-M., & Lee, J.-Y. (2018). Assessing typhoon damages to Taiwan in the recent decade: Return period analysis and loss prediction. Natural Hazards, 91(2), 759–783. https://doi.org/10.1007/s11069-017-3159-x
Dabernig, M., Mayr, G. J., Messner, J. W., & Zeileis, A. (2017). Spatial ensemble post‐processing with standardized anomalies. Quarterly Journal of the Royal Meteorological Society, 143(703), 909–916. https://doi.org/10.1002/qj.2975
Di Narzo, A. F., & Cocchi, D. (2010). A Bayesian hierarchical approach to ensemble weather forecasting. Journal of the Royal Statistical Society Series C: Applied Statistics, 59(3), 405–422. https://doi.org/10.1111/j.1467-9876.2009.00700.x
Eide, S. S., Bremnes, J. B., & Steinsland, I. (2017). Bayesian model averaging for wind speed ensemble forecasts using wind speed and direction. Weather and Forecasting, 32(6), 2217–2227. https://doi.org/10.1175/waf-d-17-0091.1
Fraley, C., Gneiting, T., Sloughter, J. M., & Raftery, Adrian E. (2007). ensembleBMA: An R package for probabilistic forecasting using ensembles and Bayesian model averaging. | University of Washington Department of Statistics. https://stat.uw.edu/research/tech-reports/ensemblebma-r-package-probabilistic-forecasting-using-ensembles-and-bayesian-model-averaging
Fraley, C., Raftery, A. E., & Gneiting, T. (2010). Calibrating multimodel forecast ensembles with exchangeable and missing members using Bayesian model averaging. Monthly Weather Review, 138(1), 190–202. https://doi.org/10.1175/2009mwr3046.1
Gneiting, T., Raftery, A. E., Westveld, A. H., & Goldman, T. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review, 133(5), 1098–1118. https://doi.org/10.1175/mwr2904.1
Grönquist, P., Yao, C., Ben-Nun, T., Dryden, N., Dueben, P., Li, S., & Hoefler, T. (2021). Deep learning for post-processing ensemble weather forecasts. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2194), 20200092. https://doi.org/10.1098/rsta.2020.0092
Hamill, T. M. (1997). Reliability diagrams for Multicategory probabilistic forecasts. Weather and Forecasting, 12(4), 736–741. https://doi.org/10.1175/1520-0434(1997)012<0736:rdfmpf>2.0.co;2
Hersbach, H. (2000). Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting, 15(5), 559–570. https://doi.org/10.1175/1520-0434(2000)015<0559:dotcrp>2.0.co;2
Hsiao, L.-F., Yang, M.-J., Lee, C.-S., Kuo, H.-C., Shih, D.-S., Tsai, C.-C., Wang, C.-J., Chang, L.-Y., Chen, D. Y.-C., Feng, L., Hong, J.-S., Fong, C.-T., Chen, D.-S., Yeh, T.-C., Huang, C.-Y., Guo, W.-D., & Lin, G.-F. (2013). Ensemble forecasting of typhoon rainfall and floods over a mountainous watershed in Taiwan. Journal of Hydrology, 506, 55–68. https://doi.org/10.1016/j.jhydrol.2013.08.046
Hsu, W., & Murphy, A. H. (1986). The attributes diagram a geometrical framework for assessing the quality of probability forecasts. International Journal of Forecasting, 2(3), 285–293. https://doi.org/10.1016/0169-2070(86)90048-8
Huang, Y.-J., Lee, Y.-Y., Chang, H.-L., Wang, C., Hong, J.-S., Hsiao, & C. K., (2023). Bayesian typhoon precipitation prediction with a mixture of ensemble forecast-based and historical event-based prediction functions. Submitted.
