Forecasting Low-Visibility Procedure States with Tree-Based Statistical Methods (vol 176, pg 2631, 2019)

摘要

Low-visibility conditions at airports can lead to capacity reductions and thereforeto delays or cancelations of arriving and departing flights. Accurate visibility forecasts are required to keep airport capacity as high as possible. We generate probabilistic nowcasts of low-visibility procedure (lvp) states, which determine the reduction of 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 theinstantaneous generation of new predictions when new observations arrive. The tree-based ensemble method boosting provides the highestbenefit in forecast performance. For lvp forecasts with lead times shorter than 1h, 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 and standard meteorological variables, such as humidity, also become relevant. Due to the copy editing process, a couple of mistakes were included in the text. Following paragraphs and tables should be read.