簡易檢索 / 詳目顯示

回結果列表
研究生： 林昱丞
Lin, Yu-Cheng
論文名稱： 深度學習應用於 ISUAL 事件辨識與高空短暫發光現象之時空特徵研究
Deep Learning for Identification and Spatiotemporal Analysis of Transient Luminous Events by ISUAL Observations
指導教授： 陳炳志
Chen, Alfred Bing-Chih
學位類別： 博士
Doctor
系所名稱： 理學院 - 物理學系
Department of Physics
論文出版年： 2026
畢業學年度： 114
語文別： 英文
論文頁數： 178
中文關鍵詞： 高空閃電事件 、福爾摩沙衛星二號 、ISUAL 、全球閃電偵測網路 、深度學習 、卷積神經網路 、DIAT
外文關鍵詞： FORMOSAT-2, Transient Luminous Events, ISUAL, WWLLN, DIAT, Convolutional Neural Networks, Deep Learning
相關次數： 點閱：7  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 搭載於福爾摩沙衛星二號(FORMOSAT-2)上的高空閃電影像儀(Imager of Sprites and Upper Atmospheric Lightning, ISUAL)專注於研究發生在對流層與電離層之間的高空短暫發光現象(Transient Luminous Events, TLEs)。ISUAL 於2004年7月至2016年6月任務期間全球性的觀測高空短暫發光現象，共紀錄了292,248筆強閃電事件，並以人工判讀辨識出48,537筆高空短暫發光事件。這些觀測資料深化了對於高空短暫發光現象的了解。然而，傳統人工判讀方法易因人為判斷標準不一致與判讀經驗的差異而產生誤差，尤其是多類型事件與弱發光事件。本研究運用深度學習技術，建立並訓練神經網路模型，系統性辨識出 66,586 筆事件，建立高空短暫發光現象事件資料庫用於後續的時空特徵研究。
    本研究首度結合 ISUAL 高空短暫發光現象事件資料庫與地面閃電觀測網絡 (World Wide Lightning Location Network, WWLLN) 的強閃電分布，克服了ISUAL觀測所無法涵蓋的中高緯度與相鄰軌道間空窗區域，成功建立完整的全球高空短暫發光現象發生率地圖。校正後的全球高空短暫發光現象發生率達到每分鐘 47.08 次。其中各類型高空短暫發光現象的每分鐘發生率分別為：淘氣精靈(elves) 32.74、紅色精靈(sprites) 5.94、精靈暈盤(halos) 2.98、藍色噴流(blue jets) 5.40 與 巨大噴流(gigantic jets) 0.02。相較於過往的研究，本研究推估之高空短暫發光現象發生率大幅提升約一個數量級，這項新的統計結果將對於現有全球大氣電路模型與大氣化學模型中，高空短暫發光現象所反映之強閃電活動的貢獻與影響需重新評估。
    此外，本研究依據柯本氣候分類 (Köppen classification) 系統性分析不同氣候環境下高空短暫發光現象發生率之變化。結果顯示，高空短暫發光現象在熱帶與溫帶地區的發生率較高，其中紅色精靈尤其集中於炎熱夏季型氣候區。為進一步探討外在因素的可能影響，本研究首次將完整的 11 年太陽活動週期與夜間高空短暫發光現象發生率進行相關性分析，結果顯示其相關性僅為弱相關(R ≤ 0.3)，顯示氣象因素而非太陽活動更可能是主導高空短暫發光現象年際變化的主要驅動機制。

    The Imager of Sprites and Upper Atmospheric Lightning (ISUAL) onboard the FORMOSAT-2 satellite was designed to investigate Transient Luminous Events (TLEs) occurring between the troposphere and ionosphere. From July 2004 to June 2016, the ISUAL instrument recorded 292,248 intense lightning events, from which 48,537 TLEs were manually identified. These observations have substantially advanced our understanding of TLEs. However, manual classification is susceptible to biases arising from inconsistent judgment criteria and variations in observer experience, particularly for multi-type or dim events. In this study, deep learning techniques were employed to develop and train convolutional neural network models that systematically identified 66,586 TLEs events. The event list provides a more consistent and comprehensive foundation for subsequent analyses of the spatiotemporal characteristics of TLEs.
