Intelligent Techniques for Identification and Tracking of Meteorological Phenomena that Could Affect Flight Safety
Técnicas inteligentes para la identificación y el seguimiento de fenómenos meteorológicos que podrían afectar la seguridad de vuelo;
Técnicas inteligentes para a identificação e monitoramento de fenômenos meteorológicos que possam afetar a segurança do vôo
dc.creator | Florez Zuluaga, Jimmy Anderson | |
dc.creator | Vargas, Jesús Francisco | |
dc.creator | Reina, Juddy K. | |
dc.date | 2017-12-06 | |
dc.date.accessioned | 2021-06-17T12:58:30Z | |
dc.date.available | 2021-06-17T12:58:30Z | |
dc.identifier | https://publicacionesfac.com/index.php/cienciaypoderaereo/article/view/559 | |
dc.identifier | 10.18667/cienciaypoderaereo.559 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12963/247 | |
dc.description | 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. | eng |
dc.description | En la aviación, los fenómenos meteorológicos son uno de los aspectos más importantes para tener en cuenta en todas las etapas de vuelo, desde la planificación hasta el aterrizaje. El desarrollo de sistemas de predicción meteorológica aplicados a la aviación puede apoyar el proceso de toma de decisiones de los controladores de tráfico aéreo y los pilotos, facilitando el análisis de las variables meteorológicas y proporcionando una primera interpretación a disposición de todos los usuarios del sistema aéreo. Por esta razón el Centro de desarrollo Tecnológico para la Defensa (CETAD) tiene como principal objetivo en este documento describir los resultados del desarrollo de una metodología sistematizada que utiliza técnicas inteligentes para la detección, identificación y seguimiento de cualquier tipo de formación que por sus características pueda representar un riesgo para la aviación, generando a su vez información de soporte al controlador aéreo.Para esto es necesario primero identificar las formaciones convectivas, clasificarlas, filtrar el ruido e individualizarlas. Este tipo de procesos pueden ser automatizados a través del análisis inteligente de productos disponibles en cualquier sistema aéreo como las imágenes satelitales multiespectrales. Posterior a una identificación, se deben determinar un grupo de características que permitan desarrollar algoritmos eficientes capaces de realizar un seguimiento del comportamiento de la formación convectiva, que permita generar pronósticos de las características de los sistemas convectivos en el corto plazo, para lo que se requiere conocer otras variables como el viento en las áreas de análisis.Este tipo de aplicaciones integradas a los sistemas de control de tráfico aérea disminuirían los riesgos debidos factores meteorológicos. | spa |
dc.description | Na aviação, os fenômenos meteorológicos são um dos aspectos mais importantes a ter em conta em todas as etapas do vôo, do planejamento ao pouso. O desenvolvimento de sistemas de previsão do tempo aplicados à aviação pode apoiar o processo de tomada de decisão de controladores e pilotos de tráfego aéreo, facilitando a análise de variáveis meteorológicas e fornecendo uma primeira interpretação disponível para todos os usuários do sistema de ar .Por esta razão, o Centro de Desenvolvimento Tecnológico para a Defesa (CETAD) tem como objetivo principal neste documento descrever os resultados do desenvolvimento de uma metodologia sistematizada que utiliza técnicas inteligentes para a detecção, identificação e monitoramento de qualquer tipo de treinamento que, devido às suas características pode representar um risco para a aviação, gerando, por sua vez, informações de suporte para o controlador de tráfego aéreo.Para isso, é necessário primeiro identificar as formações convectivas, classificá-las, filtrar