博碩士論文 105621021 詳細資訊




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:14 、訪客IP:34.228.229.51
姓名 林妤晨(Yu-Chen Lin)  查詢紙本館藏   畢業系所 大氣科學學系
論文名稱 使用CloudSat與GSMaP觀測資料探討大氣深對流之雲與降水特性
(Characteristics of the Atmospheric Deep Convective Cloud and Precipitation from CloudSat and GSMaP)
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 (2024-12-31以後開放)
摘要(中) 雲與降水均在水文循環中扮演重要角色,然在十大雲種中,積雨雲對水文循環有更直接的影響,一旦持續發展形成降水後,則易為短延時之強降雨進而造成災害。前人研究中,已知在不同的降水形態下,雲的垂直結構及雲微物理均呈現不同的特性。因此本研究將嘗試以衛星觀測資料,來瞭解大氣深對流中之雲核心(DCC)結構,分別探討其雲的微物理性質與地面降水的關係,以及大氣熱力動力狀態。
本研究使用2014年6月~2016年5月之24個月全球CloudSat/CPR與日本JAXA GSMaP全球降水產品,將二者進行時空匹配後,發現全球有三大主要DCC好發區(東南亞地區(SEA)、非洲中部(AF)及亞馬遜盆地((AZ)。研究結果指出DCC的發展高度與降雨率呈正相關,此外在對流層中,冰晶數量濃度(INC)、冰晶有效半徑(IRe)、相對溼度(RH)以及垂直上升氣流的垂直分佈顯示參數值越明顯時會有較大的降雨率,這表明這些雲微物理參數及相關大氣狀態能為直接或間接影響降雨率的敏感因素。
分析在對流層5-10 km間之中層高度DCC結構後發現,發生強降水的DCC,其IRe粒徑大於無降水的粒徑,且降雨率和IRe之間的顯著敏感高度區間在7至9 km。同時也發現,RH的變化也會影響粒徑和降雨率,並且於此高度區間,當較大雲滴粒徑(IRe>120 μm)的佔比大於50%時,發生強降雨率的機率為最高。於對流層13-14 km間之高度區間中,INC與降雨率則具有關連。於此高度中,當INC>450 L-1時容易出現大雨,而INC<400 L-1時則容易為弱降水甚至無降水情形。
更進一步分析全球三大好發區的資料後發現,DCC環境特性具有區域性環境特徵,其中SEA因海陸地形影響,水氣量豐沛,上升運動旺盛,因此DCC之環境特性會比其他兩區域更強。而SEA於對流層5-8 km高度其大冰晶粒徑分布比AF和AZ所佔比例較大;對流層高層中,冰晶濃度大小隨降雨率開始變化分界點於SEA為12 km,AF和AZ則為11 km。
摘要(英) Both clouds and precipitation play an important role in the hydrological cycle. Among the ten major cloud species, Cumulonimbus (Cb) might have strong relationship between its internal structure and precipitation. In the development of Cbs, it is typically associated with the formation of severe precipitation. Therefore, it leads several natural disasters like flash flooding and lightening with intense rainfall in a short duration. From previous study, both the vertical structure and microphysical properties of cloud present different characteristics in the various types of precipitation. This study will focus on the use of satellite observation data to understand the relationship between the structure of deep convective core (DCC) and the microphysical properties of cloud and precipitation.
We analyzed 24 months global collocated CloudSat/ CPR, JAXA GSMaP precipitation data to identify three frequent DCC regions. The results show the developing height of DCC has positive correlation with the rain rate. The vertical distribution of the ice number concentration (INC), ice effective radius (IRe), relative humidity (RH) along with vertical updraft velocity in the troposphere reveal the larger values that are associated the heavier rain rate, which suggests these are important and sensitive factors to the severe rain rate.
In the mid-level of troposphere (5-10 km in altitudes), the representative cloud parameter is the IRe. The particles size of heavy precipitation is larger than non-precipitation. The biggest difference between the rain rate and IRe is the most significant in the altitudes between 7 and 9 km. At the same time, the change in RH affects the particle size and rain rate as well. At this height, the percentage of IRe > 120 μm is greater than 50 %, and there is a higher probability of heavy rain rate occurrence; in the upper level is the INC. The height of 13-14 km is the key to discuss the INC and rain rate. When the INC > 450 L-1 is prone to heavy rain rate, while INC < 400 L-1 is prone to weak precipitation or non-precipitation. As a result, the vertical velocity, RH, convective height, IRe, INC and precipitation are known to be closely related to this study.
