新一代静止气象卫星对浙江省金丽温高速公路低能见度识别的研究  

Identification of Heavy Fog on the Jinliwen Expressway in Zhejiang Province by a New Generation Geostationary Meteorological Satellite

作　　者：梁晓妮[1] 任晨平 王志[2] 陈仲瑜 柳婧 宋建洋[2] LIANG Xiaoni;REN Chenping;WANG Zhi;CHEN Zhongyu;LIU Jing;SONG Jianyang(Meteorological Service Center in Zhejiang,Hangzhou 310017;Public Meteorological Service Center of China Meteorological Administration,Beijing 100081)

机构地区：[1]浙江省气象服务中心,杭州310017 [2]中国气象局公共气象服务中心,北京100081

出　　处：《气候与环境研究》2023年第5期471-482,共12页Climatic and Environmental Research

基　　金：浙江省公益技术应用研究项目LGF20D05003。

摘　　要：利用浙江省地面观测数据和新一代静止气象卫星数据,通过逻辑回归(LR)、线性判别(LDA)、K近邻算法(KNN)、决策树(CART)、高斯贝叶斯(NB)和支持向量机(SVM)6种机器学习算法针对浙江省金丽温高速公路进行低能见度识别建模,并运用多种评估方法评估模型结果,显示SVM算法模型效果较好,且针对小于1000 m的能见度天气有较好的识别。进一步结合地面观测数据和卫星数据建立识别模型,发现效果优于单一来源的数据建模,一般以KNN算法建模效果较好,且在对浓雾、强浓雾的识别中,结合地面和卫星数据的模型识别效果更好。针对单一数据利用SVM算法,结合地面和卫星数据选择KNN算法再对金丽温高速公路的大雾过程进行识别,显示新一代静止气象卫星数据的模拟效果不差于地面观测数据模拟效果,且能够识别夜间和凌晨的雾,对地面观测识别可作为有效补充,将对省内没有地面气象观测的低能见度识别和短临预测有一定辅助参考作用。In this paper,the field observation and new generation geostationary meteorological satellite data are utilized to conduct the low visibility training experiments on Zhejiang Jinliwen Expressway with six machine learning methods,including logistic regression,linear discriminant analysis,K-nearest neighbor algorithm(KNN),classification and regression tree,Gaussian naive Bayes,and support vector machine(SVM).In addition,this paper also incorporates a variety of methods to evaluate the training results.The SVM algorithm demonstrated a good training effect,particularly for recognizing visibility of<1000 m.Further,the integration of the ground observation and satellite data to establish the recognition model exhibits that the modeling results are better than a single source.Generally,the KNN algorithm has improved the modeling effect.Thus,to distinguish between dense fog and strong dense fog,the model combining the ground and satellite data has enhanced the recognition results.The SVM algorithm was employed for single source data,whereas the KNN algorithm was utilized for ground as well as satellite data to distinguish the fogging process on the Jinliwen Expressway.The results demonstrate that the recognition effect of the new generation geostationary meteorological satellite data is not only equivalent to the field observation recognition but also capable of distinguishing between morning and night fog,which can be utilized as an effective functional extension to the field observation recognition.Therefore,this research will provide an auxiliary reference for the identification and prediction of low visibility,thereby eliminating the requirement for field meteorological observations in the province.

关 键 词：新一代静止气象卫星 机器学习 低能见度 

分 类 号：P427.2[天文地球—大气科学及气象学]