Forecasting of Fog Index and Prediction Interval using Bayesian Methods

Fog is one of the atmospheric phenomena that affects air and surface transportation, and various regular activities. Therefore, accurate fog prediction is an important problem in the winter months when fog becomes severe in north India. Our objective in this work is long-term prediction of fog index for lead times of 6 hours to 5 days. The fog index is a holistic measure that takes into account both the fog severity and the duration. It is unit less and is normalised to be between 0 and 1. At longer lead times, studying and predicting fog index is more appropriate than instant visibility (i.e., fog) values. We augment the values predicted by classical and time series models by using a Bayesian approach for probabilistic regression that provides a credible interval around the point prediction values. The best model for fog index prediction exhibits an RMSE of 0.125 with an average band length of 0.43 for 3 days lead time at Lucknow airport. A web-based dissemination system that shows the predicted fog index values has been deployed at #1. The code is available at #1.