以星載GNSS-R技術推估海表粗糙度及其應用於改善風速反演之研究

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張琳(Lin Zhang) 查詢紙本館藏

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水文與海洋科學研究所

論文名稱

以星載GNSS-R技術推估海表粗糙度及其應用於改善風速反演之研究
(On the sea surface roughness and its influence on remote sensing using GNSS-R satellite)

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至系統瀏覽論文 (2024-12-31以後開放)

摘要(中)

全球衛星導航反射訊號接收儀（Global Navigation Satellite System-Reflectometry， GNSS-R）是以接收全球衛星定位系統在地球表面反射訊號形式，量測雷達散射截面積，以此獲取散射表面特徵之技術。自1998至2004年技術理論論證、雷達散射截面積校正演算法初步建立階段后，逐漸被廣泛應用於地球物理資訊如海表風速、海表熱通量、陸地水域面積、土壤濕度等之反演與量測，對於颱風預報、海水溢淹等災害防治有其重要意義。GNSS-R技術以L band受高風速下降水影響較小、衛星重訪率高、體積小、成本低等優勢成為微波遙測海表風速新的選擇。英國（2003， 2014）、美國（2017）、中國大陸（2019）相繼發射GNSS-R衛星用以進行風速等量測。風速-波高-頻率作為高風速海況下互相聯動的變數（wind-wave triplet）已得到基於實測的論證，代表波浪發展階段之波齡在熱帶氣旋波浪學中亦被證實與風速及海表粗糙度為一組互相影響之變數，在Cyclone Global Navigation Satellite System （CYGNSS）團隊建立高風速Geophysical Model Function （GMF）時亦被提及是造成高風速反演不確定性的主要原因之一。有研究團隊已將波浪模式之示性波高納入風速反演之考量，但尚未有人報導使用波齡或波浪平均頻率與風速共同建立風速反演模組。2023年9月臺灣獵風者號（TRITON）GNSS-R衛星即將發射，為使衛星升空後有可信賴之海氣參數反演系統，本論文以2017年發射且已作業化公開校正及反演數據產品之美國CYGNSS量測數據為先導，為獵風者號星載平台開發海表粗糙度及風速反演模組前體。
本研究之目的為，首先建立測試使用Level 1b校正模組以獲取品質合格之雷達散射截面積。其次為將基於實測及理論模型的高風速下風與波浪交互作用之最新理論引入技術發展，開發出基於目前散射原理而物理上更貼近高風速海況之風速反演模組，以推進反演技術發展降低風速反演不確定性，本研究將首次測試以無因次化之波浪頻率--波齡作為第二變數，來反演風速，並評估其與傳統方法反演品質之差異，並以相同參考風速與各國基於GNSS-R技術反演風速品質相比較。使其準確度及效能符合作業化需求，為大氣模式提供潛在可用於同化之雷達散射截面積、風速、海表粗糙度產品。
本研究貢獻在於，在各國已發展之GNSS-R衛星資料處理技術基礎上，自主建立Level 1b雷達散射截面積矯正模組，產生誤差在5%以內之Delay Doppler Map Average （DDMA）產品，並首次將代表波浪成長階段的波齡作為參數用以建立風速反演模組，將基於實測研究最新發展之高風速下波浪理論應用推廣於技術開發，並在風速品質評估階段發現，相比於直接將DDMA與風速產品相連接之方法反演的風速，反演風速之殘差降低近50%。產生之DDMA與風速反演產品具提供大氣模式做同化處理之潛力。產品品質具有國際競爭力。

摘要(英)

Global Navigation Satellite System-Reflectometry, or GNSS-R, is a method that involves receiving reflected signals from the Earth′s surface, transmitted by the GNSS. The aim is to measure the Normalized Bistatic Radar Cross Section (NBRCS), which enables the acquisition of geophysical information about the scattering surface. Following the technical theory demonstration from 1998 to 2004 and the initial establishment of the radar scattering cross-sectional area correction algorithm, the GNSS-R technique has been widely applied to the retrieval of geophysical information such as sea surface wind speed, sea surface heat flux, inland water extend detection, and soil moisture. These are significant implications for disaster prevention and mitigation, such as typhoon forecasting, inland flood inundation or seawater inundation. GNSS-R became a new candidate for microwave sea surface wind remote sensing for its low risk of being influenced by precipitation, high revisit period, low mass and low-cost properties. United Kingdom (2003, 2014), America (2017) and Mainland China (2019) have already launched their GNSS-R satellite, and they have all been applied to retrieve the sea surface wind speed, u_10. Wind speed-wave height-wave frequency has been proven to be a coupled variable in high wind conditions and is called a wind-wave triplet. Wave age which represents the wave development stage have been proved to vary with wind speed and ocean surface roughness, mean square slope. The wind speed retrieving algorithm for GNSS-R has been explored for years. Most studies directly link Delay Doppler Map observable to wind speed to generate a one-step geophysical model function (GMF). In Cyclone Global Navigation Satellite System (CYGNSS) mission, they have built a wind speed retrieving algorithm for high wind speed conditions (15~70 m/s) by linking DDM observable to hurricane penetrate-measured collocated data and mentioned that wave age and fetch length in the state of development of the long wave portion near or far from a hurricane would account for the high uncertainty in high wind speed GMF establishment. Significant wave height has been used to retrieve wind speed in NOAA′s algorithm, however, no study has reported the usage of wave age or wave frequency to retrieve the wind speed. The GNSS-R satellite TRITON will be launched in September, 2023. This study aims to build a pre-launch DDM calibration and retrieving system using the published operational products from CYGNSS system for TRITON.

