Prediction of Tropical Cyclogenesis with GNSS RO Data Assimilation through the WRF Hybrid 3DEnVar

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范春昀(Pham Xuan Quan) 查詢紙本館藏

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大氣科學學系

論文名稱

(Prediction of Tropical Cyclogenesis with GNSS RO Data Assimilation through the WRF Hybrid 3DEnVar)

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至系統瀏覽論文 (2028-8-1以後開放)

摘要(中)

本研究探討同化 (DA)全球導航衛星系統 (GNSS) 無線電掩星 (RO) 資料，對2020年至2022年於西北太平洋的十個熱帶氣旋個案之模擬生成的影響。使用 WRFDA 系統中的混成 3DEnVar 方法，進行了四個 DA 實驗：1. 僅同化傳統觀測資料 (GTS)，和2. 進一步加入 GNSS RO 觀測，並以非局地折射算符進行同化 (EPH)，或是3. GTS加入衛星輻射資料進行同化 (RAD)，以及4.同化所有觀測資料 (ALL)。由統計分析顯示，同化 GNSS RO 和衛星輻射資料時，在模擬熱帶氣旋生成的時間和位置方面，呈現顯著改善。實驗ALL在模擬熱帶氣旋生成方面表現出色，平均空間誤差顯著減少。然而，實驗EPH結合了傳統觀測和 GNSS RO 數據，在24小時預報誤差範圍內，成功模擬氣旋生成優於其他實驗，強調了 GNSS RO 資料對準確預測氣旋生成的重要性。經由與歐洲中期天氣預報中心的第五代再分析資料 (ERA5) 驗證，證實同化 GNSS掩星和衛星輻射資料，顯著改善了綜觀環境場。使用衛星資料的實驗 (EPH、RAD 和 ALL) ，校驗結果顯示，對於水汽混成比和溫度的平均誤差 (ME) 和均方根誤差 (RMSE)的表現，優於僅使用傳統資料的同化模擬實驗 (GTS)。在水汽混成比方面，同化RO 資料的實驗在850 hPa高度以上表現最佳，其中實驗 ALL在550 hPa及更高層高度上提供了最佳結果。溫度方面，實驗RAD在500 hPa高度以上，呈現明顯的改善。GNSS RO數據的引入顯著增強了折射率模擬，特別是在中至上層對流層（1-10 km），減少了偏差和RMSE值。

對於特定颱風的個案研究，如2021年的璨樹颱風 (Typhoon Chanthu) 和2020年的哈格比颱風 (Typhoon Hagupit)，由模擬結果進一步說明了同化GNSS RO 和衛星輻射，有效提升初始模式水汽場和溫度場的準確性，進而改善氣旋生成預測的有效性。此外，同化 GNSS RO 資料可有助於改善中低層對流層中的水汽含量分佈，模式水氣增加，促使有組織的對流形成、強烈垂直運動，以及中層相對渦度的發展，這些條件有利於熱帶氣旋的生成。藉由位渦度趨勢收支分析，強調了非絕熱加熱對渦旋發展和維持的影響，並指出潛熱釋放是其中的關鍵因素。

摘要(英)

This study investigates the impact of global navigation satellite system (GNSS) radio occultation (RO) data assimilation (DA) on the cyclogenesis of ten tropical cyclones in the northwestern Pacific region from 2020 to 2022. Employing a hybrid 3DEnVar in the WRFDA system, four DA experiments are conducted: assimilating conventional data only (GTS), further incorporating only with GNSS RO data (EPH) or with radiance data (RAD), and assimilating all the above observations (ALL). Statistical analyses reveal significant improvements on time and location predictions of tropical cyclogenesis, particularly when both GNSS RO and radiance data are assimilated. ALL demonstrates superior predictive capabilities in capturing tropical cyclogenesis, with an averaged spatial error reduction. However, EPH outperforms others in simulating vortex formation within a 24-h prediction error range, highlighting the positive impact of GNSS RO data on improving cyclogenesis forecasting.

The verification over a larger region shows that incorporating RO and radiance data significantly improves synoptic environment modeling and particularly cyclogenesis forecasts. The global ERA5 reanalysis confirms that the experiments using satellite data (EPH, RAD, and ALL) outperform the conventional data experiment (GTS), especially in reducing mean errors and root mean square errors for water vapor mixing ratio and temperature. EPH excels above 850 hPa for water vapor mixing ratio, but ALL provides the best results from 550 hPa and higher. For temperature, RAD shows the most significant improvements above 500 hPa. Case studies on two specific typhoons, Chanthu (2021) and Hagupit (2020), further underscore the efficacy of GNSS RO and radiance DA in improvement of moisture and temperature predictions, crucial for cyclogenesis forecasts. Besides, assimilating GNSS RO data leads to an increase in the lower-mid-tropospheric moisture, organized convection, strong vertical motions at the grid scale, and the development of midlevel vorticity, and all these conditions are favorable for tropical cyclogenesis. Through the analysis of potential vorticity tendency budget, the significance of diabatic heating in influencing the development and maintenance of vortices is highlighted with latent heat release identified as a crucial factor.

關鍵字(中)

★ 氣旋生成
★ GNSS RO
★ 輻射資料進行
★ 數據同化
★ 混成 3DEnVar

關鍵字(英)

★ Tropical cyclogenesis
★ GNSS RO
★ Radiance data
★ Data assimilation
★ Hybrid 3DEnVar

論文目次

CHINESE ABSTRACT i
ENGLISH ABSTRACT ii
ACKNOWLEDGMENTS iii
TABLE OF CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
CHAPTER 1. INTRODUCTION 1
CHAPTER 2. NUMERICAL MODEL AND DATA ASSIMILATION 7
2.1. WRF model configurations and hybrid WRFDA 7
2.2. Observations 8
2.2.1. GNSS RO data and the forward operators 8
2.2.2. Radiance data assimilation and bias correction 9
CHAPTER 3. EXPERIMENTAL DESIGNS AND OVERALL RESULTS 12
3.1. Cyclogenesis detection 13
3.2. Area-averaged synoptic verification 15
3.2.1. Verification against the ERA5 reanalysis 15
3.2.2. Verification against the RO sounding 17
CHAPTER 4. TWO TYPHOON CASES 19
4.1. Typhoon Chanthu (2021) 20
4.2. Typhoon Hagupit (2020) 22
CHAPTER 5. FURTHER ANALYSIS FOR CASE STUDIES 25
5.1. Thermodynamic diagnosis and PV tendency budget examination for TY Chanthu (2021) 26
5.2. Discussion on TY Hagupit (2020) with radiance impacts 30
CHAPTER 6. ENSEMBLE FORECASTS 33
CHAPTER 7. CONCLUSIONS 36
REFERENCES 39
TABLES CONTAINED 50
FIGURES CONTAINED 53
APPENDIX A 66
APPENDIX B 73

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指導教授

黃清勇陳舒雅(Ching-Yuang Huang Shu-Ya Chen)

審核日期

2024-7-17

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