Abstract
Abstract. 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 in optimizing flight planning and regulation. In this
paper, we investigate nowcasts, medium-range forecasts, and the
predictability limit of the lvp states at Vienna International Airport. The forecasts are
generated with boosting trees, which outperform persistence, climatology,
direct output of numerical weather prediction (NWP) models, and ordered
logistic regression. The boosting trees consist of an ensemble of decision
trees grown iteratively on information from previous trees. Their input is
observations at Vienna International Airport as well as output of a high resolution and an
ensemble NWP model. Observations have the highest impact for nowcasts up to a
lead time of +2 h. Afterwards, a mix of observations and NWP forecast
variables generates the most accurate predictions. With lead times longer
than +7 h, NWP output dominates until the predictability limit is reached
at +12 d. For lead times longer than +2 d, output from an ensemble of
NWP models improves the forecast more than using a deterministic but finer
resolved NWP model. The most important predictors for lead times up to
+18 h 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.
Subject
Applied Mathematics,Atmospheric Science,Statistics and Probability,Oceanography
Reference30 articles.
1. Agresti, A.: Categorical Data Analysis, John Wiley & Sons, Inc., 2003. a
2. Bocchieri, J. R. and Glahn, H. R.: Use of Model Output Statistics for
Predicting Ceiling Height, Mon. Weather Rev., 100, 869–879,
https://doi.org/10.1175/1520-0493(1972)100<0869:UOMOSF>2.3.CO;2, 1972. a
3. Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
4. Bühlmann, P. and Hothorn, T.: Boosting Algorithms: Regularization,
Prediction and Model Fitting, Stat. Sci., 22, 477–505,
https://doi.org/10.1214/07-STS242, 2007. a, b, c
5. Christensen, R. H. B.: ordinal – Regression Models for Ordinal Data,
available at: http://www.cran.r-project.org/package=ordinal/
(last access: 7 Juni 2017), R package version 2015.6-28, 2017. a
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