o ruído e individualizá-las. Este tipo de processo pode ser automatizado através da análise inteligente de produtos disponíveis em qualquer sistema de ar, como imagens de satélite multispectral.Após uma identificação, um grupo de características deve ser determinado que permite o desenvolvimento de algoritmos eficientes capazes de rastrear o comportamento da formação convectiva, o que permite gerar previsões das características dos sistemas convectivos no curto prazo, para o qual é necessário Conheça outras variáveis como o vento nas áreas de análise.Este tipo de aplicações integradas aos sistemas de controle de tráfego aéreo reduziria os riscos devidos a fatores meteorológicos. | por |
dc.format | application/pdf | |
dc.format | text/html | |
dc.language | spa | |
dc.publisher | Escuela de Postgrados de la Fuerza Aérea Colombiana | spa |
dc.relation | https://publicacionesfac.com/index.php/cienciaypoderaereo/article/view/559/726 | |
dc.relation | https://publicacionesfac.com/index.php/cienciaypoderaereo/article/view/559/728 | |
dc.relation | /*ref*/Cheng, H. (2017) Cloud tracking using clusters of feature points for accurate solar irradiance nowcasting. Renew. Energy, vol. 104, pp. 281 -289. https://doi.org/10.1016/j.renene.2016.12.023 | |
dc.relation | /*ref*/Roy, C; Kovordányi, R. (2012) Tropical cyclone track forecasting techniques - A review. Atmos. Res., vol. 104-105, pp. 40-69. https://doi.org/10.1016/j.atmosres.2011.09.012 | |
dc.relation | /*ref*/Henke, D.; Smyth, R; Haffke, C. and Magnusdottir, G. (2012) Automated analysis of the temporal behavior of the double Intertropical Convergence Zone over the east Pacific. Remote Sens. Environ., vol. 123, pp. 418-433. https://doi.org/10.1016/j.rse.2012.03.022 | |
dc.relation | /*ref*/Sieglaffi J.; Cronce, L.; Feltz, W. (2014) Improving satellite-based convective cloud growth monitoring with visible optical depth retrievals. J. Appl. Meteorol. Climatol. 53(2). pp. 506-520. https://doi.org/10.1175/JAMC-D-13-0139.1 | |
dc.relation | /*ref*/Parikh, J.; DaPonte, J.; Vitale, J. and Tselioudis, G. (1999) An evolutionary system for recognition and tracking of synoptic-scale storm systems. Pattern Recognit. Lett. 20(11 -13). pp. 1389-1396. https://doi.org/10.1016/S0167-8655(99)00110-5 | |
dc.relation | /*ref*/Henken, C; Schmeits, M.; Deneke, H. and Roebeling, R. (2011) Using MSG-SEVIRI cloud physical properties and weather radar observations for the detection of Cb/TCu clouds. J. Appl. Meteorol. Climatol. 50(7). pp. 1587-1600. https://doi.org/10.1175/2011JAMC2601.1 | |
dc.relation | /*ref*/Yin, S.; Qian, Y. and Gong, M. (2017) Unsupervised Hierarchical Image Segmentation through Fuzzy Entropy Maximization. Pattern Recognit., vol. 68. pp. 245-259. https://doi.org/10.1016/j.patcog.2017.03.012 | |
dc.relation | /*ref*/Mahani, S.; Gao, X.; Sorooshian, S.; and Imam, B. (2000). Estimating cloud top height and spatial displacement from scan-synchronous GOES images using simplified IR-based stereoscopic analysis. J. Geophys. Res. Atmos. 105(D12). pp. 15597-15608. https://doi.org/10.1029/2000JD900064 | |
dc.relation | /*ref*/Rillo, V; Zoilo, A.; Mercogliano, R; and Galdi, C. (2015). Detection and forecast of convective clouds using MSG data for aviation support. 2nd IEEE Int. Work. Metrol. Aerospace, Me-troaerosp. 2015 - Proc., no. Cmcc, pp. 301 -305. https://doi.org/10.1109/MetroAeroSpace.2015.7180672 | |
dc.relation | /*ref*/Chethan, H.; Raghavendra, R.; and Hemantha, G. (2009). Texture Based Approach for Cloud Classification Using SVM. 2009 IEEE Int. Conf. Adv. RecentTechnol. Commun. Comput. (ARTCom '09), pp. 688-690. https://doi.org/10.1109/ARTCom.2009.43 | |
