Forecasting low-visibility procedure states with tree-based statistical methods

Low-visibility conditions at airports can lead to capacity reductions and therefore to delays or cancelations of arriving and departing flights. Accurate visibility forecasts are required to keep the airport capacity as high as possible. We generate probabilistic nowcasts of low-visibility procedure (lvp) states, which determine the reduction of the airport capacity due to low-visibility. The nowcasts are generated with tree-based statistical models based on highly-resolved meteorological observations at the airport. Short computation times of these models ensure the instantaneous generation of new predictions when new observations arrive. The tree-based ensemble method "boosting" provides the highest benefit in forecast performance. For lvp forecasts with lead times shorter than one hour variables with information of the current lvp state, ceiling, and horizontal visibility are most important. With longer lead times visibility information of the airport’s vicinity, humidity, and climatology also becomes relevant.