臺灣博碩士論文加值系統

ABSTRACT
Global Position System (GPS) radio occultation (RO) soundings are assimilated to
explore the impacts of GPS RO data on prediction of severe weather systems such as
super cyclone Gonu (2007) over the northern Indian Ocean and Mei-yu frontal (2012)
rainfall over Taiwan. A series of experiments were conducted using the advanced
Weather Research and Forecasting (WRF) model with three dimensional variational
method (3DVAR) to assimilate GPS RO refractivity from FORMOSAT-3/COSMIC
(hereafter refer as GPS) and other datasets in order to verify the relative impact of GPS
RO soundings.
For the study of cyclone Gonu, we use Special Sensor Microwave Imager (SSM/I)
retrieved precipitable water and near-surface oceanic wind speed, and radiosonde
sounding (GTS), QuikSCAT derived winds, bogus vortex along with GPS RO data.
Significant differences in cyclone track and intensity prediction are exhibited in
various assimilations. GPS-cyc (GPS cyclic) outperformed well when compared to all
other simulations till day 3 with the smallest cross-track error, SSM/I experiment obtains
the strongest cyclone intensity. Sensitivity tests were also conducted to identify which
GPS RO sounding plays a more crucial role in track prediction. It was found that the RO
soundings in the vicinity of Gonu cyclone appear to modify the environmental conditions
that result in a later development of a couplet of high and low pressure leading to an
impact on track prediction, but the sounding closest to the cyclone center exhibits the
largest contribution. Sensitivity experiments indicate that the retrieved GPS data
information at upper levels with initial colder temperature increments indeed contributes
iii
more to improvement on track prediction. The improvement will not degrade until only
the initial RO sounding information above 14 km height is retained in the model.
Severe rainfall associated with a Mei-yu frontal system in Taiwan is studied using
three different observational operators such as local bending angle, local refractivity, and
non-local refractivity. The assimilations of the RO data are found beneficial to the
forecast of severe rainfall associated with Mei-yu frontal system in Taiwan. Distributions
of major rainfall in northern Taiwan on the second day, in particular, best benefit the
performance of the local bending angle operator, followed by local refractivity operator.
Both the operators appear to produce mostly larger positive moisture increments than the
non-local operator. We found that the impact of RO data results from several critical RO
observations near Mongolia and northern China. This study is the first attempt to provide
an inter-comparison among the three RO operators, and show the feasibility of model
assimilation with local bending angle.

摘 要
全球定位系統掩星（GPS RO）觀測資料於本研究中被同化於模式中，進一
步想了解其對災害性天氣系統的影響，如北印度洋的超級氣旋Gonu（2007）和
通過台灣地區的梅雨鋒面（2012）。本研究一系列的實驗使用WRF(Weather
Research and Forecasting )模式和三維變分資料同化方法（3DVAR），並於研究
中同化福爾摩沙衛星三號(FORMOSAT-3COSMIC)GPS RO折射率觀測資料和其
他用來驗證GPS RO的影響的觀測資料。
於颶風Gonu 的研究中， 我們使用SSM/I 衛星觀測資料(Special Sensor
Microwave Imager)、傳統觀測資料（GTS）、QuikSCAT衛星觀測資料和包含GPS
RO折射率觀測資料的虛擬渦漩。於本實驗中，同化不同的觀測資料在氣旋路徑
和強度預報有顯著不同，比較所有實驗路徑誤差，其中GPS-cyc(GPS cyclic)之氣
旋路徑誤差最小，而同化SSM/I衛星觀測資料模擬的氣旋強度最強。在敏感度實
驗中我們發現Gonu氣旋附近的GPS觀測資料修正了其環境條件，進一步造成對路
徑預測的影響，其中最接近氣旋中心的探空資料表現出最大的貢獻。敏感度實驗
中指出，GPS在模式高層初始場較冷的溫度增量對於路徑預測的改善有顯著的影
響，進一步確立同化GPS觀測資料於路徑預報中扮演著重要的角色。
梅雨（2012）鋒面系統數個實驗中，於模式中同化了GPS 掩星觀測資料並
使用三個不同的觀測算子如局地折射率(Local Reflectivity 簡稱REF)、局地偏折
角(Local Bending Angle 簡稱LBA)和非局地折射率(Nonlocal Refractivity 簡稱
EPH)，試圖了解同化不同的同化運算子對於梅雨預報的影響。我們發現同化掩
星資料對於梅雨(2012)個案預報結果皆有助益，其中LBA 實驗模擬第二天降雨
結果最接近觀測，其次為REF 實驗，這兩組實驗的模式初始場水氣增量皆比EPH
實驗大，其中我們發現數個位在蒙古和中國北方的掩星觀測資料扮演關鍵的角色。
在本研究中為國內首次嘗試這三種不同的掩星觀測算子間的比較，並表現模式同
化局地篇折角的可行性。

