Probabilistic Nowcasting of Low-Visibility Procedure States at Vienna International Airport During Cold Season

Airport operations are sensitive to visibility conditions. Low-visibility events may lead to capacity reduction, delays and economic losses. Different levels of low-visibility procedures (lvp) are enacted to ensure aviation safety. A nowcast of the probabilities for each of the lvp categories helps decision makers to optimally schedule their operations. An ordered logistic regression (OLR) model is used to forecast these probabilities directly. It is applied to cold season forecasts at Vienna International Airport for lead times of 30-min out to 2 h. Model inputs are standard meteorological measurements. The skill of the forecasts is accessed by the ranked probability score. OLR outperforms persistence, which is a strong contender at the shortest lead times. The ranked probability score of the OLR is even better than the one of nowcasts from human forecasters. The OLR-based nowcasting system is computationally fast and can be updated instantaneously when new data become available.

[1]  M. Haeffelin,et al.  PARISFOG: Shedding New Light on Fog Physical Processes , 2010 .

[2]  Silas Michaelides,et al.  Probabilistic Visibility Forecasting Using Neural Networks , 2007 .

[3]  W. Briggs Statistical Methods in the Atmospheric Sciences , 2007 .

[4]  Joby L. Hilliker,et al.  An Observations-Based Statistical System for Warm-Season Hourly Probabilistic Forecasts of Low Ceiling at the San Francisco International Airport , 1999 .

[5]  Achim Zeileis,et al.  Forecasting Low-Visibility Procedure States with Tree-Based Statistical Methods , 2018, Pure and Applied Geophysics.

[6]  Andreas Bott,et al.  Fog Prediction for Road Traffic Safety in a Coastal Desert Region: Improvement of Nowcasting Skills by the Machine-Learning Approach , 2015, Boundary-Layer Meteorology.

[7]  Richard de Dear,et al.  Application of Artificial Neural Network Forecasts to Predict Fog at Canberra International Airport , 2007 .

[8]  T. Bergot Large‐eddy simulation study of the dissipation of radiation fog , 2016 .

[9]  Robert L. Vislocky,et al.  An Automated, Observations-Based System for Short-Term Prediction of Ceiling and Visibility , 1997 .

[10]  Jörg Bendix,et al.  A 10 year fog and low stratus climatology for Europe based on Meteosat Second Generation data , 2017 .

[11]  G. Heller,et al.  The International Journal of Biostatistics Ordinal Regression Models for Continuous Scales , 2011 .

[12]  A. H. Murphy,et al.  Probability Forecasting in Meteorology , 1984 .

[13]  Rainer Winkelmann,et al.  Analysis of Microdata , 2006 .

[14]  John P. Oakley,et al.  The Fog Remote Sensing and Modeling Field Project , 2009 .

[15]  M. Pagowski,et al.  Fog Research: A Review of Past Achievements and Future Perspectives , 2007 .

[16]  H. Glahn,et al.  Use of Model Output Statistics for Predicting Ceiling Height , 1972 .

[17]  Gregory R. Herman,et al.  Using Reforecasts to Improve Forecasting of Fog and Visibility for Aviation , 2016 .

[18]  S. Chaudhuri,et al.  Nowcasting visibility during wintertime fog over the airport of a metropolis of India: decision tree algorithm and artificial neural network approach , 2014, Natural Hazards.

[19]  Caren Marzban,et al.  Ceiling and Visibility Forecasts via Neural Networks , 2007 .