Author(s)

Lei Zhu¹, Guodong Zhu^2,3, Lei Han³, Nan Wang³

¹Training Center of Xinjiang Air Traffic Management Bureau, Urumqi, China.
²College of Atmospheric Science, Nanjing University, Nanjing, China.
³Meteorological Center of Xinjiang Air Traffic Management Bureau, Urumqi, China.

This paper uses Urumqi International Airport’s hourly observation from 2007 to 2016 and builds regression prediction model for airport visibility with deep learning method. From the results we can see: the absolute error of hourly visibility is 706 m. When the visibility ≤ 1000 m, the absolute error is 325 m, and this method can predict visibility’s trend. So we can use this method to provide the airport visibility’s objective forecast guidance products for aviation meteorological services in the future. In this paper, the Urumqi area is as the research object, to explore the depth of learning in the field of weather forecasting applications, providing a new visibility return forecast for weather forecast personnel so as to improve the visibility of the level of visibility to ensure the safe and stable operation of the airport.

Deep Learning, Airport Visibility, Regression Prediction

Zhu, L. , Zhu, G. , Han, L. and Wang, N. (2017) The Application of Deep Learning in Airport Visibility Forecast. Atmospheric and Climate Sciences, 7, 314-322. doi: 10.4236/acs.2017.73023.