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气象:2021,47(9):1113-1121
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基于Bayes判别法的结冰现象资料序列订正
温华洋,朱华亮,马文周,邱康俊,张苗苗,刘壮
(安徽省气象信息中心,合肥 230031;合肥工业大学,合肥 230009)
Data Sequence Correction of Icing Weather Phenomena Based on Bayes Discriminant Method
WEN Huayang,ZHU Hualiang,MA Wenzhou,QIU Kangjun,ZHANG Miaomiao,LIU Zhuang
(Anhui Meteorological Information Centre, Hefei 230031;Hefei University of Technology, Hefei 230009)
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投稿时间:2020-05-08    修订日期:2021-07-16
中文摘要: 针对安徽省81个国家级地面气象站1961—2018年结冰现象资料序列,采用要素一致性、内部一致性、空间一致性等方法进行数据质量控制,基于质量控制后的正常年份数据进行Bayes判别模型训练,应用训练所得模型完成异常年份结冰数据的订正。结果表明:安徽省共有38个台站累计84年的年结冰日数质量控制检查异常,年结冰日数异常年份主要集中在1961—1970、1988—1999和2015—2017年,造成年结冰日数异常的原因有部分台站历史观测任务简化、气象台站分类调整以及地面气象观测业务改革等。利用Bayes判别法构建了多个结冰现象判别模型,经检验发现,模型1和模型3具有较高的判识正确率、命中率、TS评分以及较低的误警率。考虑计算的简便性,选用模型1对异常年份结冰数据进行逐日订正。通过六安站、太和站和无为站异常年份结冰现象订正结果对比发现,基于Bayes判别法的结冰现象判别模型,对不同时间段内、不同原因造成的结冰现象观测记录异常的订正均较为合理,订正后的年结冰日数变化趋势更符合实际情况,表明采用Bayes判别模型订正结冰现象是合理、可行的。
Abstract:Using the methods of element consistency, internal consistency and spatial consistency, the data qualities of 81 national surface meteorological stations in Anhui Province from 1961 to 2018 are controlled. The Bayes discriminant model is trained based on the normal year data after quality control, and the icing data of abnormal year are corrected by the model. The results show that total 84 years’ annual icing days from 38 stations in Anhui Province are abnormal, mainly concentrated in 1961-1970, 1988-1999 and 2015-2017, and fail to pass the quality control. The main reasons for these phenomena are the simplification of historical observation tasks of some stations, the adjustment of meteorological station classification, the reform of ground meteorological observation operation and so on. Then, by using the Bayes discriminant method, several discrimination models of icing weather phenomena are constructed. After verification, it is found that Model 1 and Model 3 have higher recognition accuracy, hit rate and TS score but lower false alarm rate. Considering the simplicity of calculation, Model 1 is selected to correct the icing data of abnormal years day by day. In addition, through the comparison of the icing weather phenomena correction results of Lu’an, Taihe and Wuwei Stations in abnormal years, we find that the model based on Bayes discriminant method is more reasonable for the correction of the icing weather phenomena caused by different reasons in different time periods, and the variation trend of annual icing days after correction is more in line with the actual situation, which shows that the Bayes discrimination model is reasonable and feasible to correct the icing weather phenomena.
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基金项目:国家自然科学基金项目(41805023)和安徽省气象局研究性业务科技攻关项目(YJG202003)共同资助
引用文本:
温华洋,朱华亮,马文周,邱康俊,张苗苗,刘壮,2021.基于Bayes判别法的结冰现象资料序列订正[J].气象,47(9):1113-1121.
WEN Huayang,ZHU Hualiang,MA Wenzhou,QIU Kangjun,ZHANG Miaomiao,LIU Zhuang,2021.Data Sequence Correction of Icing Weather Phenomena Based on Bayes Discriminant Method[J].Meteor Mon,47(9):1113-1121.