使用向日葵八號觀測資料探討大氣深對流發展期之雲頂特性

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：39

、訪客IP：18.119.126.80

姓名

劉宜真(Yi-Chen Liu) 查詢紙本館藏

畢業系所

大氣科學學系

論文名稱

使用向日葵八號觀測資料探討大氣深對流發展期之雲頂特性
(Investigate the Cloud Top Features of Developing Atmospheric Deep Convection from Himawari-8)

相關論文

★ 地球同步衛星觀測資料之雲區像素辨識	★ 結合掩星折射率與高光譜紅外線觀測之大氣溫溼度垂直剖面反演
★ 結合衛星反演資料與WRF模式探討梅雨鋒面水氣傳送關聯性之個案研究	★ Optimal Use of Satellite Sounding Products for Numerical Weather Prediction
★ The spatial correlation of satellite-estimated PM2.5 and epidemiological diseases in Taiwan	★ Assessment of the NWP Model Physical Fields from Radiative Quantity
★ 海表面風場與通量於熱帶氣旋發展影響之探討	★ 使用衛星資料評析全球預報模式之雲參數特性
★ 衛星輻射強度與反演產品之資料同化研究－－尼伯特颱風(2016)個案分析	★ 日本氣象同步衛星 Himawari-8 向日葵八號之雲微物理參數反演驗證與評估
★ 掩星資料於颱風快速增強機制之模擬研究－梅姬颱風(2010)	★ 利用多頻道衛星觀測評估WRF數值模式於不同微物理方案之雲特性:以梅雨鋒面降水系統個案為例
★ 應用多時期向日葵8號衛星影像進行雲像素的偵測與追蹤	★ 使用CloudSat及ECMWF再分析資料探討南海及海洋大陸地區深對流之環境因子
★ 使用 CloudSat 分析南海與海洋大陸地區之深對流與動力環境特徵	★ 印尼地區地表性質與雲特徵之探討

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2030-12-31以後開放)

摘要(中)

對流系統對於全球氣候扮演著重要的角色，除了影響水文循環、地球輻射收支與水氣及化學物質垂直傳輸混合等，而對流系統中的大氣深對流亦常在地面造成豪大雨、強風等劇烈天氣現象，致使快速發展的深對流系統常為災害天氣的主要原因。由於大氣對流生命週期十分短暫且發展快速，因此本研究使用地球同步衛星向日葵八號(Himawari-8)觀測，針對2017年6月於南海與臺灣地區發展中大氣對流雲頂特性進行分析。
利用對流雲頂高度於衛星觀測區間內的改變，可以估算雲頂垂直發展速度，發現其主要集中在0-4 m s -1，於對流系統發展最強時，可達到10 m s -1以上，且當雲頂高度低於10公里時，隨著雲頂高度向上發展垂直速度也增強。此外對流發展時雲光學厚度發展集中在0-20之間，隨著雲頂高度發展，雲光學厚度值分佈減小，南海相較臺灣對流雲更有機會發展大的光學厚度。兩區域雲滴粒徑都主要分佈在20-30微米，其中南海地區當雲頂高度發展尚在8公里以下時，雲滴粒徑分佈相對較大顆。進一步探討雲參數間的關係，雲頂垂直速度越強，對流雲越不易發展大的雲光學厚度，且不利於大顆雲滴生成。
於所區分對流雲發展的三階段中，無論是臺灣或南海地區，在對流肇始後(Post-CI)階段，大的垂直速度分佈頻率最高，此階段是對流發展最強的時候。而在對流雲生成初期，南海與臺灣地區光學厚度值都集中在20以下，至Post-CI 階段，南海相較於臺灣，光學厚度大於30的對流雲發生頻率會較高。此外南海在對流發展初期，相較臺灣地區整體雲滴粒徑較大，而進入Post-CI階段後隨著雲頂高度增加雲滴粒徑減小，而在臺灣地區雲滴粒徑都集中在20-30微米區間，直到Post-CI階段大顆(>60微米)雲滴的出現頻率才增多。

摘要(英)

Convective system plays an important role in the global climate, including hydrological cycle, radiative budget and vertical energy transportation. It is usually associated with severe weather hazards, particularly deep convective clouds. Due to the fast development and short lifecycle, it is hard to understand the characteristics of convections. The research studies development of the deep convective cloud in South China Sea (SCS) and Taiwan during June 2017 from high spatial-temporal geostationary satellite Himawari-8. We track and select convective cases by detecting minimum brightness temperature from 11 µm infrared channel as the cumulus center. Besides, to investigate the difference in the characteristics of the cloud top from generation to mature, we could separate developing convective lifetime into three stages, including Pre-CI, CI and mature stages by using convective initiation signal.
The cloud top vertical velocities (CTW) estimated by calculating the change of the cloud top height (CTH) are found to be clustered 0-4m s -1 and could reach more than 10 m s -1. Besides, the value of CTW tends to increase with height when CTH below 10 km. On the mature stage, no matter in SCS or Taiwan, the frequency of large value of CTW is highest. The cloud optical thickness (COT) is mainly distributed between 0-20, and with the increase of CTH, the higher occurrence frequency shows lower value of COT. Compared in Taiwan, the higher value of COT (> 30) occurs frequently in SCS. Furthermore, the cloud effective radius (Re) is mainly distributed between 20-30 µm. When the CTH is lower than 8 km, the Re in SCS would be larger than in Taiwan, and on mature stage, the Re decreases with development of CTH. In Taiwan, the larger Re more than 60 µm is frequently. Further analysis, the stronger the CTW, it is hard for convective clouds to develop larger COT and RE.

