联合星载毫米波雷达和激光雷达资料的云相态识别技术

摘要

依据星载偏振激光雷达云相态识别原理,借鉴星载毫米波雷达温度阈值云相态识别方法,利用支持向量机(SVM)构建了联合CloudSat和CALIPSO卫星资料的云相态识别模型并进行了实例反演验证.SVM方法训练和测试样本集采用了CloudSat的2B-GEOPROF-LIDAR云廓线数据、CALIPSO的2级1km云层数据以及欧洲中期天气预报中心的辅助温度数据,识别结果与温度阈值法得到的CloudSat云相态产品、CALIPSO云相态产品以及相关资料进行了对比验证,结果表明联合两种雷达数据的支持向量机云相态识别技术具有较高的识别精度,能够更为准确地反演云相态的垂直分布信息.%According to cloud phase discrimination theory of spaceborne polarization lidar, and use of the method of temperature threshold for spaceborne millimeter wave radar for reference, a cloud phase discrimination algorithm using CloudSat and CALIPSO Satellite data based on support vector machines (SVM) method was established. The training and testing data of samples used for establishing SVM model were mainly derived from CloudSat 2B-GEOPROF-LIDAR, CALIPSO level 2 lkm cloud layer, and ECMWF auxiliary temperature data products. The discrimination result was compared with CloudSat cloud phase product retrieved by temperature threshold method, CALIPSO cloud phase product and other relevant data. The research results show that this cloud phase discrimination technique, e.g. SVM method with combined data of radar and lidar detection, has a superior accuracy and can provide better vertical distribution information of cloud phase.