    To overcome the observational gaps at mid- to high latitudes and the inter-track gaps between adjacent ISUAL ground tracks. This study first constructed a bias-corrected TLEs occurrence density map through the fusion of ISUAL TLEs observations and World Wide Lightning Location Network (WWLLN) intense lightning distributions. The global TLEs occurrence rate is 47.08 events per minute, with individual rates of elves (32.74), sprites (5.94), halos (2.98), blue jets (5.40), and gigantic jets (0.02). Compared with existing studies, the occurrence rate estimated in this work is approximately one order of magnitude higher, indicating that the current global atmospheric electric circuit and atmospheric chemistry models may need to re-evaluate the role of intense lightning activity reflected by TLEs observations.
    Furthermore, TLEs occurrence rates were examined across different climate zones using the Köppen classification system. Higher rates were found in tropical and temperate climates, with sprites particularly frequent in hot summer regions. To further examine potential external drivers, this study for the first time analyzed a full 11-year solar cycle activity in relation to nighttime TLEs occurrence, revealing only a weak correlation (R ≤ 0.3) and favoring meteorological factors instead of solar activity as the primary drivers of TLEs variability.

    摘要 i Abstract iii Acknowledgment v Table of Contents vi List of Tables viii List of Figures x Chapter 1 Introduction 1 1.1 Transient Luminous Events 1 1.2 Scientific Significance of Global TLEs Research 5 1.2.1 Atmospheric Chemistry and Energy Deposition 5 1.2.2 Global Electric Circuit 8 1.2.3 Climatic and Solar Influences on TLEs Activity 10 1.3 Previous Studies on Global TLEs Occurrence Rates 12 1.4 Motivation 15 Chapter 2 ISUAL Instrumentation and Database 18 2.1 ISUAL Payload Configuration 19 2.2 ISUAL Observation 24 2.3 ISUAL TLEs Database and Manual Identification Event List 27 Chapter 3 Deep Learning Methodology for TLEs Analysis 32 3.1 Fundamentals of Deep Learning and Neural Networks 32 3.1.1 Artificial Intelligence, Machine Learning, and Deep Learning 33 3.1.2 Machine Learning Workflow and Training Challenges 35 3.1.3 Convolutional Neural Network Architecture 38 3.2 ISUAL Neural Network Model for Image Recognition 41 3.3 ISUAL Event List by Deep Learning Method 44 3.3.1 Reclassification of the Event List 44 3.3.2 Representative Case Studies 46 3.4 Localization of Newly Identified TLEs 53 Chapter 4 Fusion of WWLLN Lightning Observations and ISUAL TLEs List 54 4.1 Preliminary Analysis of TLEs Distributions 55 4.2 ISUAL Observational Constraints and Coverage Limitations 59 4.3 Derivation of Global TLEs Occurrence Maps by WWLLN Fusion 63 Chapter 5 Spatial Distribution of TLEs 73 5.1 Global Occurrence Density of TLEs 74 5.2 Occurrence Density across Köppen Climate Zones 84 5.2.1 Overview of the Köppen Climate Classification 85 5.2.2 TLEs Occurrence Across Major Climate Groups 92 5.2.3 TLEs Occurrence Across Seasonal Precipitation Types 95 5.2.4 TLEs Occurrence Across Thermal Subtypes 98 Chapter 6 Temporal Variations of TLEs 101 6.1 Seasonal Variations of TLEs 102 6.2 Solar Activity and TLEs Variability 105 6.3 ENSO and Interannual Variability of TLEs 112 Chapter 7 Conclusion and Future Work 118 7.1 Conclusion 118 7.2 Dual-band Imager of Atmospheric Transient (DIAT) 121 References 126 Appendix A. Deep Learning Framework and Implementation Details 134 A.1 Model for Image Recognition 134 A.1.1 Computational Environment and Implementation 134 A.1.2 Model Structure 137 A.1.3 Preprocessing 140 A.1.4 Training and Evaluation Strategy 144 A.2 Localization and Geophysical Projection of Newly Identified TLEs 146 A.2.1 Estimation of TLEs Position 148 A.2.2 Parent Lightning Centroids 150 A.2.3 Geolocation and Coordinate Mapping 153 Appendix B. Regional Dependence of the Solar–TLEs Relationship 155

    1. Adachi, T., H. et al., Electric field transition between the diffuse and streamer regions of sprites estimated from ISUAL/array photometer measurements, Geophys. Res. Lett., 33, L17803, (2006). https://doi.org/10.1029/2006GL026495.
