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We address the prediction of low-visibility events at airports using machine-learning regression. The proposed model successfully forecasts low-visibility events in terms of the runway visual range at the airport, with the use of support-vector regression, neural networks (multi-layer perceptrons and extreme-learning machines) and Gaussian-process algorithms. We assess the performance of these algorithms based on real data collected at the Valladolid airport, Spain. We also propose a study of the atmospheric variables measured at a nearby tower related to low-visibility atmospheric conditions, since they are considered as the inputs of the different regressors. A pre-processing procedure of these input variables with wavelet transforms is also described. The results show that the proposed machine-learning algorithms are able to predict low-visibility events well. The Gaussian process is the best algorithm among those analyzed, obtaining over 98% of the correct classification rate in low-visibility events when the runway visual range is $${>}$$ 1000 m, and about 80% under this threshold. The performance of all the machine-learning algorithms tested is clearly affected in extreme low-visibility conditions ( $${

The main methods for determining the length of the runway at the landing stage are shown in the given article. The shortcomings of these methods are proved. Statistical data that shows the need of landing distance recalculation for calculation of the required length of the runway are provided. The advanced mathematical model for calculating the required length of the runway at the landing stage is shown.

We consider the problem of emission of sound by an airplane in the stage of its take-off run along the runway of an airdrome under the conditions of wind action. The aircraft engines play the role of the sources of sound. In the problem, they are modeled by point sources moving in air with a constant acceleration parallel to the plane interface between the acoustic and solid elastic half spaces. The solution of the problem is obtained by using the Fourier integral transformation with respect to time and space coordinates and the method of stationary phase. We also perform the numerical analysis of the spacetime distributions of sound pressure and the levels of sound pressure for the case of take-off of a sixengine An-225 “Mriya” aircraft.

A parameterization of runway visual range (RVR) has been developed using relevant meteorological parameters such as visibility (Vk), relative humidity (RH), temperature (T), precipitation intensity (PI), and precipitation type (PT) measured in years between 2009 and 2011 at Toronto Pearson International Airport during the Canadian Airport Nowcasting Project. The FD12P probe measured PI, Vk, and PT. The observed Vk and PT were tested against data reported by hourly surface observations (SAs). The measured Vk has correlated well with the SA with a correlation coefficient (r) of 0.76 for Vk < 5 km, but the FD12P underestimated visibility by about 20% with a mean difference (MD) of about 196 m. For Vk < 2 km, the FD12P overestimated visibility by about 7% with an MD of 60 m. The SA reported slightly more snow events—22% as compared to 17%—but the FD12P reported many more snow grain cases than the SA. Both the SA and the FD12P reported rain at similar frequency—4% and 5%, respectively. Using a theoretical approach, a parameterization that can be used to determine RVR as a function of Vk has been developed. Using the observed T, RH, and dewpoint temperature (Td), a new parameterization for predicting Vk/RVR in fog has been also developed. These parameterizations agreed with observations (r ≈ 0.8). The parameterizations have been tested using the Canadian Environmental Multiscale Regional model. The results show that when PI, RH, and T are reasonably predicted and the fog events are correctly diagnosed, the model can be used to forecast RVR.

Current research to achieve a technically and socially acceptable short take-off and landing (STOL) aircraft system to help alleviate present air transportation congestion is outlined considering both flight and runway elements. The aerodynamic jet-flap propulsive-lift concept is briefly reviewed with proposed turbofan powered-lift aircraft. Short, single direction, and possibly elevated runways are considered for use in high-land-cost urban areas. Aircraft capability is being developed to enable operations under high crosswind conditions, possibly through the use of crosswind landing gears or crosswind-reducing fences erected along the sides of the runway. Techniques are being developed to insure smooth wind flow over elevated STOL ports, such as on the roofs of buildings; and emergency arresting systems are under study to contain the aircraft on the runway. Heating and grooving of the runway has been explored analytically as a method of keeping the required runway length short by guaranteeing runway friction even under ice and snow conditions.