dc.relation | /*ref*/Flnke.C.; Butts, 1; Mills, R.;and Grlmalla.M. (2013). Enhancing the security of aircraft surveillance In the next generation air traffic control system. Int. J. Crlt. Infrastruct. Prot. 6(1). pp. 3-11. https://doi.org/10.1016/j.ijcip.2013.02.001 | |
dc.relation | /*ref*/Ahlstrom, U. (2005). Work domain analysis for air traffic controller weather displays. J. Safety Res. 36(2). pp. 159-169. https://doi.org/10.1016/j.jsr.2005.03.001 | |
dc.relation | /*ref*/Jeon, D.; Eun, Y.; and Kim, H. (2015). Estimation fusion with radar and ADS-B for air traffic surveillance. Int. J. Control. Autora Syst. 13(2). pp. 336-345. https://doi.org/10.1007/s12555-014-0060-1 | |
dc.relation | /*ref*/Ahlstrom, U. and Jaggard, E. (2010). Automatic Identification of risky weather objects In line of flight (AIRWOLF). Transp. Res. PartC Emerg.Technol. 18(2). pp. 187-192. https://doi.org/10.1016/j.trc.2009.06.001 | |
dc.relation | /*ref*/Ulfbratt, E.; and McConvIlle, J. (2008). Comparison of the SE-SAR and NextGen Concepts of Operations. NCOIC Aviat. IPT, vol. 1.0, p. 22. | |
dc.relation | /*ref*/Brooker, P. (2008). SESAR and NextGen: Investing in new paradigms. J. Navlg. 61 (2). pp. 195-208. https://doi.org/10.1017/S0373463307004596 | |
dc.relation | /*ref*/Martinez, J. (1998). El futuro de la gestión, la gestión del futuro. Dlr. y progreso, no. 160, pp. 82-86. | |
dc.relation | /*ref*/Cahill, J. (2016). A Safety Impact Quantification Approach for Early Stage Innovative Aviation Concepts Application to a Third Pilot Adaptive Automation Concept, no. November, 2016. | |
dc.relation | /*ref*/Capa, E. (2015) Universidad Politécnica De Madrid. | |
dc.relation | /*ref*/Ramírez-Fernández, S.; Llzarazo-Salcedo, I. (2014). Clasificación digital de masas nubosas a partir de Imágenes meteorológicas usando algoritmos de aprendizaje de maquina. Rev. Fac. Ing. 1(73). pp. 43-57. | |
dc.relation | /*ref*/Stone, M.;and Anderson, J. (1989). Advances ¡n prlmary-radar technology. Lincoln Lab. J., pp. 363-380. | |
dc.relation | /*ref*/Weber, M. (1986). Assessment of ASR-9 Weather Channel Performance : Analysis and Simulation. | |
dc.relation | /*ref*/Welpert, A.; Flannesen, R. (2008). Enhanced weather Information for air traffic controllers using comprehensive sensor and data assimilation procedures. Eur. Radar Conf., no. October, pp. 184-187. | |
dc.relation | /*ref*/Flarrlngton, J. (2009). Weather services In the NextGen Era. Aviat. Int. News, no. January, pp. 34-37. | |
dc.relation | /*ref*/McCrea, M.; Sherali, FI.; Tranl, A. (2008). A probabilistic framework for weather-based rerouting and delay estimations within an Airspace Planning model.Transp. Res. Part C Emerg. Technol. 16(4). pp. 410-431. https://doi.org/10.1016/j.trc.2007.09.001 | |
dc.relation | /*ref*/Wiggins, M. (2014). Differences In situation assessments and prospective diagnoses of simulated weather radar returns amongst experienced pilots. Int. J. Ind. Ergon. 44(1). pp. 18-23. https://doi.org/10.1016/j.ergon.2013.08.006 | |
dc.relation | /*ref*/Peak, J. and Tag, P. (1994). Segmentation of satellite Imagery using hierarchical thresholding and neural networks. Journa of Applied Meteorology, vol. 33. pp. 605-616. https://doi.org/10.1175/1520-0450(1994)033<0605:SOSIUH>2.0.CO;2 | |
dc.relation | /*ref*/Desbois, M.; Seze, G. and Szejwach, G. (1982). Automatic classification of clouds on Meteosat imagery - Application to high-level clouds. Journal of Applied Meteorology. 21 (3). pp. 401-412. https://doi.org/10.1175/1520-0450(1982)021<0401:ACOCOM>2.0.CO;2 | |
dc.relation | /*ref*/Azimi-Sadjadl, M.; and Zekavat, S. (2000). Cloud classification using support vector machines. IGARSS 2000. IEEE 2000 Int. Geosci. Remote Sens. Symp. Tak. Pulse Planet Role Remote Sens. Manag. Environ. Proc. (Cat. No.00CFI37120). vol. 2. pp. 669-671. | |