    2. Arbib, M. A., The handbook of brain theory and neural networks (2nd ed.), Cambridge, MA: MIT Press. (2003).
    3. Arnone, E., J. et al., Climatology of transient luminous events and lightning observed above Europe and the Mediterranean Sea, Surv. Geophys., (2020). https://doi.org/10.1007/s10712-019-09573-5.
    4. Ball, J. E., Anderson, D. T., & Chan, C. S. A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools, and Challenges for the Community. Journal of Applied Remote Sensing, 11, 042609. (2017). https://doi.org/10.48550/arXiv.1709.00308.
    5. Barrington-Leigh, C. P., U. S. Inan, and M. Stanley, Identification of sprites and elves with intensified video and broadband array photometry, J. Geophys. Res., 106, 1741–1750. (2001).
    6. Bering, E. A.III, et al., Observations of transient luminous events (TLEs) associated with negative cloud to ground (−CG) lightning strokes, Geophys. Res. Lett., 31, L05104, (2004). https://doi.org/10.1029/2003GL018659.
    7. Bjørge Engeland, et al., Terrestrial Gamma Ray Flashes with accompanying Elves detected by ASIM. Journal of Geophysical Research: Atmospheres, 127, e2021JD036368. (2022). https://doi.org/10.1029/2021JD036368.
    8. Blaes, P. R., R. A. Marshall, and U. S. Inan, Global occurrence rate of elves and ionospheric heating due to cloud-to-ground lightning, J. Geophys. Res. Space Physics, 121, 699–712, (2016). https://doi.org/10.1002/2015JA021916.
    9. Blanc, E., et al., Nadir observations of sprites from the International Space Station, J. Geophys. Res., 109, A02306, (2004). https://doi.org/10.1029/2003JA009972.
    10. Brasseur, G.P. and Solomon, S. Aeronomy of the middle atmosphere. Springer, Dordrecht, The Netherlands. (2005).
    11. Chang, S. C., C. L. Kuo, L. J. Lee, A. B. Chen, H. T. Su, R. R. Hsu, H. U. Frey, S. B. Mende, Y. Takahashi, and L. C. Lee, ISUAL far-ultraviolet events, elves, and lightning current, J. Geophys. Res., 115, A00E46, (2010). https://doi.org/10.1029/2009JA014861.
    12. Chen, A. B., H.-T. Su, and R.-R. Hsu , Energetics and geographic distribution of elve-producing discharges, J. Geophys. Res. Space Physics, 119, 1381–1391, (2014). https://doi.org/10.1002/2013JA019470.
    13. Chen, A. B., R.-R. Hsu, C.-L. Kuo, H.-T. Su, L.-C. Lee, S. B. Mende, H. U. Frey, H. Fukunishi, and Y. Takahashi , Global distributions and occurrence rates of transient luminous events, J. Geophys. Res., 113, A08306, (2008). https://doi.org/10.1029/2008JA013101.
    14. Chern, J. L., R. R. Hsu, H. T. Su, S. B. Mende, H. Fukunishi, Y. Takahashi, and L. C. Lee, Global survey of upper atmospheric transient luminous events on the ROCSAT-2 satellite, J. Atmos. Terr. Phys., 65, 647–659, (2003). https://doi.org/10.1016/S1364-6826(02)00317-6.
    15. Chou, J. K., et al., Gigantic jets with negative and positive polarity streamers, J. Geophys. Res. Space Physics, 115, A00E45, (2010). https://doi.org/10.1029/2009JA014831.
    16. Chou, J. K., L. Y. Tsai, C. L. Kuo, Y. J. Lee, C. M. Chen, A. B. Chen, H. T. Su, R. R. Hsu, P. L. Chang, and L. C. Lee, Optical emissions and behaviors of the blue starters, blue jets, and gigantic jets observed in the Taiwan transient luminous event ground campaign, J. Geophys. Res., 116, A07301, (2011). https://doi.org/10.1029/2010JA016162.
