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姓名 張逸品(Yi-Pin Chang)  查詢紙本館藏   畢業系所 大氣科學學系
論文名稱 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
(Vortex initialization through the high-resolution ensemble Kalman filter framework and its impact on intensity forecast: a case study of Typhoon Megi (2010))
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摘要(中) 在颱風預報研究之課題中,強度預報一直都具有高度挑戰性,尤其快速增強(Rapid Intensification, RI)更是強度預報的一大挑戰。許多研究提出渦旋初始化方法以改善颱風初始場與強度預報,其中系集卡爾曼濾波器資料同化方法能透過流場相依的動力誤差特性結合短期模式預報(背景場)與觀測資料,提供更真實的初始場且進一步改善颱風強度預報。
  本篇研究在WRF模式與渦旋中心-局地系集轉換卡爾曼濾波器(TC Centered-Local Ensemble Transform Kalman Filter, TCC-LETKF)同化架構下,探討同化颱風內核觀測對於颱風梅姬(2010)強度預報的影響。本研究中使用之颱風內核觀測包含飛機偵查所投擲的GPS投落送(dropsonde)資料(Impact of Typhoons on the Ocean in Pacific field campaign, D’Asaro et al. 2014)以及Wu et al. (2010)所提出的海表面軸對稱風速結構。資料同化起始時間為0000 UTC 14 October 2010,同化至0000 UTC 15 October 2010和0000 UTC 16 October 2010後進行預報。實驗結果顯示,越早起始預報,強度預報結果越好,且同化投落送後,模式颱風較能掌握快速增強的趨勢;而同化海表面軸對稱風速結構雖能有效使同化期間的颱風與作業單位發布之颱風強度資訊接近,但預報後颱風增強較慢;同時同化兩種內核觀測時,如果資料量相當,不僅能改善分析場颱風強度與結構真實性,對於強度預報也有正面的影響。總結來說,海表面軸對稱風速結構能增強颱風平均風場,但對於修正熱力結構則須更為謹慎;投落送資料能在颱風分析場帶來不對稱量與溫濕度場垂直結構的資訊,因此不僅分析場颱風與真實颱風結構較接近,對於預報期間快速增強過程與積雲對流爆發(convective burst)發展也有所助益。
摘要(英) One of the most challenging issues among tropical cyclone (TC) forecasting is the intensity prediction, especially for rapid intensification (RI) cases. Vortex initialization based on ensemble data assimilation has the advantage of dynamical consistency and makes good use of observations, potentially giving more realistic initial conditions and better intensity forecasts.
In this study, initialization strategies based on EDA are applied to the case of Typhoon Megi (2010) under the framework of Weather Research and Forecasting model-TC Centered Local Ensemble Transform Kalman Filter (WRF-TCCLETKF) to explore the impact of assimilating inner-core observations, including dropsondes (DP) and the axisymmetric surface wind structure (VT). The dropsonde data were collected from the Impact of Typhoons on the Ocean in Pacific (ITOP) field campaign, and the assimilation of the axisymmetric surface wind structure follows the methodology proposed by Wu et al. (2010). The initial time of assimilation was 0000 UTC 14 October 2010, and the forecasts were carried out on 15 and 16 October 2010. Results show that the earlier forecast initial time gives better intensity prediction. The TC intensity in the VT analysis is close to the observation; however, the TC slowly intensifies with less convective bursts (CBs) during the forecast period. By contrast, although the TC intensity in the DP analysis is weaker than the observation during DA period, the TC rapidly intensifies with broader CB areas during the forecast period. Assimilating both types of observation shows positive impacts on not only the TC intensity but also the TC structure. In conclusion, assimilating the axisymmetric surface wind structure can enhance the mean wind structure, and assimilating dropsondes better represents the TC structure, leading to the process of RI.
關鍵字(中) ★ 資料同化
★ 系集卡爾曼濾波器
★ 颱風強度預報
關鍵字(英) ★ data assimilation
★ EnKF
★ TC intensity prediction
論文目次 摘要 i
Abstract ii
Acknowledgement iii
Table of Contents iv
List of Tables vi
List of Figures vi
1. Introduction 1
1.1 Background: data assimilation for improving TC forecast 1
1.2 Motivation 3
1.3 Typhoon Megi (2010) 3
2. Methodology 5
2.1 Weather Research and Forecasting (WRF) model 5
2.2 WRF-TCCLETKF system 5
2.2.1 From Kalman filter (KF), extended Kalman filter (EKF), to ensemble Kalman filter (EnKF) 6
2.2.2 Local ensemble transform Kalman filter (LETKF) 8
2.2.3 TC-centered (TCC) method 9
2.3 Inner-core observations 10
2.3.1 Dropsondes 10
2.3.2 The axisymmetric surface wind structure 11
3. Experimental settings and design 14
3.1 Separate assimilation experiments 14
3.2 Combined assimilation experiments 15
3.3 Higher horizontal resolution experiments 15
4. Results 16
4.1 Overview of separate assimilation experiments 16
4.2 Assimilation results of separate assimilation experiments 17
4.2.1 The verification of wind speed with SFMR data 17
4.2.2 The surface wind structure 18
4.2.3 The vertical wind structure 20
4.2.4 The verification of Vt, Vr, RH, and T in dropsonde space 20
4.2.5 The thermal structure 21
4.3 Forecast results of separate assimilation experiments 23
4.3.1 The large-scale condition 23
4.3.2 The wind structure 24
4.3.3 The warm core, convective bursts (CBs), and CAPE 25
4.4 Results of combined assimilation experiments 26
4.5 Results of higher horizontal resolution experiments 27
5. Conclusions and future work 28
5.1 Conclusions 28
5.2 Future work 30
References 31
Appendix 36
Tables 38
Figures 43
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指導教授 楊舒芝(Shu-Chih Yang) 審核日期 2018-1-30
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