After further analysis of the data from the three DCC developing regions in the world, it was found that the environmental characteristics of DCC have regional environmental characteristics. Among them, SEA is affected by the shape of the sea and the land, water vapor is abundant, and the updraft is strong, therefore, the environmental characteristics of DCC will be stronger than the other two regions. SEA has a large IRe distribution ratio of AF and AZ in 5-8 km. In the upper troposphere, the INC begins to change with rain rate which is 12 km in SEA, and 11 km in AF and AZ.
關鍵字(中) ★ 深對流雲核心
★ 降水
★ 衛星觀測
★ 雲垂直結構
關鍵字(英) ★ Deep Convective Core (DCC)
★ Precipitation
★ Satellite observations
★ Cloud vertical structure
論文目次 摘要 I
Abstract II
表目錄 VI
圖目錄 VII
List of Abbreviations X
第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 2
1.3 研究目的 6
第二章 資料介紹與研究方法 7
2.1 CloudSat衛星資料 7
2.2 GSMaP 10
2.3 ECMWF ERA-Interim 10
2.4 研究方法 11
2.4.1. 參數定義 12
2.4.2. 資料蒐集及匹配處理方式 13
第三章 深對流核心結構與降水特徵分析 17
3.1 等頻率高度圖(CFAD)分析 17
3.1.1 無降水之DCC特徵 19
3.1.2 不同降水率之DCC特徵 20
3.2 雲頂高度之延展 21
3.3 大氣總可降水量 23
3.4 雲參數之垂直分布 25
3.4.1 雲冰晶數量濃度垂直分布 25
3.4.2 雲冰晶有效半徑垂直分布 27
3.5 大氣環境之動力熱力參數 28
3.5.1 垂直運動速度(w) 28
3.5.2 環境相對溼度(RH) 29
3.5.3 輻合/輻散場. 30
3.6 DCC於中、高對流層之雲微物理特性 31
3.6.1 高對流層 (Upper Troposphere) 31
3.6.2 中對流層 (Mid-Troposphere) 37
3.7 小結 39
第四章 全球好發區域之分析與探討 40
4.1 雲微物理及其結構之垂直分布 42
4.1.1 CFADs結構 42
4.1.2 冰晶數量濃度 44
4.1.3 冰晶有效半徑 45
4.2 DCC於中、高對流層之雲微物理特性 46
4.2.1冰晶有效半徑 46
4.2.2冰晶數量濃度 47
4.3 大氣環境場與熱力動力狀態之區域差異 49
4.3.1 TPW 49
4.3.2水氣分層狀態 52
4.3.3垂直運動速度 57
4.4 小結 60
第五章 結論及未來展望 61
參考文獻 65
參考文獻 [ 1] 邓军英, 丁明月, 王文彩, 光莹, 陈勇航, 辛渝, . . . 朱曦. (2016). 冰云粒子微物理属性在一次强降雨过程中的垂直分布 ⓪. ARID LAND GEOGRAPHY, 39(1).
[ 2] 邓军英, 邱昀, 陈勇航, 杨莲梅, 何清, & 张萍. (2014). 强降雨过程中冰云粒子等效半径的垂直分布及其与降水的相关性. 自然灾害学报, 23(2), 120-129.
[ 3] Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., . . . Bolvin, D. (2003). The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). Journal of hydrometeorology, 4(6), 1147-1167.
[ 4] Austin, R. (2007). Level 2B radar-only cloud water content (2B-CWC-RO) process description document. CloudSat project report, 5, 1-26.
[ 5] Austin, R. T., Heymsfield, A. J., & Stephens, G. L. (2009). Retrieval of ice cloud microphysical parameters using the CloudSat millimeter‐wave radar and temperature. Journal of Geophysical Research: Atmospheres, 114(D8).
[ 6] Battaglia, A., & Simmer, C. (2008). How does multiple scattering affect the spaceborne W-band radar measurements at ranges close to and crossing the sea-surface range? IEEE transactions on geoscience and remote sensing, 46(6), 1644-1651.
[ 7] Berg, W., Kummerow, C., & Morales, C. A. (2002). Differences between east and west Pacific rainfall systems. Journal of climate, 15(24), 3659-3672.
[ 8] Bodas‐Salcedo, A., Webb, M., Brooks, M., Ringer, M., Williams, K., Milton, S., & Wilson, D. (2008). Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities. Journal of Geophysical Research: Atmospheres, 113(D8).
[ 9] Chua, Z. W., Kuleshov, Y., & Watkins, A. (2020). Evaluation of Satellite Precipitation Estimates over Australia. Remote Sensing, 12(4), 678.