    17. Christian, H. J., et al., Global frequency and distribution of lightning as observed from space by the Optical Transient Detector, J. Geophys. Res., 108(D1), 4005, (2003) https://doi.org/10.1029/2002JD002347.
    18. Chronis, T. G. Investigating possible links between incoming cosmic ray fluxes and lightning activity over the United States. Journal of Climate, 22, 5748–5754. (2009). https://doi.org/10.1175/2009JCLI2912.1
    19. Chuang, C.-W., and A. B. Chen, Global distribution and spectral features of intense lightning by the ISUAL experiment, J. Geophys. Res. Atmospheres, 127, e2022JD036473, (2022). https://doi.org/10.1029/2022JD036473.
    20. Chum, J., Langer, R., Kolmašová, I., Lhotka, O., Rusz, J., and Strhárský, I. Solar cycle signatures in lightning activity, Atmos. Chem. Phys., 24, 9119–9130, (2024). https://doi.org/10.5194/acp-24-9119-2024.
    21. Ciresan, D., U. Meier, and J. Schmidhuber, Multi-column deep neural networks for image classification, Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 3642–3649, (2012). https://doi.org/10.1109/CVPR.2012.6248110.
    22. DiGangi, E., Lapierre, J., Stock, M., Hoekzema, M., & Cunha, B. Analyzing lightning characteristics in Central and Southern South America. Electric Power Systems Research, 213, 108704. (2022). https://doi.org/10.1016/j.epsr.2022.108704
    23. Dwyer, J. R., Uman, M. A., Rassoul, H. K., Al-Dayeh, M., Caraway, L., Jerauld, J., ... & Hill, D. Energetic radiation produced during rocket-triggered lightning. Science, 299, 694–697. (2003). https://doi.org/10.1126/science.1078940
    24. Franz, R. C., R. J. Nemzek, and J. R. Winckler, Television image of a large upward electrical discharge above a thunderstorm system, Science, 249, 48–51. (1990).
    25. Fukunishi, H., Y. Takahashi, T. Adachi, R. R. Hsu, H. T. Su, A. B. Chen, S. B. Mende, H. U. Frey, and L. C. Lee, Observations of sprites and elves with the ISUAL array photometer, AGU Fall Meeting Abstracts, 51, Abstract AE51A-04. (2004).
    26. Gerken, E. A., and U. S. Inan, Comparison of photometric measurements and charge moment estimations in two sprite-producing storms, Geophys. Res. Lett., 31, L03107, (2004). https://doi.org/10.1029/2003GL018751.
    27. Goodfellow, I., et al. Deep Learning. MIT Press, Cambridge, M.A. (2016). http://www.deeplearningbook.org
    28. Gordillo-Vázquez F. J, Pérez-Invernón F. J., A review of the impact of transient luminous events on the atmospheric chemistry: Past, present, and future, Atmospheric Research, Volume 252, 105432, ISSN 0169-8095, (2021). https://doi.org/10.1016/j.atmosres.2020.105432.
    29. Gordillo-Vázquez, F. J., A. Luque, and M. Simek, Near infrared and ultraviolet spectra of TLEs, J. Geophys. Res., 117, A05329, (2012). https://doi.org/doi:10.1029/2012JA017516.
    30. Gupta, V., V. K. Mishra, P. Singhal, and A. Kumar, An overview of supervised machine learning algorithm, in Proc. 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India, 87–92, (2022) https://doi.org/10.1109/SMART55829.2022.10047618.
    31. Gurevich, A. V., Milikh, G. M., and Roussel-Dupre, R., “Runaway electron mechanism of air breakdown and preconditioning during a thunderstorm”, Physics Letters A, vol. 165, no. 5–6, Elsevier, pp. 463–468, (1992). https://doi.org/10.1016/0375-9601(92)90348-P.
    32. Hayes, L. A., Gallagher, P. T., McCauley, J., Dennis, B. R., Ireland, J., & Inglis, A. Pulsations in the Earth's lower ionosphere synchronize with solar flare emission. Journal of Geophysical Research: Space Physics, 122, 9841–9847. (2017). https://doi.org/10.1002/2017JA024647.
