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高原气象  2018, Vol. 37 Issue (4): 1051-1060    DOI: 10.7522/j.issn.1000-0534.2017.00088
论文     
基于卫星资料的中国西北地区冰云特征分析
林彤1, 郑有飞1,2,3, 李特1, 王立稳1
1. 南京信息工程大学大气物理学院, 江苏 南京 210044;
2. 江苏省大气环境监测与污染控制高技术研究重点实验室, 江苏 南京 210044;
3. 南京信息工程大学大气物理与大气环境学院, 江苏 南京 210044
The Characteristics of Ice Cloud Properties Derived from Satellite Data in Northwest China
LIN Tong1, ZHENG Youfei1,2,3, LI Te1, WANG Liwen1
1. The School of Atmospheric Physics, Nanjing University of Information & Technology, Nanjing 210044, Jiangsu, China;
2. Jiang Su Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing 210044, Jiangsu, China;
3. The School of Atmospheric Physics, Nanjing University of Information & Technology, Nanjing 210044, Jiangsu, China
 全文: PDF 
摘要: 利用2012年12月至2016年11月A-Train卫星编队中CloudSat卫星与CALIPSO卫星融合产品DARDAR数据对我国西北地区冰云发生概率的水平和季节分布特征,冰水含量和冰云有效半径的垂直分布进行了分析。结果表明,近4年西北地区平均冰云发生率为55.1%,春季冰云出现较多,除河西-内蒙古中西部部分地区外,春季冰云发生概率均在70%以上,季节性变化明显。2015年和2016年四季变化浮动比前两年大,四季冰云发生率在西北地区的沙漠区域相对较小,由于特殊地形的影响冰云发生率的高值区出现在青藏高原东北部;青藏高原东北部冰云发生率在春、秋冬季都较大,夏季最小。冰水含量在13 km以上几乎没有冷冰水分布,夏季最大且垂直方向5 km高度下几乎没有分布,秋冬季节冰水含量相对较小,西北各地区之间冰水含量的差异较大,在西北东部季风区分布最少;西北各地区冰云有效半径分布与其冰水含量分布趋势相似,春夏季大,秋冬季小,其中夏季冰云有效半径在5 km高度以下几乎没有分布,由于夏季温度较高,在37°N-39°N冰水含量较大的区域冰云有效粒子半径相对较小。
关键词: 冰云冰云发生概率冰水含量冰云有效半径    
Abstract: The Northwest China probability distribution of ice cloud occurrence frequency, ice water content and ice cloud effective radius were presented based on DARDAR data coalesce from Cloud-Aerosols Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat Satellite which come from A-Train Satellite formation during the period from December 2012 to November 2016, the distribution and vertical structures were discussed and analyzed. The results show that, the occurrence frequency of ice cloud is 55.1% over the last four years and the occurrence frequency of ice cloud in spring is mostly greater than 70% apart from the Midwest area of Hexi-Inner Mongolia, it has obvious seasonal variation. The amplitude of season variation is bigger in 2015 and 2016 than the first two years, and ice cloud occurrence frequency generally small in the desert area. The high occurrence frequency of ice cloud area is in the northeastern part of the Qinghai-Tibetan Plateau because of the special terrain; The occurrence frequency of ice cloud is both bigger in spring, autumn and winter but only smaller in summer at the northeastern of the Qinghai-Tibetan Plateau, because the warm and humid air from the Indian Ocean is blocked by the southern mountain of the plateau, which leads to a lower occurrence frequency of ice cloud in the northeastern of plateau in summer. The ice water content is almost has no ice water distribution above 13 km, ice water content in Northwest China is the biggest in summer and almost has no ice water distribution below 5 km because of the high temperature in summer that make ice cloud not easy to form in the lower floors, and the ice water content is generally smaller in autumn and winter. The difference of ice water content between different regions in northwest of China is relatively large; Ice cloud effective radius and ice water content had a similar trend in the whole of Northwest China area and five different sections, which are bigger in spring and summer and smaller in winter and autumn; Especially in summer, ice effective radius is generally larger, but ice cloud effective radius is relatively small in the numerical value of ice water content area 37°N-39°N because of the higher temperature in summer. The difference of the Ice cloud effective radius between different regions in Northwest China is relatively larger, the Ice cloud effective radius is minimum in The east seasonal wind areas of Northwest China and biggest in Northern Xinjiang and the midwest of Qilian Mountain-Qinghai area.
Key words: Ice cloud    ice cloud occurrence frequency    ice water content    effective radius
收稿日期: 2017-08-23 出版日期: 2018-08-22
:  P401  
基金资助: 国家科学自然基金项目(41590873)
通讯作者: 郑有飞(1959-),男,江苏人,教授,主要从事环境气象与气候变化方面的研究.E-mail:zhengyf@nuist.edu.cn     E-mail: zhengyf@nuist.edu.cn
作者简介: 林彤(1993-),女,山东人,硕士研究生,主要从事大气物理学方面的研究.E-mail:lintong_nuist@126.com
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林彤, 郑有飞, 李特, 王立稳. 基于卫星资料的中国西北地区冰云特征分析[J]. 高原气象, 2018, 37(4): 1051-1060.