[ 10] Dodson, J. B., Taylor, P. C., & Branson, M. (2018). Microphysical variability of Amazonian deep convective cores observed by CloudSat and simulated by a multi-scale modeling framework.
[ 11] Hamada, A., Murayama, Y., & Takayabu, Y. N. (2014). Regional characteristics of extreme rainfall extracted from TRMM PR measurements. Journal of Climate, 27(21), 8151-8169.
[ 12] Hamada, A., Takayabu, Y. N., Liu, C., & Zipser, E. J. (2015). Weak linkage between the heaviest rainfall and tallest storms. Nature communications, 6, 6213.
[ 13] Haynes, J. M., L′Ecuyer, T. S., Stephens, G. L., Miller, S. D., Mitrescu, C., Wood, N. B., & Tanelli, S. (2009). Rainfall retrieval over the ocean with spaceborne W‐band radar. Journal of Geophysical Research: Atmospheres, 114(D8).
[ 14] Hong, Y., Liu, G., & Li, J. L. (2016). Assessing the radiative effects of global ice clouds based on CloudSat and CALIPSO measurements. Journal of Climate, 29(21), 7651-7674.
[ 15] Im, E., Wu, C., & Durden, S. L. (2005, May). Cloud profiling radar for the CloudSat mission. In IEEE International Radar Conference, 2005. (pp. 483-486). IEEE.
[ 16] Jakob, C., & Klein, S. A. (1999). The role of vertically varying cloud fraction in the parametrization of microphysical processes in the ECMWF model. Quarterly Journal of the Royal Meteorological Society, 125(555), 941-965.
[ 17] Jiang, J. H., Su, H., Zhai, C., Perun, V. S., Del Genio, A., Nazarenko, L. S., . . . Cole, J. (2012). Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A‐Train” satellite observations. Journal of Geophysical Research: Atmospheres, 117(D14).
[ 18] Kawamoto, K. (2006). Relationships between cloud properties and precipitation amount over the Amazon basin. Atmospheric research, 82(1-2), 239-247.
[ 19] Kubar, T. L., & Hartmann, D. L. (2008). Vertical structure of tropical oceanic convective clouds and its relation to precipitation. Geophysical research letters, 35(3).
[ 20] Kubota, T., Shige, S., Hashizume, H., Aonashi, K., Takahashi, N., Seto, S., . . . Nakagawa, K. (2007). Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: Production and validation. IEEE transactions on geoscience and remote sensing, 45(7), 2259-2275.
[ 21] Lau, W. K., Kim, K. M., Chern, J. D., Tao, W. K., & Leung, L. R. (2020). Structural changes and variability of the ITCZ induced by radiation–cloud–convection–circulation interactions: inferences from the Goddard Multi-scale Modeling Framework (GMMF) experiments. Climate Dynamics, 54(1-2), 211-229.
[ 22] Liou, K. N. (1986). Influence of cirrus clouds on weather and climate processes: A global perspective. Monthly Weather Review, 114(6), 1167-1199.
[ 23] Liu, C., Zipser, E. J., & Nesbitt, S. W. (2007). Global distribution of tropical deep convection: Different perspectives from TRMM infrared and radar data. Journal of climate, 20(3), 489-503.
[ 24] Luo, Y., Zhang, R., Qian, W., Luo, Z., & Hu, X. (2011). Intercomparison of deep convection over the Tibetan Plateau–Asian monsoon region and subtropical North America in boreal summer using CloudSat/CALIPSO data. Journal of climate, 24(8), 2164-2177.
[ 25] Luo, Z., Liu, G. Y., & Stephens, G. L. (2008). CloudSat adding new insight into tropical penetrating convection. Geophysical research letters, 35(19).
[ 26] Luo, Z. J., Anderson, R. C., Rossow, W. B., & Takahashi, H. (2017). Tropical cloud and precipitation regimes as seen from near‐simultaneous TRMM, CloudSat, and CALIPSO observations and comparison with ISCCP. Journal of Geophysical Research: Atmospheres, 122(11), 5988-6003.
[ 27] Marchand, R., Mace, G. G., Ackerman, T., & Stephens, G. (2008). Hydrometeor detection using CloudSat—An Earth-orbiting 94-GHz cloud radar. Journal of Atmospheric and Oceanic Technology, 25(4), 519-533.
[ 28] Mega, T., Ushio, T., Kubota, T., Kachi, M., Aonashi, K., & Shige, S. (2014, August). Gauge adjusted global satellite mapping of precipitation (GSMaP_Gauge). In 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS) (pp. 1-4). IEEE.
