Fog nowcasting over the IGI airport, New Delhi, India using decision tree

Abstract

In the modeling framework, forecasting fog onset and its dissipation time is challenging work, which typically becomes a threshold problem in very dense fog (50m <Vis < 0m) cases. In addition, poor/inaccurate fog forecasts may create dangerous situations for the airline sector, where the accuracy of short-range forecasts is very essential. In the present study, we have developed a statistical tool based on real-time observational data to nowcast the dense fog events at Indira Gandhi International (IGI) Airport, New Delhi, India. The high temporal resolution observational dataset is available from three years of the Winter Fog Experiment (WiFEX) campaign. The performance of this tool for 6 dense fog events is verified with observed visibility data. The results reveal that this tool has the significant nowcasting skill for very dense fog prediction with a success rate of around 66%. This satisfactory agreement between the nowcasting tool and visibility builds the confidence to predict more dense/very dense fog events in the future after some fine-tuning in the present nowcasting tool.