冬季路面最低温度统计预报方法对比研究  

Comparative Study on Statistical Prediction Methods of the Minimum Road Surface Temperature in Winter

作　　者：贾晓红 魏巍 石岚 Jia Xiaohong;Wei Wei;Shi Lan(Inner Mongolia Meteorological Service Center,Inner Mongolia Hohhot 010051;Inner Mongolia Climate Center,Inner Mongolia Hohhot 010051)

机构地区：[1]内蒙古自治区气象服务中心,内蒙古呼和浩特010051 [2]内蒙古自治区气候中心,内蒙古呼和浩特010051

出　　处：《内蒙古气象》2024年第1期25-30,共6页Meteorology Journal of Inner Mongolia

摘　　要：基于2018—2020年G6京藏高速(简称“G6高速”)兴和服务区、卧佛山隧道出口两个交通气象站观测数据,分析了冬季路面最低温度日变化特征,利用逐步回归、回归树、最小二乘支持向量机及深度神经网络4种统计预报方法,开展了简单特征方案及复杂特征方案下冬季路面最低温度预报建模和检验。结果表明:冬季夜间路面最低温度与气温接近,上午迅速增温,13时左右提前于气温达到峰值。在模型验证过程中,兴和服务区、卧佛山隧道出口复杂特征方案在4项评价指标方面均好于简单特征方案。从总体评价指标来看,深度神经网络效果模拟最好,其中复杂特征方案中兴和服务区准确率可达74.72%,平均绝对误差为2.53℃,卧佛山隧道出口准确率高达96.38%,平均绝对误差为1.14℃,可应用于路面温度预报业务。BBased on the observation data of two traffic weather stations at Xinghe service area and the exit of the Wofoshan Tunnel along the G6 Highway from 2018 to 2020, the daily variation characteristics of the minimum road surface temperature in winter were analyzed, and the prediction models of winter minimum road temperature under simple feature scheme and complex feature scheme were established and tested by four statistical forecasting methods, including stepwise regression, regression tree, least squares support vector machine and deep neural networks. The results showed that the minimum road surface temperature was close to the air temperature at night in winter, and the road surface temperature increased rapidly in the morning, and reached the peak at about 13:00, which was earlier than the air temperature. The model test results showed that the complex feature scheme was better than the simple feature scheme in four evaluation indicators at these two traffic weather stations. According to the overall evaluation index,the model established by deep neural network method had the best prediction effect. The prediction accuracy and the average absolute error of the complex feature scheme at Xinghe service area were 74.72% and 2.53 ℃, respectively, while the prediction accuracy and the average absolute error at the exit of Wofoshan Tunnel were 96.38% and 1.14 ℃, respectively, which meant this model could be applied to forecasting road surface temperature.

关 键 词：G6高速 路面最低温度 统计预报方法 

分 类 号：P468.021[天文地球—大气科学及气象学]