Low-visibility forecasts for different flight planning horizons using tree-based boosting models
<p>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. I...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-06-01
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| Series: | Advances in Statistical Climatology, Meteorology and Oceanography |
| Online Access: | https://www.adv-stat-clim-meteorol-oceanogr.net/5/101/2019/ascmo-5-101-2019.pdf |
| Summary: | <p>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 <span class="inline-formula">+2</span> h. Afterwards, a mix of observations and NWP forecast
variables generates the most accurate predictions. With lead times longer
than <span class="inline-formula">+7</span> h, NWP output dominates until the predictability limit is reached
at <span class="inline-formula">+12</span> d. For lead times longer than <span class="inline-formula">+2</span> 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
<span class="inline-formula">+18</span> 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.</p> |
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| ISSN: | 2364-3579 2364-3587 |