There are two aims of this study. The first aim is to establish the Level 1b calibration module to obtain the qualified normalized bistatic radar cross section (NBRCS) from GNSS-R measured Delay Dopper Map (DDM). The second aim is to develop a wind speed retrieving module applying the latest theories based on theoretical models of wind and wave interactions under high wind speeds to get wind products that better represent the natural condition. For the first time, this research will test the use of dimensionless wave frequency -- wave age as a second variable for wind speed retrieval and evaluate its difference in quality from DDMA-u_10 GMF method. Furthermore, the results will be compared with the quality of wind speed retrieval based on GNSS-R technology from different countries using the same reference wind speed. The established system can provide NBRCS, wind speed and ocean surface roughness products for the data assimilation system.

The contribution of this study is, firstly, we have established a Level 1b calibration system base on the published literature and have produced Delay Doppler Map Average (DDMA) product in which the residual to the reference product is within 5%. The wind speed retrieved from the latest-built DDMA-Mean Square Slope-Wave age GMF improved the root-mean-square-difference to the degree of 50% compared to the traditional method, and the bias is within 0.1 m/s for wind speed within 0 to 12 m/s. The quality is competitive with the products from other teams.

關鍵字(中)

★ 風速反演算法
★ 全球衛星定位系統反射計
★ 波齡
★ 海表粗糙度
★ 風速品質評估

關鍵字(英)

★ wind speed retrieving algorithm
★ GNSS-R
★ wave age
★ sea surface roughness
★ wind speed product performance assessment

論文目次

目錄
中文摘要 I
ABSTRACT III
謝誌 V
目錄 VII
圖目錄 IX
表目錄 XI
縮寫列表 XII
符號說明 XIV
1. 背景介紹 1
1.1 熱帶氣旋強度變化與海氣通量交換係數研究現狀 2
1.1.1 海氣通量交換係數基本定義 2
1.1.2 海氣通量參數隨風速的變化回顧 3
1.2 熱帶氣旋下波浪場不對稱性及對海氣交互作用參數之影響回顧 3
1.2.1 波浪學基礎 3
1.2.2 TC下波浪場的不對稱性 4
1.2.3 波浪場不對稱性與海氣通量交換參數不對稱性之關聯 6
1.3 高風速天氣系統之風速及海表粗糙度觀測資料需求 7
1.4 微波波段風速遙測回顧 8
1.4.1 Ku-, Ka-, X-, C-band風速量測能力及限制 8
1.4.2 GNSS-R風速反演回顧 9
1.5 獵風者衛星簡介 12
1.6 研究動機與目的 12
1.7 研究策略與方法 13
1.8 本文章節架構 14
2. 全球衛星導航系統反射計基礎 15
2.1 雙基地前向散射及其幾何結構 15
2.2 散射表面粗糙度（SURFACE ROUGHNESS）與海表粗糙度 17
2.3 雷達散射截面積與海表粗糙度 19
2.4 雷達方程 20
3. 獲取反射計雷達散射截面積 20
3.1 LEVEL 1B處理流程 20
3.2 兩次校準獲得雷達散射截面積 22
3.3 DDM AVERAGE (DDMA)結果及品質評估 23
4. 風速反演算法建立 25
4.1 簡介 25
4.2 ONE-STEP GMF建立方法 25
4.3 採用ONE-STEP GMF方法反演品質驗證 27
4.3.1 產品品質評估方法簡介 27
4.4 誤差來源分析與討論 29
5. 以TWO-STEP 方式反演海表粗糙度及風速 32
5.1 從DDMA到MSS-方法與結果 33
5.2 從MSS到U10 35
5.3 TWO-STEP GMF建製結果 38
5.4 以TWO-STEP方式反演風速結果及產品品質評估 40
5.5 討論 41
6. 貢獻與結論 44
7. 未來工作規畫 45
參考文獻 46

