Cloud Properties Analysis and Its Application in FY-2 Cloud Detection

Liu Jian

Vol.20, NO.6, 2009

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2009 > 20(6): 673-681

Liu Jian. Cloud properties analysis and its application in FY-2 cloud detection. J Appl Meteor Sci, 2009, 20(6): 673-681.

Citation:

Liu Jian. Cloud properties analysis and its application in FY-2 cloud detection. J Appl Meteor Sci, 2009, 20(6): 673-681.

Liu Jian. Cloud properties analysis and its application in FY-2 cloud detection. J Appl Meteor Sci, 2009, 20(6): 673-681.

Citation:

Liu Jian. Cloud properties analysis and its application in FY-2 cloud detection. J Appl Meteor Sci, 2009, 20(6): 673-681.

PDF( 3164 KB)

Cloud Properties Analysis and Its Application in FY-2 Cloud Detection

Liu Jian

National Satellite Meteorological Center, Beijing 100081

Received Date: 2008-09-23
Rev Recd Date: 2009-07-17
Publish Date: 2009-12-31

Abstract

Abstract

One pixel is cloud or not dependent on cloud detection scheme. Cloud detection approach includes histogram analysis, threshold detection, deviation analysis and so on. Cloud threshold is an important factor for cloud detection scheme. Now, cloud threshold usually can be gotten by one time and multi channel data in operational cloud detection method. But if surface is covered by snow or ice, or cloud covers one area for a long time, dynamic cloud threshold method always fails. So it should depend on clear temperature background. On the other hand, climate mean surface temperature can help to distinguish the dynamic cloud threshold' s validity. ISCCP data are proved by many researches that it is one of the best satellite cloud climate data in the world now. ISCCP data are used to analyze properties of cloud and clear temperature over China and neighboured areas. Cloud can be divided into three kinds, such as low, middle and high cloud. Different kind of cloud has different distribution property of cloud top temperature on a seasonly or daily basis along the latitude. Different kinds of cloud top temperature basically show strip distribution along longitude. The lower latitude, the higher cloud top temperature is. Along the same latitude, different area has different cloud properties during one day or one year.The analysis shows that the low cloud top temperature increases progressively in the fall and winter seasons from north to south, and has obvious linear increase tendency with reduced latitude. The middle level cloud top temperature distribution has distinct properties. Except July, August and September, the distribution of middle level cloud top temperature presents the meridional strip basically.The middle level cloud top temperature gradient is large in fall and winter, which is small in spring. The high cloud top temperature presents the meridional strip distribution in fall, winter and spring. The high cloud top temperature is lower than 240 Kand it' s latitudinal gradient is small to the north of 35°N. The high cloud top temperature meridional gradient increases and it' s diurnal variation is small to the south of 35°N. The temperature difference between clear surface and the warmest cloud top has stabile daily and seasonly change in the southeast part of China. At the same time, in the north part of China, the temperature difference between clear surface and the warmest cloud top has distinct daily and seasonly change. Above analyzed properties and mean clear temperature can be used as background information to distinguish the dynamic cloud detection threshold value' s validation and offer cloud threshold for areas that are covered by low cloud for a long time. An example is showed to testify mean clear temperature and temperature difference between clear and the warmest cloud can offer great information for cloud detection scheme.
- ISCCP;
- clear temperature;
- cloud top temperature;
- cloud detection

FullText(HTML)

References(16)

Figures and Tables

图 1 多年平均1, 4, 7, 10月06:00晴空下垫面与最暖云云顶亮温差

Fig. 1 Multi-annual mean temperature difference between clear surface and the warmest cloud top at 06:00 in January, April, July and October

DownLoad: Full-Size Img PowerPoint

图 2 多年平均1月晴空下垫面与最暖云云顶亮温差每3h间隔沿83.75°E, 106.25°E

Fig. 2 Distribution pattern of multi-annual temperature difference between clear surface and cloud to p along 83.75°E, 106.25°E and 126. 25°E for every 3 hours in January

DownLoad: Full-Size Img PowerPoint

图 3 多年平均4月晴空下垫面与最暖云云顶亮温差每3h间隔沿83.75°E, 106.25°E和126.25°E的分布

Fig. 3 Distribution pattern of multi-annual temperature difference between clear surface and cloud to p along 83.75°E, 106.25°E and 126.25°E for every 3 hours in April

DownLoad: Full-Size Img PowerPoint

图 4 多年平均7月晴空下垫面与最暖云云顶亮温差每3h间隔沿83.75°E, 106.25°E

Fig. 4 Distribution pattern of multi-annual temperature difference between clear surface and cloud to p along 83.75°E, 106.25°E and 126.25°E for every 3 hours in July

DownLoad: Full-Size Img PowerPoint

图 5 多年平均10月晴空下垫面与最暖云云顶亮温差沿83.75°E, 106.25°E和126.25°E的分布

Fig. 5 Distribution pattern of multi-annual temperature difference between clear surface and cloud to p along 83. 75° E, 106. 25° E and 126.25°E for every 3 hours in October