關鍵字(中)

★ 對流雲
★ 向日葵八號
★ 雲微物理參數
★ 對流生命週期

關鍵字(英)

★ convective cloud
★ Advanced HimawariImager
★ cloud microphysical property
★ convective life cycle

論文目次

目錄
摘要 I
ABSTRACT II
表目錄 IV
圖目錄 IV
第一章介紹 1
1.1前言 1
1.2文獻回顧 2
1.3研究目的 8
第二章資料與方法 9
2.1衛星資料(HIMAWARI-8) 9
2.2雲微物理參數 10
2.3選取區域 11
2.4對流雲個案追蹤與挑選 12
2.5對流肇始訊號偵測 14
第三章發展中對流雲雲頂特性 16
3.1對流發展時段 16
3.2雲頂垂直速度 18
3.3雲光學厚度 19
3.4雲滴有效粒徑 20
3.5雲光學厚度與雲頂垂直速度 21
3.6雲滴有效粒徑與雲頂垂直速度 22
第四章不同發展階段對流雲雲頂特性 24
4.1雲頂垂直速度 25
4.2雲光學厚度 28
4.3雲滴有效粒徑 30
4.4雲水路徑 31
4.5雲頂參數交叉分析 34
第五章總結與展望 37
5.1總結 37
5.2未來展望 40
參考文獻 41