[ 29] Nam, C. C., & Quaas, J. (2012). Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data. Journal of climate, 25(14), 4975-4992.
[ 30] Ning, S., Song, F., Udmale, P., Jin, J., Thapa, B. R., & Ishidaira, H. (2017). Error analysis and evaluation of the latest GSMap and IMERG precipitation products over Eastern China. Advances in Meteorology, 2017.
[ 31] Petersen, W. A., Carey, L. D., Rutledge, S. A., Knievel, J. C., Doesken, N. J., Johnson, R. H., . . . Weaver, J. F. (1999). Mesoscale and radar observations of the Fort Collins flash flood of 28 July 1997. Bulletin of the American Meteorological Society, 80(2), 191-216.
[ 32] Posselt, D. J., Heever, S. V. D., Stephens, G., & Igel, M. R. (2012). Changes in the interaction between tropical convection, radiation, and the large-scale circulation in a warming environment. Journal of climate, 25(2), 557-571.
[ 33] Sassen, K., Matrosov, S., & Campbell, J. (2007). CloudSat spaceborne 94 GHz radar bright bands in the melting layer: An attenuation‐driven upside‐down lidar analog. Geophysical research letters, 34(16).
[ 34] Sassen, K., & Wang, Z. (2008). Classifying clouds around the globe with the CloudSat radar: 1‐year of results. Geophysical research letters, 35(4).
[ 35] Satoh, M., Inoue, T., & Miura, H. (2010). Evaluations of cloud properties of global and local cloud system resolving models using CALIPSO and CloudSat simulators. Journal of Geophysical Research: Atmospheres, 115(D4).
[ 36] Smith, J. A., Baeck, M. L., Morrison, J. E., & Sturdevant-Rees, P. (2000). Catastrophic rainfall and flooding in Texas. Journal of hydrometeorology, 1(1), 5-25.

[ 37] Smith, J. A., Baeck, M. L., Steiner, M., & Miller, A. J. (1996). Catastrophic rainfall from an upslope thunderstorm in the central Appalachians: The Rapidan storm of June 27, 1995. Water Resources Research, 32(10), 3099-3113.
[ 38] Sohn, B., Choi, M., & Ryu, J. (2015). Explaining darker deep convective clouds over the western Pacific than over tropical continental convective regions. Atmospheric Measurement Techniques, 8(11), 4573.
[ 39] Stephens, G. L. (2005). Cloud feedbacks in the climate system: A critical review. Journal of climate, 18(2), 237-273.
[ 40] Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., . . . Durden, S. L. (2002). The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation. Bulletin of the American Meteorological Society, 83(12), 1771-1790.
[ 41] Stephens, G. L., & Wood, N. B. (2007). Properties of tropical convection observed by millimeter-wave radar systems. Monthly weather review, 135(3), 821-842.
[ 42] Ushio, T., Sasashige, K., Kubota, T., Shige, S., Okamoto, K. i., Aonashi, K., . . . Kachi, M. (2009). A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. Journal of the Meteorological Society of Japan. Ser. II, 87, 137-151.
[ 43] Weare, B. C. (2000). Insights into the importance of cloud vertical structure in climate. Geophysical research letters, 27(6), 907-910.
[ 44] Witkowski, M. M., Vane, D., & Livermore, T. (2018). CloudSat-Life in Daylight Only Operations (DO-Op). Paper presented at the 2018 SpaceOps Conference.
[ 45] Yan, Y.-F., Wang, X.-C., & Liu, Y.-M. (2018). Cloud vertical structures associated with precipitation magnitudes over the Tibetan Plateau and its neighboring regions. Atmospheric and Oceanic Science Letters, 11(1), 44-53.

[ 46] Yin, J., Wang, D., Zhai, G., & Wang, Z. (2013). Observational characteristics of cloud vertical profiles over the continent of East Asia from the CloudSat data. Acta Meteorologica Sinica, 27(1), 26-39.
[ 47] Yuter, S. E., & Houze Jr, R. A. (1995). Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Monthly weather review, 123(7), 1941-1963.
[ 48] Zhang, M., Lin, W., Klein, S., Bacmeister, J., Bony, S., Cederwall, R., . . . Lohmann, U. (2005). Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. Journal of Geophysical Research: Atmospheres, 110(D15).
[ 49] Zipser, E. J. (1994). Deep cumulonimbus cloud systems in the tropics with and without lightning. Monthly weather review, 122(8), 1837-1851.
指導教授 劉千義(Chian-Yi Liu) 審核日期 2020-4-7
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明