自动站气温数据异常的补偿方法  

Compensation method for abnormal temperature data of automatic weather station

作　　者：张颖超[1,2] 郭栋[1,2] 熊雄[1,2] 贺磊[1,2] 

机构地区：[1]南京信息工程大学信息与控制学院,南京210044 [2]南京信息工程大学气象灾害预报预警与评估协同创新中心,南京210044

出　　处：《计算机应用》2014年第3期888-891,897,共5页journal of Computer Applications

基　　金：江苏省六大人才高峰项目(WLW-021);江苏省产学研联合创新资金-前瞻性联合研究项目(BY2011111);南京市产学研资金资助项目(2012T026);公益性行业(气象)科研专项(GYHY201106040);中国气象局软科学研究课题项目(SK20120146)

摘　　要：为了保证气象资料的完整性与准确性,针对含有间断噪声的自动站日平均气温数据提出了3种隶属度函数,设计了基于平方平均隶属度函数的模糊支持向量机(FSVM)补偿算法,建立了补偿模型,并与传统支持向量机(SVM)方法进行了对比。实验结果表明:基于平方平均隶属度函数的FSVM方法对噪声点有较强的识别能力,插补后的数据精度达到了1.4℃,优于传统SVM方法的1.6℃;整体预测精度达到了1.13℃,同样优于传统SVM方法的1.42℃。To ensure the integrity and accuracy of the meteorological data, combined with automatic weather station＇s daily average temperature data which contained discontinuous noise, three types of membership functions were submitted. A compensation algorithm of Fuzzy Support Vector Machine （FSVM） based on root-mean-square membership function was designed and the compensation model was established too. Finally, the FSVM method was compared with the traditional Support Vector Machine （SVM） method. The experimental resuhs show that the proposed algorithm has good recognition capability for noise points. After interpolation, the data precision was 1.4℃, better than 1.6℃ of the traditional SVM method. Moreover, the whole data precision was 1.13℃, superior to 1.42℃ of the traditional SVM method.

关 键 词：自动气象站 间断噪声 日平均气温 平方平均隶属度函数 模糊支持向量机 补偿 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]