DownLoad: Full-Size Img PowerPoint

图 6 被雾覆盖区扫描线上观测像元的FY-2C红外亮温、提取的动态阈值及ISCCP晴空多年平均下垫面亮温

Fig. 6 Pixel' s FY-2C infra red brightness temperature, the dynamic threshold value and the ISCCP multi-annual mean clear sky surface temperature in selected fog area

DownLoad: Full-Size Img PowerPoint

图 7 云检测阈值合理性检验前后的判识对比图(白色为云像元, 黑色为晴空像元; 方框为所选目标区域)

(a) FY-2C红外窗区通道亮温, (b) 用云检测阈值直接判识结果图, (c) 利用气候背景场订正云检测阈值后的云检测判识图

Fig. 7 Cloud detection image s before and after validation for cloud detection threshold value (white area is cloud pixel an d black area is clear pixel; square frame denotes the target region)

(a) brightness temperature at infared channel of FY-2C, (b) cloud detection image using cloud threshold directly, (c) cloud detection image after adjusted cloud threshold by climate background data

DownLoad: Full-Size Img PowerPoint

图 8 青藏高原地区扫描线上观测像元的FY-2C红外亮温、提取的动态阈值及ISCCP晴空多年平均地表亮温

Fig. 8 Pixel' s FY-2C infrared brightness temperature , the dy namic threshold value and the ISCCP multi-annual mean clear surface temperature over the Tibet area

DownLoad: Full-Size Img PowerPoint

References

[1]	翁笃鸣, 韩爱梅.我国卫星总云量与地面总云量分布的对比分析.应用气象学报, 1998, 9(1) : 2-37. http://qk.cams.cma.gov.cn/jams/ch/reader/view_abstract.aspx?file_no=19980105&flag=1
[2]	William B Rossow, Alison W Walker, Garder L C. Compari-son of ISCCP and Other Cloud Amounts. J Climate, 1993, 6 (12) : 2394-2418. doi: 10.1175/1520-0442(1993)006<2394:COIAOC>2.0.CO;2
[3]	William B Rossow, Robert A Schiffer.Advances in understanding clouds from ISCCP.Bull Amer Meteor Soc, 1999,80(11):2261-2287. doi: 10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2
[4]	丁守国, 石广玉, 赵春生.利用ISCCPD2资料分析近20年全球不同云类云量的变化及其对气候可能的影响.科学通报,2004,49(11):1105-1111. http://www.cnki.com.cn/Article/CJFDTOTAL-KXTB200411016.htm
[5]	刘洪利, 朱文琴, 宜树华, 等.中国地区云的气候特征分析.气象学报, 2003, 61(4) : 466-473. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200304007.htm
[6]	陈勇航, 黄建平, 陈长和, 等.西北地区空中云水资源的时空分布特征.高原气象, 2005, 24(6) : 905-912. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200506008.htm
[7]	陈勇航, 黄建平, 王天河, 等.西北地区不同类型云的时空分布及其与降水的关系.应用气象学报, 2005, 16(6) : 717-727. http://qk.cams.cma.gov.cn/jams/ch/reader/view_abstract.aspx?file_no=20050694&flag=1
[8]	刘艳, 翁笃鸣.青藏高原云对地气系统长波射出辐射(OLR) 强迫的气候研究.南京气象学院学报, 2000, 23(2) : 270-276. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX200002016.htm
[9]	刘瑞霞, 刘玉洁, 杜秉玉.利用ISCCP资料分析青藏高原云气候特征.南京气象学院学报, 2002,25(2):226-234. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX200202012.htm
[10]	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.应用气象学报, 1998, 9(4) :449-455. http://qk.cams.cma.gov.cn/jams/ch/reader/view_abstract.aspx?file_no=19980466&flag=1
[11]	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.应用气象学报, 1999, 10 (1) : 28-33. http://qk.cams.cma.gov.cn/jams/ch/reader/view_abstract.aspx?file_no=19990132&flag=1
[12]	Alan V D V, William J E, Senior Member.An automated dynamic threshold cloud masking algorithm for daytime AVHRR images over land. IEEE Transact ions on Geoscience and Remote Sensing, 2002, 40(8):1682-1694. doi: 10.1109/TGRS.2002.802455
[13]	Saunders R W, Kriebel K T.An improved method of detec-ting clear sky and cloudy radiances from AVHRR data. Int J Remote Sens, 1988, 9 : 123-150. doi: 10.1080/01431168808954841
[14]	Peak J E, Tag P M.Segmentation of satellite imagery using hierarchical thresholding and neural networks. J Appl Meteor, 1994, 33 : 605-616. doi: 10.1175/1520-0450(1994)033<0605:SOSIUH>2.0.CO;2
[15]	Nowcasting and Very Short Range Forecasting SAF.Proto-type Scientific Description for Météo France/CMS. SAF/NWC/MFCMS/MTR/PSD, Issue 1, Rev 1, 2000.
[16]	刘希, 许健民,杜秉玉.用双通道动态阈值对GMS-5图像进行自动云检测.应用气象学报,2005,16(4):434-444. http://qk.cams.cma.gov.cn/jams/ch/reader/view_abstract.aspx?file_no=20050454&flag=1