Intelligent Techniques for Identification and Tracking of Meteorological Phenomena that Could Affect Flight Safety
DOI:
https://doi.org/10.18667/cienciaypoderaereo.559Keywords:
Air Control Systems, Meteorological Analysis, Artificial Intelligence, Satellite Pictures, Next Generation Systems, ATC, Air Safety, Meteorological Risk, Cumulonimbus, Tower Cumulus, Air Risk Management.Abstract
In aviation, the meteorological phenomena are one of the most important aspects to be considered in all fly stages, from planning to landing. The development of nowcasting systems in meteorology applied to aviation can support the decision-making process for air traffic controllers and pilots, facilitating the meteorological variables analysis and providing a first interpretation available to all the users of the air system.For this reason, the Center for Technological Development for Defense (CETAD) has as main objective in this document to describe the results of the development of a systematized methodology that uses intelligent techniques for the detection and identification and monitoring of any type of training that by its characteristics can represent a risk to the aviation, generating in turn information of support to the air traffic controller.
For this, it is necessary to detect the convective formations, to classify them, to filter the noise and to individualize them. These types of processes can be automated through the intelligent analysis of products available through the MET Service of Air Navigation Services Providers, like the Colombian Civil Aviation (UAEAC) and the multispectral satellite imagery.
After detection, a group of characteristics allowing the developmet of efficient algorithms capable of monitoring the behavior of the convective formation must be determined. That allows generating forecasts of the characteristics of the convective systems in the short term and this requires to know other variables such as the wind motion in the areas of analysis. This kind of applications integrated with air traffic control systems would reduce the risks due to meteorological factors.
This work brings a procedure based on the combination of different techniques like histograms identification and neural network processing, among others, to identify a potentially hazardous phenomenon and to follow it in time and space. The use of a user-friendly interface let any user have a phenomena interpretation for supporting the decision-making process.
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