LIN Tong, ZHENG Youfei, LI Te, WANG Liwen. The Characteristics of Ice Cloud Properties Derived from Satellite Data in Northwest China. Plateau Meteorology, 2018, 37(4): 1051-1060.

链接本文:

http://www.gyqx.ac.cn/CN/10.7522/j.issn.1000-0534.2017.00088        http://www.gyqx.ac.cn/CN/Y2018/V37/I4/1051

Brown P R A, Francis P N, 1995. Improved measurements of the ice water content in cirrus using a total-water probe[J]. J Atmos Ocean Technol, 12(2):1139-1145.
Chen B, Liu X, 2005. Seasonal migration of cirrus clouds over the Asian Monsoon regions and the Tibetan Plateau measured from MODIS/Terra[J]. Geophys Res Lett, 32(1):L01804. DOI:10.1029/2004GL020868.
Delanoë J, Hogan R J, 2008. A variational scheme for retrieving ice cloud properties from combined radar, lidar, and infrared radiometer[J]. J Geophys Res, 113(D7):1829-1836.
Delanoë J, Hogan R J, 2010. Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds[J]. J Geophys Res, 115(4):1307-1314.
Eliasson S, Buehler S A, Milz M, et al, 2010. Assessing observed and modelled spatial distributions of ice water path using satellite data[J]. Atmos Chem Phys, 11(1):375-391.
Eliasson S, Holl G, Buehler S A, et al, 2013. Systematic and random errors between collocated satellite ice water path observations[J]. J Geophys Res Atmos, 118(6):2629-2642.
Foot J S, 1988. Some observations of the optical properties of clouds. I:Stratocumulus[J]. Quart J Roy Meteor Soc, 114(479):129-144.
Francis P N, Hignett P, Macke A, 1998. The retrieval of cirrus cloud properties from aircraft multi-spectral reflectance measurements during EUCREX'93[J]. Quart J Roy Meteor Soc, 124:1273-1291. DOI:10.1002/qj.49712454812.
Heymsfield A J, Protat A, Bouniol D, et al, 2008. Testing IWC retrieval methods using radar and ancillary measurements with in situ data[J]. J Appl Meteor Climatol, 47(1):135-163.
Hogan R J, 2006. Fast approximate calculation of multiply scattered lidar returns[J]. Applied Optics, 45(23):5984-5992.
Hong Y, Liu G, 2015. The characteristics of ice cloud properties derived from CloudSat and CALIPSO measurements[J]. J Climate, 28(9):3880-3901.
Huang J, Lin B, Minnis P, et al, 2006. Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia[J]. Geophys Res Lett, 33(19):L19802. DOI:10.1029/2006GL026561.
Liou K N, 1986. Influence of cirrus clouds on weather and climate processes:A global perspective[J]. Mon Wea Rev, 114(6):1167-1199.
Liou K N, Bohren C, 1980. An introduction to atmospheric radiation[J]. Physics Today, 26(7):66-67.
Liu Z, Omar A H, Hu Y, et al, 2005. CALIOP algorithm theoretical basis document-Part 3:Scene classification algorithms[R/OL]. Release 1.0, PC-SCI-202, NASA Langley Research Center, Hampton, VA, 56.[2017-04-22]. http://www-calipso.larc.nasa.gov/resources/pdfs/PC-SCI-202_Part3_v1.0.pdf.
Lynch D K, Sassen K, Del Genio A, et al, 2002, Cirrus[M]. Oxford:Oxford University Press.
Mcfarquhar G M, Heymsfield A J, Spinhirne J, et al, 1999. Thin and subvisualtropopause tropical cirrus:Observations and radiative impacts[J]. J Atmos Sci, 57(12):1841-1853.
Meyer K, Yang P, Gao B C, 2007. Tropical ice cloud optical depth, ice water path, and frequency fields inferred from the MODIS level-3 data[J]. Atmos Res, 85(2):171-182.
Ockertbell M E, Hartmann D L, 1992. The effect of cloud type on earth's energy balance:Results for selected regions[J]. J Climate, 5(10):1157-1171.
Sassen K, Wang Z, 2008. Classifying clouds around the globe with the CloudSat radar:1-year of results[J]. Geophys Res Lett, 35(4):228-236.
Stein T H M, Delano J, Hogan R J, 2011. A comparison among four different retrieval methods for ice-cloud properties using data from CloudSat, CALIPSO, and MODIS[J]. J Appli Meteor Climatol, 50(9):1952-1969.
