CAJ | 학술논문

从基于云角色的分类思想出发,利用星载毫米波雷达探测资料提取云的特征参数,建立支持向量机（support vector machine,SVM）模型实现云的分类。通过与BP（back propagation）网络模型的分类结果进行对比,发现两种模型都具有较好的分类能力,但SVM模型的识别准确率更高,计算速度更快。基于CloudSat资料的云分类实例表明,SVM模型的分类结果与CloudSat数据处理中心（Data Processing Center,DPC）发布产品具有很好的一致性。
종기우운각색적분류사상출발,이용성재호미파뢰체탐측자료제취운적특정삼수,건립지지향량궤（support vector machine,SVM）모형실현운적분류。통과여BP（back propagation）망락모형적분류결과진행대비,발현량충모형도구유교호적분류능력,단SVM모형적식별준학솔경고,계산속도경쾌。기우CloudSat자료적운분류실례표명,SVM모형적분류결과여CloudSat수거처리중심（Data Processing Center,DPC）발포산품구유흔호적일치성。
According to the role-based cloud classification method,a SVM（Support Vector Machine） model is established to achieve cloud type classification by extracting characteristic parameters of spaceborne millimeter-wave radar sounding data.By comparing with the classification results using BP（back propagation） network model,it is found that both two models have good classification capability,but the SVM model has better identification accuracy and faster calculation speed.An example of cloud type classification based on CloudSat data shows that the results of SVM model are well consistent with products published by CloudSat DPC（Data Processing Center） .