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姓名	鄭景中(Ching-Chung Cheng)  查詢紙本館藏	畢業系所	太空科學與工程學系
論文名稱	全大氣模型熱氣層中性大氣資料同化 (THERMOSPHERIC NEUTRAL DENSITY DATA ASSIMILATION BASED ON THE WHOLE ATMOSPHERE MODEL)
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摘要(中)	太空天氣的擾動會使中性粒子濃度產生劇烈變化，影響熱氣層與電離層之耦合作用，並造成通訊與導航系統的影響。然而，目前的大氣模型缺乏對高層大氣中性氣體準確預報的能力。此篇論文基於Iterative Driver Estimation and Assimilation (IDEA)資料同化技術改善全大氣模型(Whole Atmosphere Model, WAM)中性氣體濃度預報能力。然而由於2003 年11月20日發生之磁暴事件之強度，WAM模型設定需做部分修改。第一個是允許輸入模型之planetary K (Kp)指數超過9，第二個是修改模型內部之太陽風風速之經驗模式。調整後的WAM，利用Challenging Mini-Satellite Payload (CHAMP) 衛星之中性粒子濃度資料進行資料同化後，其預報之中性粒子濃度準確度顯著提升，與CHAMP以及另一獨立的觀測資料Global Ultraviolet Imager (GUVI) limb-scan airglow observations aboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED)相吻合。在高度270至320公里範圍內，IDEA-WAM與GUVI誤差(root-mean-square errors, RMSe)最小，揭示了GUVI資料在此高度範圍的實用性。為了探討未來類似GUVI limb scan儀器資料的可用性，我們將GUVI的觀測資料與WAM模型進行同化。結果顯示，同化後的粒子濃度與觀測值接近，於地磁寧靜期，該濃度與CHAMP資料吻合。於磁暴期間，同化後的表現與CHAMP資料雖有誤差，但相較於同化前的模式預報有顯著提升。由於衛星觀測之空間與時間範圍的限制，High Accuracy Satellite Drag Model (HASDM) 被視為是穩定的全球尺度且長期的中性大氣濃度資料。故本論文嘗試將HASDM與WAM模式進行資料同化。其結果顯示，與HASDM之同化可有效消除模式偏差(bias)，並且可以期望未來應用在校正不同太陽活動下的模式誤差。 儘管經由IDEA資料同化技術消除了大部分模式誤差，研究結果仍顯示，在磁暴期間，同化後的模式存在少量誤差。該誤差推測是由於高緯度至極區的由極區對流(convection)產生之濃度空洞結構(density hole structure)位置並未準確地被模式估算，進而造成模式低估粒子濃度。然而，在未來仍然需要更進一步的研究以解決該問題。此外，為了更準確捕捉磁暴期間熱氣層之快速變化並降低了兩個參數之間的互相關性，我們亦嘗試調整Kp之同化窗格由3個小時降低至1.5小時，而F10.7則保持24小時不變。結果顯示，同化後的模式偏差有效降低，然而誤差及標準差皆變大，暗示了增加時間解析度的同時，降低了IDEA系統的可觀測性(observability)。另一方面，為避免Kp與太陽風之經驗關係(empirical relationship)所產生之誤差，以及未來銜接至預報系統時所可能產生的誤差，我們嘗試使用solar heating及Joule heating scale factors做為IDEA系統的估量參數(estimators)，並讓模式輸入實際觀測的太陽風驅動參數(observed solar wind drivers)，結果顯示該scale factors可同樣應用於IDEA系統之中。 本篇論文證明IDEA資料同化技術可以有效校正地磁寧靜與擾動期的WAM中性粒子濃度，並證明CHAMP和GUVI皆可應用於資料同化技術上，HASDM資料則可以有效消除模式偏差(bias)。本文對WAM模式的校正將會對熱氣層與電離層耦合作用有更進一步的了解，提升全大氣現報與預報能力，並提供更準確的低軌衛星軌道追蹤與預測能力。
摘要(英)	Space weather disturbances lead to significant changes in the neutral density and have a substantial impact on the thermosphere-ionosphere system due to the interaction between neutral particles and plasma. These subsequently affect the satellite drag, which plays an important role in satellite orbit maintenance, collision avoidance, and satellite traffic management. However, we still lack the ability to reliably predict the dynamic density of the upper atmosphere. The Iterative Driver Estimation and Assimilation (IDEA) data assimilation technique was employed with the Whole Atmosphere Model (WAM) to enhance neutral density specification in the upper thermosphere. Given the intensity of the November 2003 storm, two changes were necessary in WAM prior to applying IDEA. The first was to allow the Kp geomagnetic index to exceed 9 and the second was to modify the relationship between Kp and the solar wind parameters used to drive the model. With these changes in place, results show that WAM ingesting the accelerometer estimates of neutral density from the Challenging Mini-Satellite Payload (CHAMP) satellite effectively captured the thermospheric neutral density at the CHAMP’s altitude. Furthermore, data assimilation outputs were also validated against an independent neutral density data set from the Global Ultraviolet Imager (GUVI) limb-scan daytime airglow observations aboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite, and the strong agreement within 270-320 km altitude shows the utility of the IDEA technique and a potential use case of GUVI data set in the IDEA system. An experiment was conducted in which WAM ingested GUVI-derived neutral density at 300 km, and IDEA-GUVI