參考文獻

Adler, Robert F., and Douglas D. Fenn, 1979 : Thunderstorm intensity as determined from satellite data. Journal of Applied Meteorology 18.4: 502-517.
Bedka, K. M., Wang, C., Rogers, R., Carey, L. D., Feltz, W., & Kanak, J., 2015. Examining deep convective cloud evolution using total lightning, WSR-88D, and GOES-14 super rapid scan datasets. Weather and Forecasting, 30(3), 571-590.
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., ... & Okuyama, A., 2016. An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. Journal of the Meteorological Society of Japan. Ser. II, 94(2), 151-183.
Chang, H. L., Brown, B. G., Chu, P. S., Liou, Y. C., & Wang, W. H., 2017. Nowcast guidance of afternoon convection initiation for Taiwan. Weather and Forecasting, 32(5), 1801-1817.
Ding, Y., Li, C., He, J., Chen, L., Gan, Z., Qian, Y., ... & Xu, J., 2006. South China Sea monsoon experiment (SCSMEX) and the East Asian monsoon. Acta Meteorologica Sinica, 20(2), 159.
Dodson, J. B., D. A. Randall, and K. Suzuki, 2013: Comparison of observed and simulated tropical cumuliform clouds by CloudSat and NICAM. Journal of Geophysical Research: Atmospheres, 118, 1852-1867.
Done, J., Davis, C. A., & Weisman, M., 2004. The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecasting (WRF) model. Atmospheric Science Letters, 5(6), 110-117.
Feingold, G., Boers, R., Stevens, B., & Cotton, W. R., 1997. A modeling study of the effect of drizzle on cloud optical depth and susceptibility. Journal of Geophysical Research: Atmospheres, 102(D12), 13527-13534.
Garstang, M., and A. K. Betts, 1974: a review of the tropical boundary layer and cumulus convection: structure, parameterization, and modeling. Bulletin of the American Meteorological Society, 55, 1195-1205.
Hamada, A, & Takayabu, YN., 2016. Convective Cloud Top Vertical Velocity Estimated from Geostationary Satellite Rapid‐Scan Measurements. Geophysical Research Letters, 43(10), 5435-5441
Heymsfield, G. M., Tian, L., Heymsfield, A. J., Li, L., & Guimond, S., 2010. Characteristics of deep tropical and subtropical convection from nadir-viewing high-altitude airborne Doppler radar. Journal of the Atmospheric Sciences, 67(2), 285-308.
Jensen, M. P., Petersen, W. A., Bansemer, A., Bharadwaj, N., Carey, L. D., Cecil, D. J., ... & Giangrande, S. E. (2016). The midlatitude continental convective clouds experiment (MC3E). Bulletin of the American Meteorological Society, 97(9), 1667-1686.
Lee, S., Han, H., Im, J., Jang, E., & Lee, M. I., 2017: Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data. Atmos. Meas. Tech., 10, 1859–1874.
Lin, P. F., Chang, P. L., Jou, B. J. D., Wilson, J. W., & Roberts, R. D., 2011. Warm season afternoon thunderstorm characteristics under weak synoptic-scale forcing over Taiwan Island. Weather and forecasting, 26(1), 44-60.
Liu, C.-Y., Kuo, S. C., Lim, A. H., Hsu, S. C., Tseng, K. H., Yeh, N. C., & Yang, Y. C., 2016. Optimal use of space-borne advanced infrared and microwave soundings for regional numerical weather prediction. Remote Sensing, 8(10), 816.
Liu, C.-Y., Chiu, C-H., Lin, P-H., Min, M. 2020: Comparison of Cloud-Top Property Retrievals from Advanced Himawari Imager, MODIS, CloudSat/CPR, CALIPSO/ CALIOP, and radiosonde. Journal of Geophysical Research: Atmospheres, doi:10.1029/2020JD032683 (accepted).
Lucas, C., Zipser, E. J., & Lemone, M. A., 1994. Vertical velocity in oceanic convection off tropical Australia. Journal of the atmospheric sciences, 51(21), 3183-3193.
Luo, ZJ, Jeyaratnam, J, Iwasaki, S, Takahashi, H, & Anderson, R., 2014. Convective Vertical Velocity and Cloud Internal Vertical Structure: An a‐Train Perspective. Geophysical Research Letters, 41(2), 723-729.
Maddox, R. A., 1980: Mesoscale convective complexes. Bull. Amer. Meteor. Soc., 61, 1374–1387, doi:10.1175/1520-0477(1980)061,1374: MCC.2.0.CO;2
Mecikalski, J. R. and Bedka, K. M., 2006: Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery, Mon. Weather Rev., 134, 49–78, doi:10.1175/MWR3062.1.
Mecikalski, J. R., MacKenzie, W. M., Koenig, M., and Muller, S., 2009: Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part I: Infrared Fields, J. Appl. Meteorol. Climatol., 49, 521–534, doi:10.1175/2009JAMC2344.1.
Mecikalski, J. R., MacKenzie, W. M., König, M., and Muller, S., 2010: Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part II: Use of Visible Reflectance, J. Appl. Meteorol. Climatol., 49, 2544–2558, doi:10.1175/2010JAMC2480.1.
Mecikalski, J. R., Watts, P. D., & Koenig, M., 2011: Use of Meteosat Second Generation optimal cloud analysis fields for understanding physical attributes of growing cumulus clouds. Atmospheric research, 102(1-2), 175-190.
Roberts, R. D., and S. Rutledge, 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562–584
Senf, F., Dietzsch, F., Hünerbein, A., & Deneke, H., 2015: Characterization of initiation and growth of selected severe convective storms over central Europe with MSG-SEVIRI. Journal of Applied Meteorology and Climatology, 54(1), 207-224.
Senf, F., & Deneke, H., 2017: Satellite-based characterization of convective growth and glaciation and its relationship to precipitation formation over central Europe. Journal of Applied Meteorology and Climatology, 56(7), 1827-1845.
Sieglaff, J. M., Cronce, L. M., Feltz, W. F., Bedka, K. M., Pavolonis, M. J., & Heidinger, A. K., 2011: Nowcasting convective storm initiation using satellite-based box-averaged cloud-top cooling and cloud-type trends. Journal of applied meteorology and climatology, 50(1), 110-126.
Sieglaff, J. M., Cronce, L. M., & Feltz, W. F., 2014: Improving satellite-based convective cloud growth monitoring with visible optical depth retrievals. Journal of Applied Meteorology and Climatology, 53(2), 506-520.
Spiridonov, V., Baez, J., & Telenta, B., 2017: Heavy convective rainfall forecast over Paraguay using coupled WRF-cloud model. In Perspectives on Atmospheric Sciences (pp. 183-189). Springer, Cham.
Wang, B., Zhang, Y., & Lu, M. M., 2004: Definition of South China Sea monsoon onset and commencement of the East Asia summer monsoon. Journal of Climate, 17(4), 699-710.
Wu, Q., Wang, H. Q., Zhuang, Y. Z., Lin, Y. J., Zhang, Y., & Ding, S. S., 2016: Correlations of multispectral infrared indicators and applications in the analysis of developing convective clouds. Journal of Applied Meteorology and Climatology, 55(4), 945-960.
Zhou Xin, Zhou Shunwu, Qin Danyu, et al, 2019: Analysis of Cloud Top Features During Convective Initiation Using FY-2F Satellite Scan Data. Meteorological Monthly, 45(2): 216-227. DOI: 10.7519/j.issn.1000-0526.2019.02.007.

指導教授

劉千義(Chian-Yi Liu)

審核日期

2020-7-28

推文