參考文獻

參考文獻
Al-Khaldi, M. M., Johnson, J. T., Gleason, S., Chew, C. C., Gerlein-Safdi, C., Shah, R., & Zuffada, C. (2021). Inland water body mapping using CYGNSS coherence detection. IEEE Transactions on Geoscience and Remote Sensing, 59(9), 7385-7394.
Alonso-Arroyo, A., Camps, A., Park, H., Pascual, D., Onrubia, R., & Martin, F. (2015). Retrieval of Significant Wave Height and Mean Sea Surface Level Using the GNSS-R Interference Pattern Technique: Results From a Three-Month Field Campaign. IEEE Transactions on Geoscience and Remote Sensing, 53(6), 3198-3209. https://doi.org/10.1109/Tgrs.2014.2371540
Armatys, M. J. (2001). Estimation of sea surface winds using reflected GPS signals. University of Colorado at Boulder.
Asharaf, S., Posselt, D. J., Said, F., & Ruf, C. S. (2023). Updates on CYGNSS Ocean Surface Wind Validation in the Tropics. Journal of Atmospheric and Oceanic Technology, 40(1), 37-51. https://doi.org/10.1175/jtech-d-21-0168.1
Asharaf, S., Waliser, D. E., Posselt, D. J., Ruf, C. S., Zhang, C. D., & Putra, A. W. (2021). CYGNSS Ocean Surface Wind Validation in the Tropics. Journal of Atmospheric and Oceanic Technology, 38(4), 711-724. https://doi.org/10.1175/Jtech-D-20-0079.1
Balasubramaniam, R., & Ruf, C. (2020). Azimuthal Dependence of GNSS-R Scattering Cross-Section in Hurricanes. Journal of Geophysical Research-Oceans, 125(7). https://doi.org/ARTN e2020JC016167
10.1029/2020JC016167
Barrick, D. (1968). Relationship between slope probability density function and the physical optics integral in rough surface scattering. Proceedings of the IEEE, 56(10), 1728-1729.
Barrick, D. (1968). Rough surface scattering based on the specular point theory. IEEE Transactions on Antennas and Propagation, 16(4), 449-454.
Bass, F. G., & Fuks, I. M. (2013). Wave Scattering from Statistically Rough Surfaces: International Series in Natural Philosophy (Vol. 93). Elsevier.
Black, P. G., D′Asaro, E. A., Drennan, W. M., French, J. R., Niiler, P. P., Sanford, T. B., Terrill, E. J., Walsh, E. J., & Zhang, J. A. (2007). Air–Sea Exchange in Hurricanes: Synthesis of Observations from the Coupled Boundary Layer Air–Sea Transfer Experiment. Bulletin of the American Meteorological Society, 88(3), 357-374. https://doi.org/https://doi.org/10.1175/BAMS-88-3-357
Camps, A., Park, H., Pablos, M., Foti, G., Gommenginger, C. P., Liu, P.-W., & Judge, J. (2016). Sensitivity of GNSS-R spaceborne observations to soil moisture and vegetation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10), 4730-4742.
Cartwright, J., Banks, C. J., & Srokosz, M. (2019). Sea ice detection using GNSS‐R data from TechDemoSat‐1. Journal of Geophysical Research: Oceans, 124(8), 5801-5810.
Chen, S., Qiao, F. L., Zhang, J. A., Xue, Y. H., Ma, H. Y., & Chen, S. Y. (2022). Observed Drag Coefficient Asymmetry in a Tropical Cyclone. Journal of Geophysical Research-Oceans, 127(9). https://doi.org/ARTN e2021JC018360
10.1029/2021JC018360
Clarizia, M. P. (2012). Investigating the effect of ocean waves on GNSS-R microwave remote sensing measurements University of Southampton].
Clarizia, M. P., Pierdicca, N., Costantini, F., & Floury, N. (2019). Analysis of CYGNSS data for soil moisture retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2227-2235.
Clarizia, M. P., & Ruf, C. S. (2016). Wind Speed Retrieval Algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) Mission. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4419-4432. https://doi.org/10.1109/Tgrs.2016.2541343
Clarizia, M. P., & Ruf, C. S. (2020). Statistical derivation of wind speeds from CYGNSS data. IEEE Transactions on Geoscience and Remote Sensing, 58(6), 3955-3964.
Clarizia, M. P., Ruf, C. S., Jales, P., & Gommenginger, C. (2014). Spaceborne GNSS-R Minimum Variance Wind Speed Estimator. IEEE Transactions on Geoscience and Remote Sensing, 52(11), 6829-6843. https://doi.org/10.1109/Tgrs.2014.2303831
Crespo, J. A., Posselt, D. J., & Asharaf, S. (2019). CYGNSS surface heat flux product development. Remote Sensing, 11(19), 2294.
Dierssen, H., Garaba, S., Vlahos, P., & Monahan, E. (2020). Recent Advances in the Study of Oceanic Whitecaps.
