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

Abstract

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