TABLE OF CONTENTS
Abstract Chinese i
Abstract English ii
Dedication iv
Acknowledgments v
Table of Contents x
List of figures xiii
List of Tables xx
CHAPTER 1 INTRODUCTION 1
1.1 General Introduction 1
1.2 Brief History of Global Position System Radio Occultation Technique 4
1.3 FORMOSAT-3/ COSMIC 5
1.4 Need of the present research 6
1.5 Motivation 8
CHAPTER 2 MODEL AND METHODOLOGY 11
2.1 WRF modeling System 11
2.1.1 WRF Preprocessing System (WPS) 13
2.1.2 WRF-Var or WRFDA 13
2.1.3 WRF Model 13
2.1.4 Post-processing 14
2.2 WRF 3DVAR 14
2.3 The Methodology 16
2.4 Bogus Data Assimilation 19
2.5 The Equitable Threat Score (ETS) 21
CHAPTER 3 DATA AND CASE EXPERIMENTS 22
3.1 NCEP AVN 22
3.2 Global Telecommunication System (GTS) 22
3.3 FORMOSAT-3/COSMIC (GPS) 22
xi
3.4 Special Sensor Microwave Imager (SSM/I) 23
3.5 QuikSCAT 24
3.6 Bogus Vortex 24
3.7 Case Experiments 25
3.7.1 The Super Cyclone Gonu 25
3.7.2 The Mei-yu Front 26
CHAPTER 4 THE SUPER CYCLONE GONU 28
4.1 Description of Experiments 28
4.2 Analyses of Initial Increments 29
4.2.1 Analyses of Moisture and Temperature 29
4.2.2 Analysis of Wind 31
4.3 Verification of the Model Results 32
4.4 Cyclone Intensity 34
4.5 Cyclone Track 34
4.6 Mean Track Errors 36
4.7 Cyclic Experiments 37
4.8 Possible Mechanisms of Predicted Tracks 38
4.9 Sensitivity Experiments 38
4.9.1 Sensitivity Tests for GPS RO Soundings 38
4.9.2 Sensitivity with Bogus Vortex 40
4.9.3 Sensitivity Tests for QuikSCAT Data 41
CHAPTER 5 MEI-YU FRONT 43
5.1 Mei-yu Front 43
5.2 Description of Experiments 43
5.3 Analysis and Discussion 44
5.3.1 Analysis of Initial Increments 44
5.3.2 Analysis of Winds, Divergence and Cloud Liquid Water 46
5.3.3 Comparison of Accumulated Rainfall between CTL, GTS and LBA 47
5.3.4 Intercomparison of Accumulated Rainfall from LBA, REF and EPH 48
xii
5.3.5 Cyclic Experiments 49
5.4 Sensitivity Experiments 50
5.4.1 Sensitivity Experiment with GPS 50
5.4.2 Sensitivity Experiments with Moisture and Temperature 51
5.4.3 Sensitivity Experiment with Microphysics 51
5.4.4 Sensitivity Experiment with Background Error 52
5.5 Verification of Equitable Threat Score (ETS) 52
CHAPTER 6 CONCLUSIONS 54
References 59
Tables 72
Figures 79

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