Downs, B., Kettner, A. J., Chapman, B. D., Brakenridge, G. R., O’Brien, A. J., & Zuffada, C. (2023). Assessing the Relative Performance of GNSS-R Flood Extent Observations: Case Study in South Sudan. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-13.
Edokossi, K., Calabia, A., Jin, S., & Molina, I. (2020). GNSS-reflectometry and remote sensing of soil moisture: A review of measurement techniques, methods, and applications. Remote Sensing, 12(4), 614.
Elfouhaily, T., Chapron, B., Katsaros, K., & Vandemark, D. (1997). A unified directional spectrum for long and short wind-driven waves. Journal of Geophysical Research-Oceans, 102(C7), 15781-15796. https://doi.org/Doi 10.1029/97jc00467
Emanuel, K. (2003). A Similarity Hypothesis for Air–Sea Exchange at Extreme Wind Speeds. Journal of the Atmospheric Sciences, 60(11), 1420-1428. https://doi.org/https://doi.org/10.1175/1520-0469(2003)060<1420:ASHFAE>2.0.CO;2
Emanuel, K. A. (1986). An Air-Sea Interaction Theory for Tropical Cyclones. Part I: Steady-State Maintenance. Journal of Atmospheric Sciences, 43(6), 585-605. https://doi.org/https://doi.org/10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2
Emanuel, K. A. (1995). Sensitivity of Tropical Cyclones to Surface Exchange Coefficients and a Revised Steady-State Model incorporating Eye Dynamics. Journal of Atmospheric Sciences, 52(22), 3969-3976. https://doi.org/https://doi.org/10.1175/1520-0469(1995)052<3969:SOTCTS>2.0.CO;2
Foti, G., Gommenginger, C., Jales, P., Unwin, M., Shaw, A., Robertson, C., & Rosello, J. (2015). Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat-1 mission. Geophysical Research Letters, 42(13), 5435-5441. https://doi.org/10.1002/2015gl064204
Foti, G., Gommenginger, C., & Srokosz, M. (2017). First Spaceborne GNSS-Reflectometry Observations of Hurricanes From the UK TechDemoSat-1 Mission. Geophysical Research Letters, 44(24), 12358-12366. https://doi.org/10.1002/2017gl076166
Garrison, J. L., Katzberg, S. J., & Hill, M. I. (1998). Effect of sea roughness on bistatically scattered range coded signals from the Global Positioning System. Geophysical Research Letters, 25(13), 2257-2260.
Garrison, J. L., Komjathy, A., Zavorotny, V. U., & Katzberg, S. J. (2002). Wind speed measurement using forward scattered GPS signals. IEEE Transactions on Geoscience and Remote Sensing, 40(1), 50-65.
Gerlein‐Safdi, C., & Ruf, C. S. (2019). A CYGNSS‐based algorithm for the detection of inland waterbodies. Geophysical Research Letters, 46(21), 12065-12072.
Gleason, S. (2006). Land and Ice Remote Sensing From Low Earth Orbit Using GNSS Bistatic Radar. Proceedings of the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2006),
Gleason, S., Ruf, C. S., Clarizia, M. P., & O′Brien, A. J. (2016). Calibration and unwrapping of the normalized scattering cross section for the cyclone global navigation satellite system. IEEE Transactions on Geoscience and Remote Sensing, 54(5), 2495-2509.
Gleason, S., Ruf, C. S., O′Brien, A. J., & McKague, D. S. (2019). The CYGNSS Level 1 Calibration Algorithm and Error Analysis Based on On-Orbit Measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(1), 37-49. https://doi.org/10.1109/Jstars.2018.2832981
Gleason, S., Zavorotny, V. U., Akos, D. M., Hrbek, S., PopStefanija, I., Walsh, E. J., Masters, D., & Grant, M. S. (2018). Study of Surface Wind and Mean Square Slope Correlation in Hurricane Ike With Multiple Sensors. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(6), 1975-1988. https://doi.org/10.1109/Jstars.2018.2827045
Golbraikh, E., & Shtemler, Y. M. (2016). Foam input into the drag coefficient in hurricane conditions. Dynamics of Atmospheres and Oceans, 73, 1-9.
Green, B. W., & Zhang, F. (2013). Impacts of air–sea flux parameterizations on the intensity and structure of tropical cyclones. Mon. Wea. Rev., 141, 2308-2324. https://doi.org/https://doi.org/10.1175/MWR-D-12-00274.1
Hasselmann, K. (1973). Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Ergänzung zur Deut. Hydrogr. Z., Reihe A (8), 12, 1-95. https://epic.awi.de/id/eprint/10163/
