博碩士論文 996201009 詳細資訊

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姓名 林冠任(Kuan-Jen Lin)  查詢紙本館藏   畢業系所 大氣物理研究所
論文名稱 改善區域系集卡爾曼濾波器在颱風同化及預報中的spin-up問題-2008年颱風辛樂克個案研究
(Improving the spin-up of the regional EnKF for typhoon assimilation and prediction with the 2008 typhoon Sinlaku)
★ 利用WRF-LETKF同化系統探討掩星折射率觀測對於強降水事件預報之影響★ LETKF加速就位法於颱風同化預報之應用
★ 利用系集重新定位法改善颱風路徑預報-2011年南瑪都颱風個案研究★ 利用局地系集轉換卡爾曼濾波器雷達資料同化系統改善定量降水即時預報:莫拉克颱風(2009)
★ 利用系集資料同化系統估算區域大氣化學耦合模式中trace物種之排放與吸收:以CO2為例★ OSSE實驗架構下利用系集預報敏感度工具探討觀測對於颱風路徑預報及結構之影響
★ 利用局地系集轉換卡爾曼濾波器雷達資料同化系統改善短期定量降雨預報: SoWMEX IOP8 個案分析★ 利用系集重新定位法改善對流尺度定量降水即時預報:2009年莫拉克颱風個案研究
★ LAPS 短時(0-6小時)系集降水機率預報之評估與應用★ 利用辛樂克颱風(2008)建立的觀測系統模擬實驗評估系集奇異向量在颱風系集預報之應用
★ 雷達資料同化於多重尺度天氣系統(梅雨)的強降雨預報影響:SoWMEX IOP#8 個案研究★ 基於高解析度系集卡爾曼濾波器之渦旋初始化及其對於颱風強度預報之影響:2010年梅姬颱風個案研究
★ 系集轉換卡爾曼漸進式平滑器在資料同化之應用★ 不同微物理方案在雲可解析模式的系集預報分析: SoWMEX-IOP8 個案
★ 利用正交向量改善系集卡爾曼濾波器之系集空間及其對同化與預報之影響★ 系集資料同化系統與高解析度海氣耦合模式於 颱風預報之應用
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摘要(中) 系集卡爾曼濾波器(EnKF)的特性是利用系集之間發展的差異來代表與背景場動力相依(flow-dependent)的誤差統計特性,而其優勢的前提為系集對於誤差特性能有一定的代表程度。但在區域EnKF系統中因多半使用冷起始,因而需要數個同化循環後方能得到較可靠的誤差統計特性並有效的利用觀測資訊產生好的分析場。此也造成EnKF在劇烈天氣系統的模擬當中需要一段spin-up的時間來達到其分析同化之應有表現。即便有觀測資料,這2~3天的spin-up時間卻嚴重限制了模式能及早模擬及預測颱風發展的能力
本研究在WRF模式搭配局地化系集轉換卡爾曼濾波器(LETKF)資料同化系統的架構下使用由Kalnay and Yang(2010)所提出的”Running In Place(RIP)”方法來加速EnKF的spin-up,希望在較早的時間就能正確的預報颱風的路徑。在本研究中是首次針對2008年的颱風辛樂克進行真實個案的模擬。
摘要(英) The characteristic of Ensemble Kalman Filter (EnKF) is to use a set of ensemble to represent the flow-dependent dynamical error statistic. However, the prerequisite for optimizing the performance of EnKF is that the ensemble perturbations are representative to the error characteristic. However, for the regional EnKF, the cold starting procedure is normally used to initialize the ensemble members. In these cases, a spin-up period is required to construct a reliable background error covariance. After the spin-up, the observation can be correctly used and provide effective analysis corrections and the regional EnKF can then achieve its asymptotic level of performance. For typhoon assimilation and prediction, such issue of the EnKF’s spin-up becomes a serious concern in Taiwan since this limits our ability to predict the typhoon movement during the early stage of typhoon.
In this study, the ‘Running In Place (RIP)’ method proposed by Kalnay and Yang (2010) is implemented in the framework of the Local Ensemble Transform Kalman Filter (LETKF) coupled with the Weather Research and Forecasting (WRF) model. The RIP method is used to accelerate the spin-up of EnKF, in order to make reliable typhoon track prediction at earlier time. The work with the 2008 Typhoon Sinlaku is the first real case study using the RIP method.
Result shows that, the RIP method is beneficial for improving the environmental condition for the typhoon so that the size of the typhoon can be better estimated in the analysis fields. However, less impact on the inner structure of the typhoon and there is no significant improvement for the center position and intensity. For forecast verification, the track prediction has been significantly improved with RIP after 24-hr forecast hours. The improvement is especially significant during the rapid intensifying period of the Typhoon. It is because that with the RIP method, the limited observations can be used more effectively. We suggest that after the background error covariance has been spun-up and can reasonably extract the observation information, RIP method should be turned off and the assimilation should switch back to the standard LETKF cycle to avoid the overfitting.
關鍵字(中) ★ 颱風預報
★ 系集卡爾曼濾波器
★ 資料同化
關鍵字(英) ★ typhoon prediction
★ data assimilation
★ EnKF
論文目次 摘要 i
Abstract: ii
Acknowledgement iv
Table of Contents v
圖目錄: vi
1.Introduction 1
1.1Study review 1
1.2Motivation 3
2.Typhoon Sinlaku(2008-09-09 ~ 2008-09-21) 5
3.Methodology 7
3.1Weather Research and Forecasting(WRF) model 7
3.2Local Ensemble Transform Kalman Filter (LETKF) 7
3.3Running In Place(RIP) method 9
3.4Observation impact estimation method 11
4.Experimental design 14
4.1Setting for the model and assimilation system 14
4.2Ensemble initialization 16
4.4Experiments 17
5.Experiments results 19
5.1Results of the control experiments (LRn,LRy) 19
5.1.1Background error covariance 20
5.1.2Analysis verification 23
5.1.3Forecast performance 24
5.1.4Observation impact at 0906z 26
5.2Results of different cold-starting time 27
5.2.1Impact of different cold-starting time on the standard LETKF 28
5.2.2Impact of different cold-starting time on the RIP method 29
5.3The threshold for the RIP method 30
6.Discussion and conclusion 32
References: 35
Tables and Figures 37
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指導教授 楊舒芝(Shu-Chih Yang) 審核日期 2012-8-24
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