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Cloud Type Identification Based on Macro and Micro Properties of Clouds from MODIS
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摘要: 利用MODIS云光学厚度、云粒子有效半径、云顶高度、云相态等产品，以及表征6种云类的云光学厚度、云粒子有效半径、云顶高度、云相态的特征值，采用最小距离分类法和多阈值判识法相结合，对卫星观测像元的云进行分类，包括层云、层积云、积云、积雨云、雨层云、高积云/高层云、卷云以及卷云伴随高积云或高层云的多层云、卷云伴随层云或层积云的多层云、高积云或高层云伴随层积云或层云的多层云10类。2008年、2013年卫星分类结果与地面站云类观测对比，达到60%的一致性；将相同时间的地面小时降水量与分类结果叠加显示，出现降水处多为雨层云或积雨云。Abstract: Satellite cloud type product has been operationally processed in China National Satellite Meteorological Center (NSMC) for many years. But due to causes of instruments on board and methods used for cloud type identification, this product still needs improving. In 2011, American scientists proposed a new method to classify cloud types in NPOESS (national polar-orbiting operational environmental satellite system) cloud products algorithm theoretical basis documents. This method uses the satellite derived cloud optical thickness product, cloud effective radius product, cloud top height product, cloud phase product, a set of characteristic values of cloud optical thickness, cloud effective radius, cloud top height, and cloud phase for 6 cloud types to calculate distances between satellite data and characteristic parameters of 6 cloud types. Finally, a minimum distance is obtained, and the corresponding cloud type is derived.Using MODIS data, the minimum distance cloud type identification method is combined with multiple-threshold method, and cloud type identification experiments are carried out. By incorporating methods into software, and using cloud optical thickness product, cloud effective radius product, cloud top height product, cloud phase product, cloud top temperature product, and brightness temperature product of MODIS as inputs of the software, cloud type identification results are outputted for years of 2008 and 2013. Results are compared with ground cloud type observations, and two series are more than 60% consistent. Also, pictures combining satellite derived cloud types and ground hourly precipitation amount observations reflect that analyzed cumulonimbus and nimbostratus are reasonably in the zone of raining. Because the cloud optical thickness can largely reveal the water content in clouds and the vertical thickness of clouds, this cloud type identification method captures raining clouds effectively.
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图 2 2014年12月29日05:05台风蔷薇的云分类结果
Fig. 2 Classified cloud types of typhoon Qiangwei at 0505 UTC 29 Dec 2014
图 3 2014年12月29日05：05台风蔷薇MODIS云光学厚度
Fig. 3 MODIS optical thickness data of typhoon Qiangwei at 0505 UTC 29 Dec 2014
图 4 2014年6月27日05：15—05：20梅雨锋云系的分类结果
Fig. 4 Classified cloud types of Meiyu front for 0515-0520 UTC 27 Jun 2014
图 5 2013年1月3日04：50—06：35我国南方的层状云系的MODIS通道32亮温
Fig. 5 The grayscale image of brightness temperature of MODIS channel 32 for 0450-0635 UTC 3 Jan 2013
图 6 2013年1月3日04:50—06:35层状云系的分类结果
Fig. 6 Classified cloud types for 0450-0635 UTC 3 Jan 2013
图 7 2012年8月3日04：55—05：05地面2 h降水量 (数字，单位：mm) 与云分类结果叠加
Fig. 7 Chart combined cloud types with 2 h precipitation (number, unit:mm) for 0455-0505 UTC 3 Aug 2012
表 1 各种类型云的宏微观特性
Table 1 Macro and micro properties of 6 cloud types
云类 云顶高度/km 云粒子有效半径/μm 云光学厚度 云相态 平均 范围 平均 范围 平均 范围 层积云/层云 1.3 0~2.5 13.5 2~25 5.5 1~10 水 高层云/高积云 3.5 1.5~5.5 17 4~30 17 2~32 水/冰 积云 3.3 0.2~6.5 27.5 5~50 26.5 3~50 水/冰 积雨云 12 6~20 27.5 5~72 50 25~100 冰 雨层云 8 4~11 17 5~50 50 25~100 水/冰 卷云 9.5 6.5~15 65 10~120 3.5 0.01~8 冰 
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