摘要
利用NOAA-16/AMSU-B微波亮温资料和GOES-9光学遥感资料对2004年6月16日一次锋面气旋云系中的强对流云团进行识别,尝试了NOAA-16/AMSU-B微波两窗区通道亮温、3个微波水汽通道间亮温差,GOES-9红外亮温阈值、水汽和红外通道亮温差、红外和水汽通道亮温多光谱逐个修改聚类等方法,通过比较各种方法的识别结果,分析各种识别技术的特点,同时采用地面常规观测资料进行叠加,对识别方法进行了验证。结果表明:微波对强对流云团均能较好识别,但89 GHz通道亮温受地表影响较大,不能很好剔除过冷水体,150 GHz通道亮温与微波水汽通道间亮温差的识别结果较一致,3个微波水汽通道间亮温差对阈值的依赖性相对较小;GOES-9红外亮温阈值因其随时空变化对识别结果会造成较大差别,而水汽和红外通道亮温差对强对流云团能进行较好定位,但识别范围较小,多光谱逐个修改聚类方法对积雨云的识别效果较好,且和NOAA-16/AMSU-B识别结果有较好的对应关系;地面常规观测资料的叠加结果也说明,多波段遥感资料对强对流云团的识别结果与当时的天气现象及积雨云状均有较好的对应关系。
Detection and analysis on deep convective clouds in a frontal cyclone using NOAA-16/AMSU-B and GOES-9 data are investigated. A series of detection algorithms and discrimination are adopted, including the microwave brightness temperatures detection from the two window channels, water vapor channel microwave brightness differences identification based on the NOAA-16/AMSU-B data, infrared brightness thresholds detection of cloud top temperatures, the water vapor and infrared window temperature differences determination, and the classification of cumulonimbus clouds correlating with deep convective clouds applying with infrared/water vapor spectral features. These methods are validated by overlaying surface conventional data on the results and comparing them. The results show that microwave brightness temperatures from window channels can discriminate deep convective clouds effectively, while the brightness temperatures of 89 GHz are affected by surface features and the cold water surfaces are mistaken to convective clouds. The brightness temperatures of 150 GHz are just slightly influenced by surface characteristics, so the detection areas are coincident with those from water vapor channel microwave brightness differences identification, which can identify the deep convective clouds well and depend on the thresholds less. As to GOES-9, different infrared brightness thresholds bring about significant detection differences. Single thresholds are applicable to the local areas and the thresholds applicable to global regions should vary in spatial and temporal scales. The water vapor and infrared window temperature differences can detect convective regions well, while the determination areas are smaller. The stepwise cluster can identify cumulonimbus clouds correlating with deep convective clouds by means of infrared/water vapor spectral features, which can classify clouds objectively by combining image and pattern recognition. The detection areas are coincident with NOAA-16/AMSU-B detection areas, and the surface conventional data can validate the results, including hazards weather and cumulonimbus clouds.
出处
《应用气象学报》
CSCD
北大核心
2009年第4期428-436,共9页
Journal of Applied Meteorological Science
基金
国家自然科学基金项目(40805012)
中国博士后科学基金(20070420577)共同资助
关键词
微波遥感
光学遥感
强对流云
microwave remote sensing
optical remote sensing
deep convective cloud
