广州白云机场低能见度客观预报方法试验  

Experiment on Objective Forecast Methods for The Low Visibility of Guangzhou Baiyun Airport

作　　者：文俊鹏 蓝静 刘峰 Wen Junpeng;Lan Jing;Liu Feng(Meteorological Center of Central South Air Traffic Management Bureau of Civil Aviation Administration of China,Guangzhou 510405;Guangzhou Climate and Agrometeorology Center,Guangzhou 511400)

机构地区：[1]民航中南空管局气象中心,广州510405 [2]广州市气候与农业气象中心,广州511400

出　　处：《气象科技进展》2021年第2期176-180,共5页Advances in Meteorological Science and Technology

基　　金：环境气象中长期预报方法研究(GRMC2017Q15)。

摘　　要：利用全球预报系统(GFS)模式数据与广州白云机场自动气象观测系统(AWOS)自动观测数据,建立了白云机场能见度的多元逐步回归和BP神经网络逐3 h的客观预报模型,并对白云机场2015年一次低能见度天气过程进行预报分析。结果表明,在白云机场能见度下降并到达2 km的天气过程中,两种预报模型均能提前24 h预报出来;对于白云机场大雾天气的预报,两种预报模型可提前12 h预报出来,其中BP神经网络预报与实况相比,预报大雾出现时间与实际时间仅相差1个时次(3 h),大雾消失时间一致,最低能见度相差261 m,而多元逐步回归方法对大雾的预报空报较低,因此结合两种模型的预报有利于提高白云机场大雾预报的准确率。Using GFS model data and AWOS automatic observation data of Guangzhou Baiyun airport,two visibility objective forecast models were established by multiple stepwise regression method and BP neural network method.Two models were utilized to predict a Baiyun airport’s low visibility weather in 2015.The results showed that both models can predict the low visibility weather 24 h in advance when the visibility drops to 2000 m.For the forecasting of foggy weather,both two models can predicted 12 h in advance,and the BP neural network model can forecast the variation tendency of low visibility while the multiple stepwise regression model has a lower vacancy forecasting rate.Therefore,combining the two models has the potential to improving the accuracy of fog forecasting in Baiyun airport.

关 键 词：广州白云机场 低能见度 客观预报方法 试验 

分 类 号：V321.213[航空宇航科学与技术—人机与环境工程] P457.7[天文地球—大气科学及气象学]