中尺度强暴雨云团云特征的多种卫星资料综合分析 mobilesport365_365游戏盒子

姓名
邮箱
手机号码
标题
留言内容
验证码

中尺度强暴雨云团云特征的多种卫星资料综合分析

1.
国家卫星mobilesport365_365游戏盒子_28365备用网址官方网站中心, 北京 100081
2.
北京大学物理学院大气科学系, 北京 100871

资助项目:

教育部博士站基金项目 20020001027

国家自然科学基金项目 49794030

国家重点基础研究项目 2004CB418300

计量
- 摘要浏览量: 7710
- HTML全文浏览量: 3618
- PDF下载量: 7940
- 被引次数: 0
出版历程
- 收稿日期: 2006-04-20
- 修回日期: 2006-11-01
- 刊出日期: 2007-04-30

Case Study on Cloud Properties of Heavy Rainfall Based upon Satellite Data

1.
National Satellite Meteorological Center, Beijing 100081
2.
Department of Atmospheric Science, Peking University, Beijing 100871

摘要

摘要: 针对2002年6月23—27日发生于江淮地区的一次中尺度强暴雨过程，利用FY-1D，EOS和NOAA卫星的可见光、红外、微波通道遥感观测、反演资料，从相态、光学厚度、垂直结构等各方面分析云特征，并将分析结果与同时段地面雨量观测进行对比分析，发现云光学厚度大且云顶粒子为大粒子、冰相态是此次降雨过程中云团的主要云特征，地面雨量的大小与云光学厚度密切相关，两者间基本呈正相关关系；稳定少变的大光学厚度云或云光学厚度显着增大均可带来强的地面降水。微波资料可以很好地体现降雨云团的垂直结构。分析结果显示，卫星遥感对揭示中尺度强暴雨云团的云特征，具有很好的指示作用。
- 云相态;
- 光学厚度;
- 微波;
- 强暴雨
Abstract: Heavy rainfall is one of meteorological disasters in China. Precipitation has complex spatial and temporal distribution. It is difficult to get 3 dimension information by regular observation method. Remote sensing is a kind of useful ways to monitor precipitation. Weather satellites have high space and time resolution which are becoming a kind of important measurements. Cloud is one of key factors to produce precipitation. To do research on heavy rainfall, it is needed to understand cloud properties well.It is important to investigate the relationship between clouds and strong precipitations and try to reveal kinds of cloud properties which can produce heavy rainfall by satellite data. Visible, infrared and microwave data of FY-1D, EOS and NOAA satellites are used to analyze cloud properties of the heavy rainfall case. Visible and infrared radiance data have high spatial resolution, and can be used to show the detailed property information of small convective cloud. But properties of cloud on visible and infrared channel don't have clear relationship with precipitation. In the atmosphere, rain particles have stronger_effect than clouds on microwave. Rain particles also absorb and re-emit more radiance than cloud particles. Compared with visible and infrared data, microwave data can show the structure properties of strong precipitation cloud, although its spatial resolution is low. So it is very helpful to monitor 3 dimension properties of convective cloud if these different kinds of data can be combined together. Cloud phase, cloud optical thickness and cloud vertical structure are selected as analysis physical parameters. Using visible and infrared data, cloud phase and optical thickness can be retrieved. Microwave data can be directly obtained from NOAA/AMSU data. Precipitation data are also used to validate the analysis results.A heavy rainfall happens in Huaihe River drainage area from June 23 to 27, 2002. This precipitation process is selected to study the relationship between cloud properties and heavy precipitation. Combined data(cloud phase, cloud optical thickness, microwave data and surface precipitation data)are used. Case study shows that clouds are mainly made up of ice clouds or large water cloud particles with thick cloud optical thickness when heavy rain happens. When cloud optical thickness increases or cloud optical thickness maintains large value during 6 hours, strong precipitation occurs on the surface. There exists a good positive relationship between precipitation and cloud optical thickness. The stronger the precipitation, the thicker the cloud optical thickness, or the more precipitation, the larger optical thickness difference during 6 hours. Clouds with stable large optical thickness or with great optical thickness increase will produce strong precipitation on the surface during a period of time. Different microwave channel data show different information coming from different atmosphere layer. When heavy rain appears, different microwave channel data could show strong convective structure from low layer to high layer of atmosphere. Analysis results show that cloud properties of heavy rain are shown better when different kinds of satellite remote sensing data are combined together.
- cloud phase;
- optical thickness;
- microwave;
- heavy rainfall

HTML全文

图 1 2002年6月25日FY-1D, EOS, NOAA卫星云相态识别图像

（图上叠加卫星观测所在时段内小时雨量观测值r）

Fig. 1 Cloud phase images of FY-1D, EOS and NOAA on June 25, 2002

(hourly precitiation r in situ is overlapped)

下载: 全尺寸图片幻灯片

图 2 FY-1D(a)和NOAA(b)卫星反演的云光学厚度图像

(图上叠加卫星观测所在时段内小时雨量观测值r)