Javanshiri, Z., Fathi, M., & Mohammadi, S. A. (2021). Comparison of the bma and emos statistical methods for probabilistic quantitative precipitation forecasting. Meteorological Applications, 28(1). https://doi.org/10.1002/met.1974
Jian, G.-J., Teng, J.-H., Wang, S.-T., Cheng, M.-D., Cheng, C.-P., Chen, J.-H., & Chu, Y.-J. (2022). An Overview of the Tropical Cyclone Database at the Central Weather Bureau of Taiwan. https://doi.org/10.21203/rs.3.rs-1671522/v1
Kim, H.-S., Kim, J.-H., Ho, C.-H., & Chu, P.-S. (2011). Pattern classification of typhoon tracks using the fuzzy C-means Clustering Method. Journal of Climate, 24(2), 488–508. https://doi.org/10.1175/2010jcli3751.1
Li, C.-H., Berner, J., Hong, J.-S., Fong, C.-T., & Kuo, Y.-H. (2019). The Taiwan WRF Ensemble Prediction System: Scientific Description, model-error representation and performance results. Asia-Pacific Journal of Atmospheric Sciences, 56(1), 1–15. https://doi.org/10.1007/s13143-019-00127-8
Liu, J., & Xie, Z. (2014). BMA probabilistic quantitative precipitation forecasting over the Huaihe Basin using Tigge Multimodel Ensemble forecasts. Monthly Weather Review, 142(4), 1542–1555. https://doi.org/10.1175/mwr-d-13-00031.1
Marty, R., Fortin, V., Kuswanto, H., Favre, A.-C., & Parent, E. (2014). Combining the bayesian processor of output with Bayesian model averaging for reliable ensemble forecasting. Journal of the Royal Statistical Society Series C: Applied Statistics, 64(1), 75–92. https://doi.org/10.1111/rssc.12062
Messner, J. W., Mayr, G. J., & Zeileis, A. (2016). Nonhomogeneous boosting for predictor selection in ensemble postprocessing. Monthly Weather Review, 145(1), 137–147. https://doi.org/10.1175/mwr-d-16-0088.1
Raftery, A. E., Gneiting, T., Balabdaoui, F., & Polakowski, M. (2005). Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 133(5), 1155–1174. https://doi.org/10.1175/mwr2906.1
Rasp, S., & Lerch, S. (2018). Neural networks for postprocessing ensemble weather forecasts. Monthly Weather Review, 146(11), 3885–3900. https://doi.org/10.1175/mwr-d-18-0187.1
Salvatier, J., Wiecki, T. V., & Fonnesbeck, C. (2016). Probabilistic programming in Python using Pymc3. PeerJ Computer Science, 2. https://doi.org/10.7717/peerj-cs.55
Scheuerer, M. (2013). Probabilistic quantitative precipitation forecasting using ensemble model output statistics. Quarterly Journal of the Royal Meteorological Society, 140(680), 1086–1096. https://doi.org/10.1002/qj.2183
Scheuerer, M., & Hamill, T. M. (2015). Statistical postprocessing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Monthly Weather Review, 143(11), 4578–4596. https://doi.org/10.1175/mwr-d-15-0061.1
Sloughter, J. M. L., Raftery, A. E., Gneiting, T., & Fraley, C. (2007). Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Monthly Weather Review, 135(9), 3209–3220. https://doi.org/10.1175/mwr3441.1
Sloughter, J. M. L., Gneiting, T., & Raftery, A. E. (2010). Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. Journal of the American Statistical Association, 105(489), 25–35. https://doi.org/10.1198/jasa.2009.ap08615