Holthuijsen, L. H. (2010). Waves in oceanic and coastal waters. Cambridge university press.
Holthuijsen, L. H., Powell, M. D., & Pietrzak, J. D. (2012). Wind and waves in extreme hurricanes. Journal of Geophysical Research-Oceans, 117. https://doi.org/Artn C09003
10.1029/2012jc007983
Hwang, P. A. (2006). Duration‐and fetch‐limited growth functions of wind‐generated waves parameterized with three different scaling wind velocities. Journal of Geophysical Research: Oceans, 111(C2).
Hwang, P. A. (2016). Fetch- and Duration-Limited Nature of Surface Wave Growth inside Tropical Cyclones: With Applications to Air–Sea Exchange and Remote Sensing. Journal of Physical Oceanography, 46(1), 41-56. https://doi.org/10.1175/jpo-d-15-0173.1
Hwang, P. A. (2019). Surface Foam and L-Band Microwave Radiometer Measurements in High Winds. IEEE Transactions on Geoscience and Remote Sensing, 57(5), 2766-2776. https://doi.org/10.1109/tgrs.2018.2876972
Hwang, P. A. (2021). Deriving L-Band Tilting Ocean Surface Roughness From Measurements by Operational Systems. IEEE Transactions on Geoscience and Remote Sensing, 59(2), 940-949. https://doi.org/10.1109/tgrs.2020.3001023
Hwang, P. A. (2022a). Azimuthal Variation of L-Band Tilting Roughness Inside Tropical Cyclones. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. https://doi.org/10.1109/lgrs.2020.3023655
Hwang, P. A. (2022b). Ocean Surface Roughness from Satellite Observations and Spectrum Modeling of Wind Waves. Journal of Physical Oceanography, 52(9), 2143-2158. https://doi.org/https://doi.org/10.1175/JPO-D-22-0043.1
Hwang, P. A., Fan, Y., Ocampo-Torres, F. J., & García-Nava, H. (2017). Ocean Surface Wave Spectra inside Tropical Cyclones. Journal of Physical Oceanography, 47(10), 2393-2417. https://doi.org/10.1175/jpo-d-17-0066.1
Hwang, P. A., & Fan, Y. L. (2018). Low-Frequency Mean Square Slopes and Dominant Wave Spectral Properties: Toward Tropical Cyclone Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, 56(12), 7359-7368. https://doi.org/10.1109/Tgrs.2018.2850969
Hwang, P. A., Li, X., & Zhang, B. (2017). Retrieving hurricane wind speed from dominant wave parameters. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 2589-2598. https://doi.org/https://doi.org/10.1109/JSTARS.2017.2650410
Hwang, P. A., & Sletten, M. A. (2008). Energy dissipation of wind-generated waves and whitecap coverage. Journal of Geophysical Research, 113(C2). https://doi.org/10.1029/2007jc004277
Janssen, P. A. E. M., & Bidlot, J. R. (2023). Wind-Wave Interaction for Strong Winds. Journal of Physical Oceanography, 53(3), 779-804. https://doi.org/10.1175/Jpo-D-21-0293.1
Jing, C., Niu, X., Duan, C., Lu, F., Di, G., & Yang, X. (2019). Sea surface wind speed retrieval from the first Chinese GNSS-R mission: Technique and preliminary results. Remote Sensing, 11(24), 3013.
Klein, L., & Swift, C. (1977). An improved model for the dielectric constant of sea water at microwave frequencies. IEEE Transactions on Antennas and Propagation, 25(1), 104-111.
Klotz, B. W., & Uhlhorn, E. W. (2014). Improved Stepped Frequency Microwave Radiometer Tropical Cyclone Surface Winds in Heavy Precipitation. Journal of Atmospheric and Oceanic Technology, 31(11), 2392-2408. https://doi.org/https://doi.org/10.1175/JTECH-D-14-00028.1
Komjathy, A., Armatys, M., Masters, D., Axelrad, P., Zavorotny, V., & Katzberg, S. (2004). Retrieval of ocean surface wind speed and wind direction using reflected GPS signals. Journal of Atmospheric and Oceanic Technology, 21(3), 515-526.
Komjathy, A., Zavorotny, V., Axelrad, P., Born, G., & Garrison, J. (1998). GPS signal scattering from sea surface: Comparison between experimental data and theoretical model. Presented at the Fifth International Conference on Remote Sensing for Marine and Coastal Environments,
Komjathy, A., Zavorotny, V. U., Axelrad, P., Born, G. H., & Garrison, J. L. (2000). GPS signal scattering from sea surface: Wind speed retrieval using experimental data and theoretical model. Remote Sensing of Environment, 73(2), 162-174.