data assimilation outputs closely matched GUVI observations throughout the storm period, while another data source should be incorporated to supplement the information. Given the limited spatial and temporal coverage of satellite measurements, High Accuracy Satellite Drag Model (HASDM) density database was employed for robust, global-scale, and long-term data integration into the IDEA scheme. Results show that the IDEA-HASDM effectively eliminated the model bias and brought the model density into the agreement with CHAMP during the quiet time. The prominent discrepancy, however, between WAM and CHAMP densities arises from the density dips at high latitudes during the peak of the storm. This discrepancy might result from different locations of observed and modeled high-latitude density hole structures, while further investigations are required to address this issue in the future. Additionally, to capture the rapid changes in the thermosphere, a reduced Kp estimation window from 3 to 1.5 hours was experimented. Meanwhile, the estimated F10.7 value remained unchanged at 24 hr in order to limit the correlation between Kp and F10.7 corrections. While this adjustment significantly reduced the model bias, it resulted in worse root-mean-square errors and standard deviation, which suggests that estimating six 1.5-hour Kp values instead of three 3-hour Kp lowers the observability of these interrelated and correlated parameters. For the future transition from a nowcasting to a forecasting system, solar and Joule heating scale factors were used as the estimators in IDEA, allowing the model to ingest the observed solar wind drivers. The good agreement between IDEA and CHAMP shows that the two scale factors can equally be integrated in the IDEA system. This dissertation demonstrates the utility of the IDEA scheme based on WAM and that using various data sources, such as neutral density estimates from accelerometers and airglow limb scan measurement and HASDM database. The improvements made to WAM can further enhance our understanding of the thermosphere-ionosphere coupling, bolster the whole atmosphere nowcasting and forecasting capability, and improve the accuracy of LEO satellite orbit determination.
關鍵字(中)	★ 太空天氣 ★ 熱氣層 ★ 中性大氣 ★ 資料同化 ★ 全大氣模型 ★ 磁暴	關鍵字(英)	★ thermosphere ★ neutral density ★ IDEA data assimilation ★ whole atmosphere model ★ CHAMP ★ TIMED/GUVI ★ HASDM ★ geomagnetic storm
論文目次	Table of Contents 摘要 i Abstract iii Acknowledgement v Table of Content vi List of Figures vii List of Tables x Chapter 1. Introduction 1 1.1 Motivation and objective 1 1.2 Thermosphere and ionosphere 3 Chapter 2. Methodology and Dataset 10 2.1 Whole atmosphere model (WAM) 10 2.2 Iterative driver estimation and assimilation (IDEA) 15 2.3 Satellite measurements and HASDM neutral density database 25 Chapter 3. Thermospheric Neutral Density Data Assimilation 38 3.1 WAM vs CHAMP neutral density observations 40 3.2 Assimilation experiments design 44 3.3 Data assimilation in the quiet time 47 3.4 Data assimilation during the geomagnetic disturbed period 49 3.5 IDEA neutral density evaluation and validation 55 3.6 Data assimilation using TIMED/GUVI and HASDM densities 66 Chapter 4. Discussion 79 4.1 Density dips anomalies at high latitudes 81 4.2 Changing the data assimilation windows 85 4.3 IDEA data assimilation using solar heating/Joule heating scaling 88 4.4 Future applications 92 Chapter 5. Summary and Conclusion 95 Reference 99
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指導教授	劉正彥(Jann-Yenq Liu)	審核日期	2024-10-16
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