Author(s)

Ajinkya Sadashiv Mane¹, Srinivas Subrahmanyam Pulugurtha¹, Venkata Ramana Duddu¹, Christopher Michael Godfrey²

¹IDEAS Center, Civil & Environmental Engineering Department, The University of North Carolina at Charlotte, Charlotte, USA.
²Atmospheric Sciences Department, The University of North Carolina at Asheville, Asheville, USA.

Low visibility condition hinders both air traffic and road traffic operations. Accurate forecasting of visibility condition helps aircraft operators and travelers to make better decisions and improve their safety. It is, therefore, essential to investigate and identify the predictor variables that could influence and help predict visibility. The objective of this study is to identify the predictor variables that influence visibility. Four years of surface weather observations, from January 2011 to December 2014, were collected from the weather stations located in and around the state of North Carolina, USA for the model development. Ordinary least squares (OLS) and weighted least squares (WLS) regression models were developed for different visibility and elevation ranges. The results indicate that elevation, cloud cover, and precipitation are negatively associated with the visibility in visibility less than 15,000 m model. The elevation, cloud cover and the presence of water bodies within the vicinity play an important role in the visibility less than 2000 m model. The chances of low visibility condition are higher between six to twelve hours after the rainfall when compared to the first six hours after the rainfall. The results from this study help to understand the influence of predictor variables that should be dealt with to improve the traffic operations and safety concerning the visibility near the airports/road transportation network.

Visibility, Prediction, Weather Station, Linear Regression Model, Elevation

Mane, A. , Pulugurtha, S. , Duddu, V. and Godfrey, C. (2022) Predictor Variables Influencing Visibility Prediction Based on Elevation and Its Range for Improving Traffic Operations and Safety. Journal of Transportation Technologies, 12, 439-452. doi: 10.4236/jtts.2022.123027.