    33. Hayes, Laura & O'Hara, Oscar & Murray, Sophie & Gallagher, Peter. Solar Flare Effects on the Earth's Lower Ionosphere. (2021). https://doi.org/10.48550/arXiv.2109.06558.
    34. Haykin, S., Neural Networks and Learning Machines (3rd ed.), Pearson Education. (2008)
    35. Hsu, R.-R., A. B. Chen, C.-L. Kuo, H.-T. Su, H. U. Frey, S. B. Mende, Y. Takahashi, and L.-C. Lee, On the global occurrence and impacts of transient luminous events (TLEs), AIP Conf. Proc., 1118(1), 99–107, (2009). https://doi.org/10.1063/1.3137720.
    36. Hutchins, M. L., R. H. Holzworth, J. B. Brundell, and C. J. Rodger, Relative detection efficiency of the World Wide Lightning Location Network, Radio Sci., 47, RS6005, (2012). https://doi.org/10.1029/2012RS005049.
    37. Inan, U. S., T. F. Bell, and J. V. Rodriguez, Heating and ionization of the lower ionosphere by lightning, Geophys. Res. Lett., 18, 705–708. (1991).
    38. Inan, U. S., W. A. Sampson, and Y. N. Taranenko, Space-time structure of optical flashes and ionization changes produced by lightning-EMP, Geophys. Res. Lett., 23, 133–136, (1996). https://doi.org/10.1029/95GL03816.
    39. Inan, U. S., S. A. Cummer, and R. A. Marshall, A survey of ELF and VLF research on lightning-ionosphere interactions and causative discharges, J. Geophys. Res., 115, A00E36, (2010). https://doi.org/10.1029/2009JA014775.
    40. Krizhevsky, A., Sutskever, I., & Hinton, G., ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105. (2012).
    41. Kuo, C.L., et al, Electric fields and electron energies inferred from the ISUAL recorded sprites, Geophys. Res. Lett., 32, L19103, (2005). https://doi.org/10.1029/2005GL023389
    42. Kuo, C. L., et al. Modeling elves observed by FORMOSAT-2 satellite, J. Geophys. Res., 112, A11312, (2007). https://doi.org/10.1029/2007JA012407.
    43. Kuo, C. L., A. B. Chen, J. K. Chou, L. Y. Tsai, R. R. Hsu, H. T. Su, H. U. Frey, S. B. Mende, Y. Takahashi, and L. C. Lee , Radiative emission and energy deposition in transient luminous events, J. Phys. D Appl. Phys., 41, 234014, (2008). https://doi.org/10.1088/0022-3727/41/23/234014.
    44. Kuo, C. L., J. K. Chou, L. Y. Tsai, A. B. Chen, H. T. Su, R. R. Hsu, S. A. Cummer, H. U. Frey, S. B. Mende, Y. Takahashi, L. C. Lee., Discharge processes, electric field, and electron energy in ISUAL-recorded gigantic jets, J. Geophys. Res. (Space Physics), 114, A04314, (2009). https://doi.org/10.1029/2008JA013791.
    45. Kuo, C. L., Su, H. T., Hsu, R. R., & Lee, L.-C., The blue luminous events observed by ISUAL payload onboard FORMOSAT 2 satellite. Journal of Geophysical Research: Space Physics, 120(11), 9795–9805. (2015). https://doi.org/10.1002/2015JA021386.
    46. Kuo, C. L., Huang, T. Y., Hsu, C. M., Sato, M., Lee, L. C., & Lin, N.-H. Resolving elve, halo and sprite halo images at 10,000 fps in the Taiwan 2020 campaign. Atmosphere, 12(8), Article 1000. (2021). https://doi.org/10.3390/atmos12081000.
    47. Lee, L. J., et al., Controlling synoptic-scale factors for the distribution of transient luminous events, J. Geophys. Res., 115, A00E54, (2010). https://doi.org/10.1029/2009JA014823.
    48. Lee, L. J., S.-M. Huang, J.-K. Chou, C.-L. Kuo, A. B. Chen, H.-T. Su, R.-R. Hsu, H. U. Frey, Y. Takahashi, and L.-C. Lee, Characteristics and generation of secondary jets and secondary gigantic jets, J. Geophys. Res., 117, A06317, (2012). https://doi.org/10.1029/2011JA017443.