Komori, S., Iwano, K., Takagaki, N., Onishi, R., Kurose, R., Takahashi, K., & Suzuki, N. (2018). Laboratory measurements of heat transfer and drag coefficients at extremely high wind speeds. J. Phys. Oceanogr., 48, 959-974. https://doi.org/https://doi.org/10.1175/JPO-D-17-0243.1
Li, C., & Huang, W. (2013). Simulating Gnss-R Delay-Doppler Map of Oil Slicked Sea Surfaces under General Scenarios. Progress In Electromagnetics Research B, 48, 61-76. https://doi.org/10.2528/pierb12111509
Li, X., Yang, J., Yan, Y., & Li, W. (2022). Exploring CYGNSS mission for surface heat flux estimates and analysis over tropical oceans. Frontiers in Marine Science, 9, 1001491.
Li, X. F., Zhang, J. A., Yang, X. F., Pichel, W. G., DeMaria, M., Long, D., & Li, Z. W. (2013). Tropical Cyclone Morphology from Spaceborne Synthetic Aperture Radar. Bulletin of the American Meteorological Society, 94(2), 215-+. https://doi.org/10.1175/Bams-D-11-00211.1
Marshall, J., & Plumb, R. A. (1989). Atmosphere, ocean and climate dynamics: an introductory text. Academic Press.
Montgomery, M. T., Nguyen, S. V., Smith, R. K., & Persing, J. (2009). Do tropical cyclones intensify by WISHE? Quart. J. Roy. Meteor. Soc., 135, 1697-1714. https://doi.org/https://doi.org/10.1002/qj.459
Montgomery, M. T., Persing, J., & Smith, R. K. (2015). Putting to rest WISHE-ful misconceptions for tropical cyclone intensification. J. Adv. Model. Earth Syst., 7, 92-109. https://doi.org/https://doi.org/10.1002/2014MS000362
Montgomery, M. T., & Smith, R. K. (2014). Paradigms for tropical cyclone intensification. Australian Meteorological and Oceanographic Journal, 64(1), 37-66. https://doi.org/https://doi.org/10.1071/ES14005
Mueller, J. A., & Veron, F. (2014). Impact of sea spray on air–sea fluxes. Part II: Feedback effects. J. Phys. Oceanogr., 44, 2835-2853. https://doi.org/https://doi.org/10.1175/JPO-D-13-0246.1
Munoz-Martin, J. F., Perez, A., Camps, A., Ribó, S., Cardellach, E., Stroeve, J., Nandan, V., Itkin, P., Tonboe, R., & Hendricks, S. (2020). Snow and ice thickness retrievals using GNSS-R: Preliminary results of the MOSAiC experiment. Remote Sensing, 12(24), 4038.
Naud, C. M., Crespo, J. A., Posselt, D. J., & Booth, J. F. (2023). Cloud and Precipitation in low-latitude extratropical cyclones conditionally sorted on CYGNSS surface latent and sensible heat fluxes. Journal of Climate, 1-41.
Peng, M. S., Jeng, B., & Williams, R. T. (1999). A numerical study on tropical cyclone intensification. Part I: Beta effect and mean flow effect. J. Atmos. Sci., 56, 1404-1423. https://doi.org/https://doi.org/10.1175/1520-0469(1999)056<1404:ANSOTC>2.0.CO;2
Portabella, M., Stoffelen, A., Lin, W. M., Turiel, A., Verhoef, A., Verspeek, J., & Ballabrera-Poy, J. (2012). Rain Effects on ASCAT-Retrieved Winds: Toward an Improved Quality Control. IEEE Transactions on Geoscience and Remote Sensing, 50(7), 2495-2506. https://doi.org/10.1109/Tgrs.2012.2185933
Rodriguez-Alvarez, N., Bosch-Lluis, X., Camps, A., Aguasca, A., Vall-Llossera, M., Valencia, E., Ramos-Perez, I., & Park, H. (2011). Review of crop growth and soil moisture monitoring from a ground-based instrument implementing the interference pattern GNSS-R technique. Radio Science, 46(06), 1-11.
Rodriguez-Alvarez, N., Holt, B., Jaruwatanadilok, S., Podest, E., & Cavanaugh, K. C. (2019). An Arctic sea ice multi-step classification based on GNSS-R data from the TDS-1 mission. Remote Sensing of Environment, 230, 111202.
Ruf, C. (2022). CYGNSS handbook.
Ruf, C., Asharaf, S., Balasubramaniam, R., Gleason, S., Lang, T., Mckague, D., Twigg, D., & Waliser, D. (2019). In-Orbit Performance of the Constellation of CYGNSS Hurricane Satellites. Bulletin of the American Meteorological Society, 100(10), 2009-2023. https://doi.org/10.1175/Bams-D-18-0337.1
Ruf, C., Gleason, S., Ridley, A., Rose, R., & Scherrer, J. (2017). The nasa cygnss mission: Overview and status update. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),
Ruf, C. S., Atlas, R., Chang, P. S., Clarizia, M. P., Garrison, J. L., Gleason, S., Katzberg, S. J., Jelenak, Z., Johnson, J. T., Majumdar, S. J., O′brien, A., Posselt, D. J., Ridley, A. J., Rose, R. J., & Zavorotny, V. U. (2016). New Ocean Winds Satellite Mission to Probe Hurricanes and Tropical Convection. Bulletin of the American Meteorological Society, 97(3). https://doi.org/10.1175/Bams-D-14-00218.1
