對IBM_VDRAS四維變分資料同化系統的改進以及在探討複雜地形上劇烈降雨過程的應用:北台灣午後對流個案分析

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吳英璋(Ying-Jhang Wu) 查詢紙本館藏

畢業系所

大氣科學學系

論文名稱

對IBM_VDRAS四維變分資料同化系統的改進以及在探討複雜地形上劇烈降雨過程的應用:北台灣午後對流個案分析
(The improvement of a 4DVar data assimilation system (IBM_VDRAS) and its applications in analyzing heavy rainfall processes over complex terrain: A case study in Northern Taiwan)

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摘要(中)

台灣擁有複雜地形使得預報對流初始、發展、增強及傳遞是更具有挑戰性，本篇研究選擇2014年8月19日北台灣夏季午後對流個案，分析方法分為兩部分。第一部分為地面測站資料分析，第二部分使用最新研發的四維變分都卜勒雷達資料同化系統(IBM_VDRAS)，其運用沉浸邊界法(Immersed Boundary Method)，因此具有解析地形的能力，並且能同化雷達及地面觀測資料，產出每17.5分鐘更新的完整三維高時空解析分析場，本研究共產生8個分析場來分析。
在對流產生降雨前，地面測站資料可分析出水平輻合帶由山區往平地移動，溫度場顯示對流蒸發冷卻效應，降雨觀測可看出被地形隔離的兩個降雨帶。從IBM_VDRAS分析場可以看出，此降雨事件主要是由兩個獨立對流胞成長開始，其中一個對流胞的外流邊界與另一個對流胞合併，使得後者增強，並且往台北市移動，產生80mm的觀測雨量。從近地表輻合和相對溼度場顯示，外流邊界的合併提供動力輻合增強，以及平流潮濕的環境有利對流發展。之後進行移除地形及地面資料同化變數的敏感度實驗，探討地形及同化變數對於定量降水預報的影響。結果顯示，雪山山脈在此事件中扮演的角色為阻礙外流邊界向南傳遞，而陽明山及林口台地增加外流邊界移動速度，此外，同化地面風場可修正地面風速偏差量及地表輻合帶強度並改善地量降水預報結果。

摘要(英)

The complex terrain in Taiwan area makes it more challenging to forecast convection initiation, intensification, and propagation. In this research, the heavy rainfall event occurring on 19 August 2014 in northern Taiwan is selected. We use a newly-developed four-dimensional variational Doppler radar assimilation system (IBM_VDRAS), which is capable of simulating the topographic effect by adopting the so-called Immersed Boundary Method, and assimilating radar observations and surface station data. The products of IBM_VDRAS are a series of frequently-updated three-dimensional analysis fields over the complex terrain. In this case study, a total of eight analysis fields times are generated with a temporal interval of 17.5 min over a period of 2.5 h.
From the surface observations and the high temporal/spatial resolution analysis fields generated by IBM_VDRAS, it is found that the rainfall process started with the initiation of individual convective cells. The outflow of one of the convective cells merged with another convective system and helped to intensify the latter. The intensified major convective cell then moved into the Taipei metropolitan area and produced 80 mm of heavy precipitation within 2.5 h. The role played by the topographic forcing on the development of the convective system is investigated. A series of experiments are also designed and conducted by moving out terrain or surface assimilated variables to examine the performance of IBM_VDRAS in short-term rainfall forecasts. The result shows that SMR prevents the outflow from propagating southward, and LKHL and MTYM increase the outflow propagation speed. The surface wind assimilation improves the QPF skill by correcting the wind speed bias and controlling the magnitude of low-level convergence.

關鍵字(中)

★ 資料同化
★ 定量降雨預報
★ 敏感度測試
★ 都卜勒雷達觀測
★ 劇烈降水
★ 伴隨模式

關鍵字(英)

★ data assimilation
★ quantitative precipitation forecast
★ sensitivity test
★ Doppler radar observation
★ heavy rainfall
★ adjoint model

論文目次

Chinese Abstract I
English abstract II
Acknowledgements III
Table of Content IV
List of Tables VII
List of Figures VII
Chapter 1 Introduction 1
1.1 Background of the afternoon thunderstorm 1
1.2 Background of data assimilation 2
1.3 Research goal and outline 5
Chapter 2 Methodology: Variational Doppler Radar Analysis System 6
2.1 Mesoscale background 6
2.2 Cloud-resolving model 7
2.3 Cost function and the adjoint model 10
2.4 Surface data assimilation and Immersed Boundary Method 12
2.5 The improvement of IBM_VDRAS 13
Chapter 3 Real case overview and data processing/preparation 15
3.1 An overview of the event 15
3.2 Synoptic scale analysis 15
3.3 Data processing 16
3.3.1 Radar description and quality control (QC) 16
3.3.2 Surface station description and QC 17
3.4 Treatment of surface station analysis 17
3.5 Domain setting and experiment design 18
3.5.1 Assimilation Strategy 19
3.5.2 TEAM-R validation and station temperature verification 20
Chapter 4 Surface station and IBM_VDRAS analysis 22
4.1 Surface station analyses 22
4.2 IBM_VDRAS analyses 24
4.3 Rainfall structure analysis 26
Chapter 5 Quantitative Precipitation Forecast (QPF) and sensitivity test 28
5.1 IBM_VDRAS QPF 28
5.2 Experimental design for sensitivity test of the topographic effect 28
5.3 Results for sensitivity test of the topographic effect 29
5.4 Experimental design for sensitivity test of surface assimilation 30
5.5 Results for sensitivity test of surface assimilation 31
5.6 Fraction skill score (FSS) of QPFs 32
5.7 The rainfall mechanism illustrated by IBM_VDRAS forecast 33
5.8 Schematic diagram for the heavy rainfall event 33
Chapter 6 Summary and future work 36
6.1 Summary and conclusions 36
6.2 Future work 37
Appendix A The adjoint model and adjoint code 39
A-1 Theoretical derivation of the adjoint model 39
A-2 Practical derivation of the adjoint code 41
A-3 Derivation of the adjoint variables for diagnostic variables 46
A-4 Derivation of Immersed Boundary Method (IBM) adjoint 49
Appendix B Surface station treatment and assimilation 52
B-1 Surface observation variables 52
B-2 Observation variables transformation 52
B-3 Objective surface assimilation 54
Appendix C Immersed boundary method (IBM) 56
C-1 Identify the ghost cell 56
C-2 Finding the image point 56
C-3 Update the values of the ghost cell 57
Appendix D The improvement of IBM_VDRAS 60
D-1 Problems when lateral boundaries intersect with terrain 60
D-2 Update ghost cell value from the horizontal rather than vertical grid points 62
D-3 Modification of surface assimilation system 63
Appendix E Others 65
E-1 Radar QC 65
E-2 Surface temperature terrain-following interpolation 66
References 67
Tables 72
Figures 75

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指導教授

廖宇慶(Liou Yu-Chieng)

審核日期

2019-5-31

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