Abstract:

Based on the observational data of the traffic-meteorological stations along the expressway of Hebei Province, the thick fog processes (visibility<500 m) in autumn and winter of 2013 and 2014 were found out firstly, then the temporal distribution of thick fog along the expressway and variation of some meteorological elements during thick fog processes were analyzed. The results show that during the period of 18:00-20:00, the thick fog began to appear most frequently, and at the stage from 08:00 to 10:00 it began to disappear more frequently. The frequency of lasting time ranging from 12 to 24 hours was highest for the thick fog processes. During thick fog processes, relative humidity was between 95% and 100%, and T-Td and wind speed ranged from -1.0 to 2.0 ℃ and 0 to 5.8 m·s-1, respectively. Namely, the greater of relative humidity, the lower of T-Td, the smaller of the wind speed, the higher of the possibility of low visibility was. By analysis of the correlation between meteorological elements and visibility, the relative humidity, dew point temperature difference, wind speed, wind direction, pressure, temperature, visibility were selected as the network input to establish BP neural network model. Taking the change of simulated visibility at Wuqiang and Hengshui sites during two thick fog processes as example to test, it all achieved good test results.

Key words: thick fog, visibility, meteorological element, BP neural network

摘要：

利用河北省高速公路沿线交通气象站的观测资料，统计2013年和2014年秋冬季浓雾（能见度＜500 m）过程个例，分析高速公路沿线浓雾的时间分布特征和各气象要素变化。结果表明：（1）18:00—20:00（北京时，下同），浓雾开始出现的频率最高；（2）08:00—10:00，浓雾结束的频率最高；（3）浓雾过程持续时间在12~24 h的频率最高；（4）相对湿度在95%~100%之间，温度露点差在-1.0~2.0 ℃，风速在0~5.8 m·s-1，即相对湿度越大、温度露点差越低、风速越小，则出现低能见度的可能性越大。分析各气象要素与能见度的相关性，最后选定相对湿度、温度露点差、风速、风向、气压、气温、能见度7个气象因子作为网络输入建立BP神经网络模型，并以武强、衡水单站2次浓雾过程中能见度变化为例进行检验，取得较好的试验效果。

关键词: 浓雾, 能见度, 气象要素, BP神经网络