Stauffer, R., Umlauf, N., Messner, J. W., Mayr, G. J., & Zeileis, A. (2017). Ensemble postprocessing of daily precipitation sums over complex terrain using censored high-resolution standardized anomalies. Monthly Weather Review, 145(3), 955–969. https://doi.org/10.1175/mwr-d-16-0260.1
Sturtz, S., Ligges, U., & Gelman, A. (2005). R2WinBUGS: A package for running WinBUGS from R. Journal of Statistical Software, 12(3). https://doi.org/10.18637/jss.v012.i03
Su, S.-H., Kuo, H.-C., Hsu, L.-H., & Yang, Y.-T. (2012). Temporal and spatial characteristics of typhoon extreme rainfall in Taiwan. Journal of the Meteorological Society of Japan. Ser. II, 90(5), 721–736. https://doi.org/10.2151/jmsj.2012-510
Taillardat, M., Mestre, O., Zamo, M., & Naveau, P. (2016). Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics. Monthly Weather Review, 144(6), 2375–2393. https://doi.org/10.1175/mwr-d-15-0260.1
中華民國交通部中央氣象署(2023)。颱風百問。上網日期:2023年10月01日。檢自: https://www.cwa.gov.tw/V8/C/K/Encyclopedia/typhoon/index.html
蔡甫甸、中華民國交通部中央氣象局氣象預報中心(2015)。民國104年北太平洋西部颱風概述。颱風調查報告。上網日期:2023年10月01日。檢自: https://photino.cwa.gov.tw/rdcweb/lib/cd/cd02tyrp/typ/2015/y104_annual_final.pdf
張惠玲、陳冠儒、吳佳蓉、汪琮、洪景山、楊舒芝(2018)。臺灣地區WRF颱風系集降雨機率預報之評估、校正與經濟價值分析-第一部分:預報評估。大氣科學,46(1),69-104。https://doi.org/10.3966/025400022018034601003
李志昕、洪景山(2014)。區域系集預報系統研究:系集成員產生方式之評估。大氣科學,42(2),153-179。https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=02540002-201409-201410240016-201410240016-153-179
許聖章、張靜貞(2011)。台灣颱風災害之影響評估-以蔬菜供需爲例。應用經濟論叢,(89),31-62。https://doi.org/10.7086/TJAE.201106.0031
蘇元風、陳偉柏、傅鏸漩、張駿暉、張志新(2016)。降雨預報於淹水災害管理之應用──以蘇迪勒颱風為例。災害防救科技與管理學刊,5(2),1-17。https://doi.org/10.6149/JDM.2016.0502.01
王俊明、李心平、李鎮鍵、臧運忠、謝正倫(2010)。莫拉克颱風災害綜覽。中華防災學刊,2(1),27-34。https://doi.org/10.30052/JTDPS.201002.0004
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91899-
dc.description.abstract颱風是台灣常見的天然災害,其挾帶極端降雨,常常對台灣造成嚴重的損害。如果沒有適當的預防措施,突如其來的豪大雨很容易導致作物損失、洪水、交通中斷,甚至引發山崩,威脅到人們的生命安全。因此,在氣象學和政策決策中,颱風的定量降雨預報至關重要。
目前的降雨預報大多是基於系集預報系統(ensemble prediction system, EPS)輸出的預報成員去進行預測,然而這種預測系統存在著系統性偏差。在以往的研究中,常會使用系集模式輸出統計(ensemble model output statistics, EMOS)與貝氏模型平均(Bayesian model averaging, BMA)進行統計後處理,這兩種方法都能同時構建校正模型並提供機率預報。但基於颱風資料的特殊性,其提供的訓練期間較短並且含有極端的雨量觀測值,兩種方法都不適合直接應用於颱風期間的降雨預測。
本研究採用中央氣象局以WRF區域模式為基礎的系集預報資料,並針對颱風發生的事件,建構混合的預測模型,結合了系集預報成員與歷史颱風記錄的資訊,透過貝氏統計進行後處理。其中,基於系集預報成員的預測分佈,可以在系集預報產生的同時建構,可解決短期數據的問題,而加入歷史颱風記錄,則能額外提供地區相關的訊息,協助預測強降雨的發生。
本研究在CRPS和可信度圖等氣象評估指標中,皆可看出良好的校正成效。此外,校正模型的計算量不致太過繁重,展現了該模型在未來的颱風事件中能被應用的潛力。
zh_TW
dc.description.abstractTyphoon is a natural disaster that often brings extreme rainfall and causes severe damage to Taiwan. Without appropriate prevention measures, sudden rainfall can easily cause crop losses, flooding, transportation disruption, and even lead to landslides endangering people's safety. Therefore, a real-time and reliable probability prediction of typhoon precipitation has been essential in meteorology and policy decisions.