Ruf, C. S., & Balasubramaniam, R. (2019). Development of the CYGNSS Geophysical Model Function for Wind Speed. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(1), 66-77. https://doi.org/10.1109/Jstars.2018.2833075
Ruf, C. S., Chew, C., Lang, T., Morris, M. G., Nave, K., Ridley, A., & Balasubramaniam, R. (2018). A New Paradigm in Earth Environmental Monitoring with the CYGNSS Small Satellite Constellation. Sci Rep, 8(1), 8782. https://doi.org/10.1038/s41598-018-27127-4
Ruf, C. S., Gleason, S., & McKague, D. S. (2019). Assessment of CYGNSS Wind Speed Retrieval Uncertainty. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(1), 87-97. https://doi.org/10.1109/Jstars.2018.2825948
Said, F., Jelenak, Z., Park, J., & Chang, P. S. (2022). The NOAA Track-Wise Wind Retrieval Algorithm and Product Assessment for CyGNSS. IEEE Transactions on Geoscience and Remote Sensing, 60. https://doi.org/10.1109/Tgrs.2021.3087426
Sapp, J. W., Alsweiss, S. O., Jelenak, Z., Chang, P. S., & Carswell, J. (2019). Stepped frequency microwave radiometer wind-speed retrieval improvements. Remote Sensing, 11(3), 214.
Soloviev, A. V., Lukas, R., Donelan, M. A., Haus, B. K., & Ginis, I. (2014). The air-sea interface and surface stress under tropical cyclones. Sci. Rep., 4, 5306. https://doi.org/https://doi.org/10.1038/srep05306
Soloviev, A. V., Lukas, R., Donelan, M. A., Haus, B. K., & Ginis, I. (2017). Is the state of the air-sea interface a factor in rapid intensification and rapid decline of tropical cyclones? J. Geophys. Res. Oceans, 122, 10 174-110 183. https://doi.org/https://doi.org/10.1002/2017JC013435
Sroka, S., & Emanuel, K. (2021). A Review of Parameterizations for Enthalpy and Momentum Fluxes from Sea Spray in Tropical Cyclones. Journal of Physical Oceanography, 51(10), 3053-3069. https://doi.org/10.1175/Jpo-D-21-0023.1
Tamizi, A., Alves, J. H., & Young, I. R. (2021). The Physics of Ocean Wave Evolution within Tropical Cyclones. Journal of Physical Oceanography, 51(7), 2373-2388. https://doi.org/10.1175/Jpo-D-21-0005.1
Tamizi, A., & Young, I. R. (2020). The Spatial Distribution of Ocean Waves in Tropical Cyclones. Journal of Physical Oceanography, 50(8), 2123-2139. https://doi.org/10.1175/Jpo-D-20-0020.1
Troitskaya, Y., Druzhinin, O., Kozlov, D., & Zilitinkevich, S. (2018). The “bag breakup” spume droplet generation mechanism at high winds. Part II: Contribution to momentum and enthalpy transfer. J. Phys. Oceanogr., 48, 2189-2207. https://doi.org/https://doi.org/10.1175/JPO-D-17-0105.1
Troitskaya, Y., Kandaurov, A., Ermakova, O., Kozlov, D., Sergeev, D., & Zilitinkevich, S. (2018). The “bag breakup” spume droplet generation mechanism at high winds. Part I: Spray generation function. J. Phys. Oceanogr., 48, 2167-2188. https://doi.org/https://doi.org/10.1175/JPO-D-17-0104.1
Troitskaya, Y., Sergeev, D., Kandaurov, A., Vdovin, M., & Zilitinkevich, S. (2019). The effect of foam on waves and the aerodynamic roughness of the water surface at high winds. J. Phys. Oceanogr., 49, 959-981. https://doi.org/https://doi.org/10.1175/JPO-D-18-0168.1
Troitskaya, Y. I., Kandaurov, A., Ermakova, O., Kozlov, D., Sergeev, D., & Zilitinkevich, S. (2017). Bag-breakup fragmentation as the dominant mechanism of sea-spray production in high winds. Sci. Rep., 7, 1614. https://doi.org/https://doi.org/10.1038/s41598-017-01673-9
Troitskaya, Y. I., Sergeev, D. A., Kandaurov, A. A., Baidakov, G. A., Vdovin, M. A., & Kazakov, V. I. (2012). Laboratory and theoretical modeling of air-sea momentum transfer under severe wind conditions. Journal of Geophysical Research-Oceans, 117, C00J21. https://doi.org/Artn C00j21
10.1029/2011jc007778
Tsai, Y. F., Yeh, W. H., Juang, J. C., Yang, D. S., & Lin, C. T. (2021). From GPS Receiver to GNSS Reflectometry Payload Development for the Triton Satellite Mission. Remote Sensing, 13(5). https://doi.org/ARTN 999
10.3390/rs13050999
