基于CART决策树的河北省低能见度分类研究

doi:10.3969/j.issn.1673-503X.2022.03.013

Abstract

Abstract:

Based on the hourly observation data of 142 national weather stations in Hebei province from 2016 to 2019, a visibility forecast model was constructed for low visibility and high-frequency areas using Empirical Orthogonal Function and CART decision tree classification and regression methods, and a fitting test was carried out.The results show that except in Zhangjiakou, Chengde, and Qinhuangdao, fog days most appear in the areas south of 40°N, with the average peak of annual fog days more than 50 days.The relative humidity, surface temperature, wind speed, and other meteorological factors are significantly correlated with visibility.High correlation factors act as input variables to the forecast model, which is proved to have a good effect on the winter forecast with high accuracies.In summer, the false alarm rates are lower and the differences between day and night are less obvious, with little differences between the three indexes.Whereas in winter, the accuracy rates and false alarm rates are significantly superior to those in the daytime, and the missing alarm rates are slightly higher.The fitting results of three heavy fog processes on December 7-10, 2019, at Shijiazhuang station are good without any fogy time missed.

Key words: Visibility, CART decision tree, Fitting prediction

CLC Number:

P427.2

Qi YOU,Xiao-li QU,Zeng-bao ZHAO,Jie WANG,Di ZHANG,Lin-han YANG. Low visibility classification based on CART decision tree in Hebei province[J]. Journal of Meteorology and Environment, 2022, 38(3): 112-118.

Figures/Tables 9

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气象因子	相关系数	气象因子	相关系数
2 min风向	0.084	露点温度	0.077
2 min风速	0.330^**	相对湿度	-0.579^**
地表温度	0.355^**	整点气温	0.308^**
地表最低温度	0.358^**	最低气温	0.308^**
过去3 h变压	-0.170^**	最高气温	0.313^**
过去24 h变温	0.105^**

预报检验	夏日	夏夜	冬日	冬夜
准确率	42.1	41.1	49.8	55.0
漏报率	51.4	52.8	15.7	21.6
误报率	24.1	24.0	45.1	35.1

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Low visibility classification based on CART decision tree in Hebei province

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