Fog Prediction for Road Traffic Safety in a Coastal Desert Region: Improvement of Nowcasting Skills by the Machine-Learning Approach

Bartoková, Ivana; Bott, Andreas; Bartok, Juraj; Gera, Martin

doi:10.1007/s10546-015-0069-x

Cited by 39 publications

(24 citation statements)

References 23 publications

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“…Statistical supervised machine-learning (ML) techniques rely on a training period of historical data which connect forecasts and observed visibility. In this framework, machine-learning algorithms are emerging as suitable methods for detection and prediction of meteorological phenomena [2,15,16]. Some machine-learning methods have been used for low-visibility forecasting, for example artificial networks [17], multiple linear regression [18], and tree-based methods [16].…”

Section: Introductionmentioning

confidence: 99%

“…In this framework, machine-learning algorithms are emerging as suitable methods for detection and prediction of meteorological phenomena [2,15,16]. Some machine-learning methods have been used for low-visibility forecasting, for example artificial networks [17], multiple linear regression [18], and tree-based methods [16]. In the literature, ML regression techniques have been used for low-visibility events forecasting by Cornejo-Bueno et al [19] at Valladolid airport, Spain, with a focus on runway visual range.…”

Section: Introductionmentioning

confidence: 99%

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Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

Bari

Ouagabi

2020

SN Appl. Sci.

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Low-visibility conditions are a weather hazard that affects all forms of transport, and accurate forecasting of their spatial coverage is still a challenge for meteorologists, particularly over a large domain. Current predictions of visibility are based on physical parametrizations in mesoscale models and are thus limited with respect to accuracy. This paper examines the use of supervised machine-learning regression techniques (tree-based ensemble, feed-forward neural network and generalized linear methods) to diagnose visibility from operational mesoscale model forecasts over a large domain. To achieve this, hourly forecasts of meteorological parameters in the lower levels of the atmosphere have been used. In the short-range forecasting framework, the machine-learning algorithms were developed to provide hourly forecasts up to 24 h. To assess the performance of the developed models, hourly observed data, collected at 36 synoptic land stations over the northern part of Morocco, have been used. This region is characterized by a heterogeneous topography. The tree-based ensemble methods have shown some improvement in visibility forecasting in comparison with the operational visibility diagnostic scheme based on Kunkel's formula and also with persistence. It is also found that this machine-learning technique performs better when the forecast depends on multiple predictors instead of only a few with very high importance. In addition, their performance is very sensitive to the disproportionality of data availability between daytime and night-time. Furthermore, it is found that the performance decreases when principal components are used instead of raw correlated data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

Bari

Ouagabi

2020

SN Appl. Sci.

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“…Hilliker and Fritsch 1999;Herman and Schumacher 2016). Some machinelearning methods have also been used for low-visibility forecasts; for example, tree-based methods (Dutta and Chaudhuri 2015;Bartokov et al 2015;Dietz et al 2017) and artificial neural networks (Bremnes and Michaelides 2007;Marzban et al 2007;Fabbian et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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

Kneringer

Dietz

Mayr

et al. 2018

Pure Appl. Geophys.

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Abstract-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.

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“…Glahn et al (2017) combined this system with the physically-based forecasts of Benjamin et al (2016) to improve the performance. Other statistical techniques to forecast visibility are, for example, neural networks (e.g., Pasini et al 2001;Marzban et al 2007), Bayesian model averaging (e.g., Roquelaure et al 2009), or decision trees (e.g., Bartoková et al 2015;Dutta and Chaudhuri 2015). Herman and Schumacher (2016) compared various statistical methods for visibility predictions at airports and found that no specific model performs best overall.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Low-Visibility Procedure States with Tree-Based Statistical Methods

Dietz

Kneringer

Mayr

et al. 2018

Pure Appl. Geophys.

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Abstract-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 1 h 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 and standard meteorological variables such as humidity also become relevant.

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Fog Prediction for Road Traffic Safety in a Coastal Desert Region: Improvement of Nowcasting Skills by the Machine-Learning Approach

Cited by 39 publications

References 23 publications

Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

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

Forecasting Low-Visibility Procedure States with Tree-Based Statistical Methods

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