dc.relation | /*ref*/Bedard Jr., A. (2015). AVIATION METEOROLOGY | Aviation Weather Flazards, Second Edi., vol. 1. Elsevier. https://doi.org/10.1016/B978-0-12-382225-3.00075-X | |
dc.relation | /*ref*/Senhaml. (1959). Guía De Meteorología General. | |
dc.relation | /*ref*/Smoot, D. (2015) Global Flidrology And Climate Center. [Online]. Available: https://weather.msfc.nasa.gov/GOES/satellite-description.html. | |
dc.relation | /*ref*/Zaccolo, M. (2002). Good Features to Track. Methods Mol. Biol. 178(Dec). pp. 255-8. | |
dc.relation | /*ref*/Shibata, M.; Yasuda, Y; and Ito, M. (2008). Moving object detection for active camera based on optical flow distortion. Proc. 17th World Congr., pp. 14720-14725. https://doi.org/10.3182/20080706-5-KR-1001.02492 | |
dc.relation | /*ref*/S. N. de M. e Flldrologla, Capitulo 13. Pronóstico Meteorológico. In: GUIA DE METEOROLOGIA GENERAL, pp. 1-16. | |
dc.relation | /*ref*/(2012) Manual del Sistema Mundial de Proceso de Datos y de Predicción. | |
dc.relation | /*ref*/Matas, J.; Galambos.C.; and Klttler, J. (2000). Robust Detection of Lines Using the Progressive Probabilistic Plough Transform. Comput. Vis. Image Underst. 78(1) pp. 119-137. https://doi.org/10.1006/cviu.1999.0831 | |
dc.relation | /*ref*/Puca, S.; Biron, D.; De Leonlbus, L; Rosci, R; and Zauli, F. Improvements on numérica I'object'detection and nowcasting of convective cell with the use of seviri data (ir and wv channels ) and neural technlqhes. | |
dc.source | Ciencia y Poder Aéreo; Vol. 12 No. 1 (2017): Enero - Diciembre; 24-35 | eng |
dc.source | Ciencia y Poder Aéreo; Vol. 12 Núm. 1 (2017): Enero - Diciembre; 24-35 | spa |
dc.source | Ciencia y Poder Aéreo; v. 12 n. 1 (2017): Enero - Diciembre; 24-35 | por |
dc.source | 2389-9468 | |
dc.source | 1909-7050 | |
dc.subject | Air Control Systems | eng |
dc.subject | Meteorological Analysis | eng |
dc.subject | Artificial Intelligence | eng |
dc.subject | Satellite Pictures | eng |
dc.subject | Next Generation Systems | eng |
dc.subject | ATC | eng |
dc.subject | Air Safety | eng |
dc.subject | Meteorological Risk | eng |
dc.subject | Cumulonimbus | eng |
dc.subject | Tower Cumulus | eng |
dc.subject | Air Risk Management. | eng |
dc.subject | Sistemas de control de aire | spa |
dc.subject | análisis meteorológico | spa |
dc.subject | inteligencia artificial | spa |
dc.subject | imágenes satelitales | spa |
dc.subject | sistemas de próxima generación | spa |
dc.subject | ATC | spa |
dc.subject | seguridad aérea | spa |
dc.subject | riesgo meteorológico | spa |
dc.subject | cumulonimbus | spa |
dc.subject | cúmulo de la torre | spa |
dc.subject | gestión del riesgo del aire. | spa |
dc.subject | Sistemas de controle de ar | por |
dc.subject | análise meteorológica | por |
dc.subject | inteligência artificial | por |
dc.subject | imagens de satélite | por |
dc.subject | sistemas de próxima geração | por |
dc.subject | ATC | por |
dc.subject | segurança aérea | por |
dc.subject | risco meteorológico | por |
dc.subject | cumulonimbus | por |
dc.subject | cluster de torre | por |
dc.subject | gerenciamento de risco aéreo | por |
dc.title | Intelligent Techniques for Identification and Tracking of Meteorological Phenomena that Could Affect Flight Safety | eng |
dc.title | Técnicas inteligentes para la identificación y el seguimiento de fenómenos meteorológicos que podrían afectar la seguridad de vuelo | spa |
dc.title | Técnicas inteligentes para a identificação e monitoramento de fenômenos meteorológicos que possam afetar a segurança do vôo | por |
dc.type | info:eu-repo/semantics/article | |
dc.type | info:eu-repo/semantics/publishedVersion |
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