    49. Liu, N., V. P. Pasko, D. H. Burkhardt, H. U. Frey, S. B. Mende, H.-T. Su, A. B. Chen, R.-R. Hsu, L.-C. Lee, H. Fukunishi, and Y. Takahashi, Comparison of results from sprite streamer modeling with spectrophotometric measurements by ISUAL instrument on FORMOSAT-2 satellite, Geophys. Res. Lett., 33, L01101, (2006). https://doi.org/10.1029/2005GL024243.
    50. Lyons, Walter & Andersen, Laura & Nelson, Thomas & Huffines, G., Characterisics of sprite-producing electrical storms in the steps 2000 domain. 86th AMS Annual Meeting. (2006).
    51. Martineau, P., Nakamura, H., and Kosaka, Y.: Influence of ENSO on North American subseasonal surface air temperature variability, Weather Clim. Dynam., 2, 395–412, (2021). https://doi.org/10.5194/wcd-2-395-2021.
    52. Maurya, A. K., Parihar, N., Dube, A., Singh, R., Kumar, S., Chanrion, O., Tomičić, M., & Neubert, T. Rare observations of sprites and gravity waves supporting D‑, E‑, F‑region ionospheric coupling. Scientific Reports, 12, 581. (2022). https://doi.org/10.1038/s41598-021-03808-5.
    53. Mende, S. B., H. U. Frey, R. R. Hsu, H. T. Su, A. B. Chen, L. C. Lee, D. D. Sentman, Y. Takahashi, and H. Fukunishi, D region ionization by lightning-induced electromagnetic pulses, J. Geophys. Res. (Space Physics), 110, A11312, (2005). https://doi.org/10.1029/2005JA011064.
    54. Neubert, T. et al., Observation of the onset of a blue jet into the stratosphere. Nature. 589. 371-375. (2021). https://doi.org/10.1038/s41586-020-03122-6.
    55. Newsome, R. T., and U. S. Inan, Free‐running ground‐based photometric array imaging of transient luminous events, J. Geophys. Res., 115, A00E41, (2010). https://doi.org/10.1029/2009JA014834.
    56. Nicoll, K. A. Space weather influences on atmospheric electricity. Weather, 69, 238–241. (2014). https://doi.org/10.1002/wea.2323
    57. Pasko, V. P., and J. J. George, Three-dimensional modeling of blue jets and blue starters, J. Geophys. Res., 107(A12), 1458, (2002). https://doi.org/10.1029/2002JA009473.
    58. Pasko, V. P., U. S. Inan, T. F. Bell, and Y. N. Taranenko, Sprites produced by quasi-electrostatic heating and ionization in the lower ionosphere, J. Geophys. Res., 102, 4529–4562, (1997). https://doi.org/10.1029/96JA03528.
    59. Peel, M. C., Finlayson, B. L., and McMahon, T. A. Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644. (2007). https://doi.org/10.5194/hess-11-1633-2007
    60. Pérez-Invernón, F. J., Luque, A., & Gordillo-Vázquez, F. J. Modeling the chemical impact and the optical emissions produced by lightning-induced electromagnetic fields in the upper atmosphere: The case of halos and elves triggered by different lightning discharges. Journal of Geophysical Research: Atmospheres, 123, 7615–7641. (2018). https://doi.org/10.1029/2017JD028235
    61. Peterson, H., M. Bailey, J. Hallett, and W. Beasley, NOx production in laboratory discharges simulating blue jets and red sprites, J. Geophys. Res., 114, A00E07, (2009). https://doi.org/10.1029/2009JA014489.