Fig. 2 Retrieved cloud optical thickness images of FY-1D(a)and NOAA(b)

(hourly precipitation r in stiu is overlapped)

下载: 全尺寸图片幻灯片

图 3 地面雨量与云光学厚度间的统计关系

Fig. 3 The statistical relationship between precipitation and cloud optical thickness

下载: 全尺寸图片幻灯片

图 4 6 h内云光学厚度变化合成图像

（红色：NOAA卫星反演的光学厚度；绿色：FY-1D与NOAA卫星反演的光学厚度差值；蓝色：FY-1D卫星反演的光学厚度；图像上叠加6 h雨量值）

Fig. 4 Composed image of 6-hour cloud optical thickness change

(red:retrieved NOAA cloud optical thickness; green:the difference between retrieved FY-1D and NOAA cloud optical thickness; blue:retrieved FY-1D cloud optical thickness; 6 h precipitation is overlapped)

下载: 全尺寸图片幻灯片

图 5 2002年6月25日NOAA/AMSU-B 4个微波通道亮度温度图像

Fig. 5 Four microwave channel images of NOAA/AMSU-B on June 25, 2002

下载: 全尺寸图片幻灯片

图 6 116°E经向剖线上像元在183. 3±1 GHz, 183. 3±3 GHz, 183.3±7 GHz和89 GHz通道的亮温分布

Fig. 6 Brightness temperature distribution of pix els at 183. 3±1 GHz, 183.3±3 GHz, 183. 3±7 GHz和89 GHz along 116°E

下载: 全尺寸图片幻灯片

图 7 NOAA16/AMSU 89 GHz与云光学厚度合成图像

(图上叠加卫星观测所在时段内小时雨量观测值r)

Fig. 7 Composed image containing NOAA16/AMSU 89 GHz and cloud optical thickness

(hourly precipitation r in situ is overlapped)

下载: 全尺寸图片幻灯片

表 1 AMSU-B光谱通道特征及其主要探测目的

Table 1 Spectral properties and main purposes of AMSU-B

参考文献(10)

[1]	刘健, 许健民, 方宗义.利用NOAA卫星的AVHRR资料试做云性质的分析.应用mobilesport365_365游戏盒子_28365备用网址官方网站学报, 1998, 9(4):449-455. http://qikan.zoztao.com/jams/ch/reader/view_abstract.aspx?file_no=19990132&flag=1
[2]	刘健, 许健民, 方宗义.利用NOAA卫星的AVHRR资料试分析云和雾顶部粒子的尺度特征.应用mobilesport365_365游戏盒子_28365备用网址官方网站学报, 1999, 10(1):28-33. http://qikan.zoztao.com/jams/ch/reader/view_abstract.aspx?file_no=19990132&flag=1
[3]	Strabala K I, Ackerman S A, Menzel W P.Cloud properoties inferred from 8~12 μm data.J Appl Meteor, 1994, 2:212-229. doi: 10.1175/1520-0450%281994%29033<0212%3ACPIFD>2.0.CO%3B2
[4]	Baum B A, Peter F Soulen, Kathleen I Strabala, et al.Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS 2:Cloud thermodynamic phase.J Geophys Res, 2000, 105(9):11781-11792. https://www.researchgate.net/publication/230676654_Remote_sensing_of_cloud_properties_using_MODIS_airborne_simulator_imagery_during_SUCCESS_2_Cloud_thermodynamic_phase
[5]	Liu Jian, Dong Chaohua, Zhu Xiaoxiang.Thermodynamic phase analysis of cloud particles with FY-1C data.Meteorology and Atmospheric Physics, 2002, 80:65-71. doi: 10.1007/s007030200015
[6]	Liu Jian, Zhu Yuanjin.Detection of multilayer cirrus cloud using FY-1C data.Acta Meteorologica Sinica, 2002, 16(3):327-337. https://www.researchgate.net/publication/288586055_Detection_of_multilayer_cirrus_cloud_using_FY-1C_data
[7]	Bryan A Baum, Spinhirne J D.Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS 3:Cloud overlap.J Geophys Res, 2000, 105:11793-11804. doi: 10.1029/1999JD901091
[8]	Hansen J E, Travis L D.Light scattering in planetary atmospheres.Space Sci Rev, 1974, 16:527-610. doi: 10.1007/BF00168069
[9]	刘健, 董超华.利用FY-1C资料反演水云的光学厚度和粒子有效半径.红外与毫米波学报, 2003, 22(6):436-441. http://www.cnki.com.cn/Article/CJFDTOTAL-HWYH200306008.htm
[10]	Menzel W P, Strabala K.Cloud Top Properties and Cloud Phase:Algorithm Theoretical Basis Document.ATBD-MOD-04, NASA Goddard Space Flight Center, 1997. https://modis.gsfc.nasa.gov/data/atbd/atbd_mod04.pdf