Short-Term Fog Forecasting using Meteorological Observations at Airports in North India

Fog is a major phenomenon in North India during the winter months of November to February. It lowers visibility in these regions which adversely affects surface and air transportation, agriculture, and other day-to-day activities. Flights get delayed, canceled or diverted due to reduced visibility conditions. Therefore, accurate forecasting of fog (which is measured as visibility in terms of distance) at the airports up to 6 hours is important. We develop data science models that use historical meteorological observations (1974-2021) from METAR reports of ground stations at different airports of North India for short-term fog prediction. Models are designed for both binary classification ("fog" versus "no-fog") as well as multi-class classification (that categorizes fog into 4 classes). In addition, we also design regression models that predict visibility. Results show that the best visibility regression model has a root-mean-squared-error (RMSE) of 0.20 km for 3-hour lead time prediction for Lucknow airport. Corresponding classification accuracies are 0.90 and 0.79 for the 2-class and 5-class problems respectively. Similar trends were observed for other airports and lead times. A web-based dissemination system has been deployed at https://fog.iitk.ac.in.