Low visibility forecasts for different flight planning horizons using tree-based boosting models

Low visibility conditions enforce special procedures that reduce the operational flight capacity at airports. Accurate and probabilistic forecasts of these capacity-reducing low-visibility procedure (lvp) states help the air traffic management to optimize flight planning and regulation. In this paper we investigate nowcasts, medium-range forecasts, and the predictability limit of the lvp states at Vienna Airport. The forecasts are computed with boosting trees, which consist of an ensemble of decision trees grown iteratively on residuals of previous trees. The model predictors are observations at Vienna Airport and output of a high resolution and an ensemble numerical weather prediction (NWP) model. Observations have highest impact for nowcasts up to a lead time of two hours. Afterwards a mix of observations and NWP forecast variables generates the most accurate predictions. With lead times longer than eight hours NWP output dominates until the predictability limit is reached at +12 days. For lead times longer than two days ensemble output generates higher improvement than a single higher resolution. The most important predictors for lead times up to +18 hours are observations of lvp and dew point depression, as well as NWP dew point depression. At longer lead times dew point depression and evaporation from the NWP models are most important.