Abstract
This work presents an analysis of low-visibility event persistence and prediction at Villanubla Airport (Valladolid, Spain), considering Runway Visual Range (RVR) time series in winter. The analysis covers long- and short-term persistence and prediction of the series, with different approaches. In the case of long-term analysis, a Detrended Fluctuation Analysis (DFA) approach is applied in order to estimate large-scale RVR time series similarities. The short-term persistence analysis of low-visibility events is evaluated by means of a Markov chain analysis of the binary time series associated with low-visibility events. We finally discuss an hourly short-term prediction of low-visibility events, using different approaches, some of them coming from the persistence analysis through Markov chain models, and others based on Machine Learning (ML) techniques. We show that a Mixture of Experts approach involving persistence-based methods and Machine Learning techniques provides the best results in this prediction problem.
Highlights
Very low-visibility events due to fog are classified among severe weather conditions that most affect air traffic and flight operations at airports [1,2,3], since they can dramatically reduce the runway capacity [4]
We distinguish between long-term and show-term persistence, due to the different nature of their analyses: for long-term persistence, we show the results of the Detrended Fluctuation Analysis (DFA) approach, which provides its correlation exponent, α
We have considered the following statistic metrics in order to evaluate the different prediction systems proposed: accuracy (ACC), true positive rate (TPR), true negative rate (TNR) and F1 score (F1S)
Summary
Very low-visibility events due to fog are classified among severe weather conditions that most affect air traffic and flight operations at airports [1,2,3], since they can dramatically reduce the runway capacity [4]. Forecasting low-visibility conditions is a recurrent problem for airport managers. It is, a very difficult task requiring both knowledge of the meteorological causes of fog formation, and a thorough awareness of the local topography. As stated by many authors [5,6,7,8], the forecasting of fog events by numerical weather prediction is difficult, in part because fog formation is extremely sensitive to small-scale variations of atmospheric variables, such as wind-shifts or changes in the low-level stability. One of the first attempts was the use of linear regression [9], but the recent
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