    62. Pinto Jr., O., Pinto, I. and Neto, O. Lightning Enhancement in the Amazon Region Due to Urban Activity. American Journal of Climate Change, 2, 270-274. (2013). http://dx.doi.org/10.4236/ajcc.2013.24026
    63. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. Deep learning and process understanding for data-driven Earth system science. Nature, 566, 195–204. (2019). https://doi.org/10.1038/s41586-019-0912-1
    64. Rodger, C. J., Werner, S., Brundell, J. B., Lay, E. H., Thomson, N. R., Holzworth, R. H., and Dowden, R. L. Detection efficiency of the VLF World-Wide Lightning Location Network (WWLLN): initial case study, Ann. Geophys., 24, 3197–3214, (2006). https://doi.org/10.5194/angeo-24-3197-2006
    65. Rodger, C. J., A. Seppälä, and M. A. Clilverd, Significance of transient luminous events to neutral chemistry: Experimental measurements, Geophys. Res. Lett., 35, L07803, (2008). https://doi.org/10.1029/2008GL033221.
    66. Rudlosky, S. D., & Fuelberg, H. E. Pre- and postupgrade distributions of NLDN reported cloud-to-ground lightning characteristics in the contiguous United States. Monthly Weather Review, 138, 3623–3633. (2010). https://doi.org/10.1175/2010MWR3283.1
    67. Rycroft, M. J., and A. Odzimek, Effects of lightning and sprites on the ionospheric potential, and threshold effects on sprite initiation, obtained using an analog model of the global atmospheric electric circuit, J. Geophys. Res., 115, A00E37, (2010). https://doi.org/10.1029/2009JA014758.
    68. Rycroft, M.J., Nicoll, K.A., Aplin, K.L., Recent advances in global electric circuit coupling between the space environment and the troposphere. Journal of Atmospheric and Solar-Terrestrial Physics, 90–91, 198–211. (2012). https://doi.org/10.1016/j.jastp.2012.03.015.
    69. Sato, M., and H. Fukunishi, Global sprite occurrence locations and rates derived from triangulation of transient Schumann resonance events, Geophys. Res. Lett., 30, 1859, (2003). https://doi.org/10.1029/2003GL017291
    70. Sato, M, T. et al., Overview and early results of the Global Lightning and Sprite Measurements mission: Overview and early results of GLIMS. Journal of Geophysical Research Atmospheres. 120. 3822–3851. (2015). https://doi.org/10.1002/2014JD022428.
    71. Stephan, A. W., Blanc, E., Neubert, T., & Farges, T. Sprite produced by consecutive impulse charge transfers following a negative stroke: Observation and simulation. Journal of Geophysical Research: Atmospheres, 121(8), 4082–4092. (2016). https://doi.org/10.1002/2015JD024644.
    72. Stringfellow, M. Lightning incidence in Britain and the solar cycle. Nature 249, 332–333 (1974). https://doi.org/10.1038/249332a0
    73. Su, H. T., R. R. Hsu, A. B. Chen, Y. C. Wang, W. S. Hsiao, W. C. Lai, L. C. Lee, M. Sato, and H. Fukunishi (2003), Gigantic jets between a thundercloud and the ionosphere, Nature, 423, 974–976, https://doi.org/10.1038/nature01759.
    74. Su, H.-T., R.-R. Hsu, A. B.-C. Chen, Y.-J. Lee, and L.-C. Lee, Observation of sprites over the Asian continent and over oceans around Taiwan, Geophys. Res. Lett., 29(4), 3-1–3-4, (2002). https://doi.org/10.1029/2001GL013737.
    75. Wescott, E. M., D. Sentman, D. Osborne, D. Hampton, and M. Heavner Preliminary results from the Sprites94 aircraft campaign: 2. Blue jets, Geophys. Res. Lett., 22, 1209–1212, (1995). https://doi.org/10.1029/95GL00582.
    76. Williams, E. R. The global electrical circuit: A review. Atmospheric Research, 91(2–4), 140–152. (2009). https://doi.org/10.1016/j.atmosres.2008.05.018
    77. Wu, Y. J., et al. (2012), Occurrence of elves and lightning during El Niño and La Niña, Geophys. Res. Lett., 39, L03106, https://doi.org/10.1029/2011GL049831.
    78. Yair, Y., P. Israelevich, A. D. Devir, M. Moalem, C. Price, J. H. Joseph, Z. Levin, B. Ziv, A. Sternlieb, and A. Teller, New observations of sprites from the space shuttle, J. Geophys. Res., 109, D15201, (2004). https://doi.org/10.1029/2003JD004497.

    QR CODE