Current precipitation forecasts are mainly based on the Ensemble Prediction Systems Outputs; however, this prediction system has a systematic bias. In previous studies, two types of post-processing methods, the ensemble model output statistics (EMOS) and Bayesian model averaging (BMA), have been commonly used to construct models and provide probabilistic forecasts at the same time. However, due to the short-termed training period and extreme-value observations, both methods are unsuitable to be directly applied to precipitation prediction during the attack of typhoons. In this research, we construct a mixture model using a fully Bayesian approach that combines ensemble member forecasts with important information from historical records of typhoons. Each member-based component distribution can be trained with the real-time member forecast; it is designed to address the issue of limited short-term data, while the historical data provides additional information for handling heavy rain events.
Our model has demonstrated better predictive capabilities across various meteorological evaluation metrics, including Continuous ranked probability score (CRPS) and reliability diagram. Furthermore, the computational load is not heavy, suggesting promising application of this model to future events.
en
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dc.description.tableofcontents口試委員會審定書 i
致謝 ii
摘要 iii
Abstract iv
目次 v
圖次 vi
表次 vii
第一章 背景與動機 1
1.1颱風背景、動機 1
1.2降雨預報 2
1.3應用於降雨量預報的統計後處理方式 3
第二章 研究方法 7
2.1 Bmix第一部分: 基於系集預報成員的貝氏統計預測函數 7
2.2 Bmix第二部分: 基於歷史颱風資訊建構的貝氏統計預測函數 9
2.3 以Python語言執行Bmix後處理 10
第三章 個案分析:杜鵑颱風 11
3.1預報與觀測資料 11
3.2颱風個案資料 12
3.3分類混和貝氏統計C.Bmix 14
3.4應用結果 16
3.4.1傳統BMA於有限資料下的應用設定 16
3.4.2結果 18
3.4.2-1 Continuous Ranked Probability Skill Score(CRPSS) 18
3.4.2-2 機率預報圖 21
3.4.2-3 可信度圖Reliability diagram 21
第四章 結論與討論 23
4.1探討山脈西側區域的預報能力以及其他CRPSS較小的格點 23
4.2先驗分佈設定 25
4.3先驗分佈中過往颱風報數的選擇方式 25
4.4混和模型Bmix中權重 λ 的設定 26
4.5使用多個EPS來源的後處理應用 26
參考文獻 28
附錄 34
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dc.language.isozh_TW-
dc.title使用貝氏統計即時後處理颱風降雨量預測zh_TW
dc.titleA Bayesian mixture approach to real-time typhoon precipitation forecasten
dc.typeThesis-
dc.date.schoolyear112-1-
dc.description.degree碩士-
dc.contributor.oralexamcommittee張惠玲;王彥雯;洪景山zh_TW
dc.contributor.oralexamcommitteeHui-Ling Chang;Charlotte Wang;Jing-Shan Hongen
dc.subject.keyword統計後處理,系集預報,貝氏統計,定量降雨機率預報,颱風雨量,zh_TW
dc.subject.keywordstatistical post-processing,ensemble forecast,Bayesian statistics,probabilistic quantitative precipitation forecast,typhoon precipitation,en
dc.relation.page56-
dc.identifier.doi10.6342/NTU202304411-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2023-11-14-
dc.contributor.author-college公共衛生學院-
dc.contributor.author-dept流行病學與預防醫學研究所-
dc.date.embargo-lift2026-11-11-
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