Tian W, Tian H, Dhomse S, et al, 2011. A study of upper troposphere and lower stratosphere water vapor above the Tibetan Plateau using AIRS and MLS data[J]. Atmos Sci Lett, 12(2):233-239.
Wu D L, Austin R T, Deng M, et al, 2009. Comparisons of global cloud ice from MLS, CloudSat, and correlative data sets[J]. J Geophys Res, 2009, 114(D8):D00A24. DOI:10.1029/2008JD009946.
Wu D L, Jiang J H, Davis C P, 2006. EOS MLS cloud ice measurements and cloudy-sky radiative transfer model[J]. IEEE Trans Geosci Remote Sens, 44(5):1156-1165.
Wyser K, 1998. The effective radius in ice clouds[J]. J Climate, 11(7):1793-1802.
陈超, 孟辉, 靳瑞军, 等, 2014. 基于CloudSat云分类资料的华北地区云宏观特征分析[J]. 气象科技, 42(2):294-301. Chen C, Meng H, Jin R J, et al, 2014. Cloud macroscopic characteristics over North China based on CloudSat data[J]. Meteor Sci Technol, 42(2):294-301.
陈勇航, 陈艳, 黄建平, 等, 2007. 中国西北地区云的分布及其变化趋势[J]. 高原气象, 26(4):741-748. Chen Y H, Chen Y, Huang J P, et al, 2007. Distribution and variation trend of cloud over Northwestern China[J]. Plateau Meteor, 26(4):741-748.
陈勇航, 黄建平, 陈长和, 等, 2005a. 西北地区空中云水资源的时空分布特征[J]. 高原气象, 24(6):905-912. Chen Y H, Huang J P, Chen C H, et al, 2005a. Temporal and spatial distributions of cloud water resources over Northwestern China[J]. Plateau Meteor, 24(6):905-912.
陈勇航, 黄建平, 王天河, 等, 2005b. 西北地区不同类型云的时空分布及其与降水的关系[J]. 应用气象学报, 16(6):717-727. Chen Y H, Huang J P, Wang T H, et al, 2005b. Temporal and spatial distributions of the different clouds over Northwestern China with the relation to precipitation[J]. J Appli Meteor, 16(6):717-727.
霍娟, 2015. 基于 CloudSat/CALIPSO资料的海陆上空中云的物理属性分析[J]. 气候与环境研究, 20 (1):30-40. Huo J, 2015. Physical properties of mid-level clouds based on CloudSat/CALIPSO data over land and sea[J]. Climatic Environ Res, 20 (1):30-40.
刘建军, 陈葆德, 2017. 基于CloudSat卫星资料的青藏高原云系发生频率及其结构[J]. 高原气象, 36(3):632-642. Liu J J, Chen B D, 2017. Cloud occurrence frequency and structure over the Qinghai-Tibetan Plateau from CloudSat observation[J]. Plateau Meteor, 36(3):632-642. DOI:10.7522/j. issn. 1000-0534.2017.00028.
刘瑞霞, 刘玉洁, 杜秉玉, 2004. 中国云气候特征的分析[J]. 应用气象学报, 15(4):468-476. Liu R X, Liu Y J, Du B Y, 2004. Cloud climatology characteristics of China from ISCCP data[J]. J Appli Meteor, 15(4):468-476.
闵敏, 王普才, 宗雪梅, 2011. 中国地区卷云分布特征的星载激光雷达遥感[J]. 气候与环境研究, 16(3):301-309. Min M, Wang P C, Zong X M, 2011. Cirrus cloud distribution over China from Spaceborne Lidar observations[J]. Climatic Environ Res, 16(3):301-309.
彭杰, 沈新勇, 王志立, 等, 2010. 中国地区云的观测研究进展[J]. 安徽农业科学, 38(24):13070-13073. Peng J, Shen X Y, Wang Z L, et al, 2010. Overview of observational researches on cloud over China[J]. Anhui Agricultural Sciences, 38(24):13070-13073.
杨冰韵, 张华, 彭杰, 等, 2014. 利用 CloudSat 卫星资料分析云微物理和光学性质的分布特征[J]. 高原气象, 33(4):1105-1118. Yang B Y, Zhang H, Peng J, et al, 2014. Analysis on global distribution characteristics of cloud microphysical and optical properties based on the CloudSat data[J]. Plateau Meteor, 33(4):1105-1118. DOI:10.7522/j. issn. 1000-0534.2013.00026.
叶培龙, 王天河, 尚可政, 等, 2014. 基于卫星资料的中国西部地区云垂直结构分析[J]. 高原气象, 33(4):977-98. Ye P L, Wang T H, Shang K Z, et al, 2014. Analysis of cloud vertical structure over Western China based on active satellite data[J]. Plateau Meteor, 33(4):977-98. DOI:10.7522/j. issn. 1000-0534.2013.00158.
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