Tye, J., Jales, P., Unwin, M., & Underwood, C. (2016). The first application of stare processing to retrieve mean square slope using the SGR-ReSI GNSS-R experiment on TDS-1. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10), 4669-4677.
Uhlhorn, E. W., & Black, P. G. (2003). Verification of remotely sensed sea surface winds in hurricanes. J. Atmos. Oceanic Technol., 20, 99-116.
Uhlhorn, E. W., Black, P. G., Franklin, J. L., Goodberlet, M., Carswell, J., & Goldstein, A. S. (2007). Hurricane surface wind measurements from an operational Stepped Frequency Microwave Radiometer. Mon. Wea. Rev., 135, 3070-3085.
Ulaby, F. T., Long, D. G., & University of Michigan. Press. (2014). Microwave radar and radiometric remote sensing [still image]. The University of Michigan Press.
Unnithan, S. K., Biswal, B., & Rüdiger, C. (2020). Flood inundation mapping by combining GNSS-R signals with topographical information. Remote Sensing, 12(18), 3026.
Wang, T. L., Ruf, C. S., Block, B., McKague, D. S., & Gleason, S. (2019). Design and Performance of a GPS Constellation Power Monitor System for Improved CYGNSS L1B Calibration. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(1), 26-36. https://doi.org/10.1109/Jstars.2018.2867773
Wright, C. W., Walsh, E. J., Vandemark, D., Krabill, W. B., Garcia, A. W., Houston, S. H., Powell, M. D., Black, P. G., & Marks, F. D. (2001). Hurricane directional wave spectrum spatial variation in the open ocean. Journal of Physical Oceanography, 31(8), 2472-2488. https://doi.org/Doi 10.1175/1520-0485(2001)031<2472:Hdwssv>2.0.Co;2
Yan, Q., & Huang, W. (2016). Spaceborne GNSS-R sea ice detection using delay-Doppler maps: First results from the UK TechDemoSat-1 mission. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10), 4795-4801.
Yan, Q., & Huang, W. (2019). Sea ice remote sensing using GNSS-R: A review. Remote Sensing, 11(21), 2565.
Young, I. R. (1999). Wind generated ocean waves. Elsevier.
Young, I. R. (2006a). Directional spectra of hurricane wind-waves. J. Geophys. Res., 111, C08020.
Young, I. R. (2006b). Directional spectra of hurricane wind waves. Journal of Geophysical Research-Oceans, 111(C8). https://doi.org/Artn C08020
10.1029/2006jc003540
Yu, K., Han, S., Bu, J., An, Y., Zhou, Z., Wang, C., Tabibi, S., & Cheong, J. W. (2022). Spaceborne GNSS Reflectometry. Remote Sensing, 14(7), 1605. https://www.mdpi.com/2072-4292/14/7/1605
Yu, K., Li, Y., & Chang, X. (2018). Snow depth estimation based on combination of pseudorange and carrier phase of GNSS dual-frequency signals. IEEE Transactions on Geoscience and Remote Sensing, 57(3), 1817-1828.
Zakharow, V. E., Badulin, S. I., Hwang, P. A., & Caulliez, G. (2015). Universality of sea wave growth and its physical roots. Journal of Fluid Mechanics, 780, 503-535. https://doi.org/10.1017/jfm.2015.468
Zavorotny, V. U., Gleason, S., Cardellach, E., & Camps, A. (2014). Tutorial on remote sensing using GNSS bistatic radar of opportunity. IEEE Geoscience and Remote Sensing Magazine, 2(4), 8-45.
Zavorotny, V. U., & Voronovich, A. G. (2000). Scattering of GPS signals from the ocean with wind remote sensing application. IEEE Transactions on Geoscience and Remote Sensing, 38(2), 951-964. https://doi.org/Doi 10.1109/36.841977
Zhang, F., & Emanuel, K. (2016). On the role of surface fluxes and WISHE in tropical cyclone intensification. J. Atmos. Sci., 73, 2011-2019. https://doi.org/https://doi.org/10.1175/JAS-D-16-0011.1
Zhang, J. A., Black, P. G., French, J. R., & Drennan, W. M. (2008). First direct measurements of enthalpy flux in the hurricane boundary layer: The CBLAST results. Geophysical Research Letters, 35(14). https://doi.org/Artn L14813
10.1029/2008gl034374
Zhang, L., & Oey, L. (2018). An observational analysis of ocean surface waves in tropical cyclones in the western North Pacific Ocean. J. Geophys. Res. Oceans.
Zhang, L., & Oey, L. (2019). Young Ocean Waves Favor the Rapid Intensification of Tropical Cyclones—A Global Observational Analysis. Monthly Weather Review, 147(1), 311-328. https://doi.org/https://doi.org/10.1175/MWR-D-18-0214.1

指導教授

錢樺(Hwa Chien)

審核日期

2023-7-26

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