Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/180172 
Erscheinungsjahr: 
2017
Schriftenreihe/Nr.: 
Working Papers in Economics and Statistics No. 2017-22
Verlag: 
University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon), Innsbruck
Zusammenfassung: 
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.
Schlagwörter: 
aviation meteorology
visibility
nowcast
decision tree
bagging
random forest
boosting
Dokumentart: 
Working Paper

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