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Detection of convective clouds using meteorological data fusion for aviation safety support
... Route changes due to external factors, such as air emergencies, weather conditions, and other situations, affect normal airspace flow [15]- [18]. These changes demand a reconfiguration of air operations in the national airspace. ...
Decision support systems serve as support in situations, contexts, or unstructured problems, such as the recognition and classification of patterns and the detection of anomalies or atopic data, through of process of mining modeling and analysis of data. This study shows an approach that uses artificial intelligence techniques and statistical descriptions applied to portions of the airspace that are delimited using Voronoi regions, with the purpose of perform automation of the air route validation process. To this end a systemic methodology compares data generated by aerial surveillance systems with historical data from the air traffic and control system of aircraft operating over Colombian air space. For the characterization of the routes, the mathematical properties of the Voronoi regions segment the airspace, which allows associating the detections that make up the trajectories of the aircraft to regions. The aggregation of points in each region makes it possible to describe the routes with a reduced number of characteristics that serve as input to supervised algorithms that classify the route to which each trajectory belongs with an accuracy greater than 95%. As described in this study, these types of validations are used by command-and-control systems as a basis for supporting the decision-making process.
-The research proposes the analysis of technology management and innovation model, from the experience of twenty Colombian researchers, from a Research, Development and Innovation Centre related to aerospace and defence issues, such as, objective of RDI in defence, the ideation process, the process of developing science, technology and innovation (STI) projects, the organizational structure for RDI, the availability of public and private resources, the project financing process, the corporate willingness to adopt technologies, the support process for the technologies developed and the transfer of results were reviewed. The research was developed under the qualitative method, carrying out a descriptive and non-experimental analysis under an empirical phenomenological design, using interviews as a research instrument. As a result, it was possible to identify parameters to enhance and improve RDI capabilities in aerospace defence projects and generate a spillover effect on other actors, identifying relevant aspects to strengthen.
The decision-making (DM) process in critical environments is a complex process that can be simulated due to current telematic capabilities, which allow the real time interaction of large amounts of data. This document describes the proposed architecture from a research process, developed by the FAC Aerospace Technology Development Center (CETAD), where using computational andexpert system tools, allowed to create acomputational environment for decision maker evaluated his options to preparesfor real events, simulating characteristics, resources and strategies in a real time environment.This document describes an investigation product resulted in a simulation system, based ona combination of fuzzylogic, genetic algorithms and decision trees which let modelledand simulated various entities andtheir automatic response according to simulated patterns and situations, in which, through operators, decision maker can modify entities behaviour, according to parameterized restrictions and physical conditions. Also based on business intelligence tools, reports are generated to evaluate the decisions made. This type of technologies improves planning capacity and facilitatethe decision-making process.System allows simulating any media deployment in national security and critical eventscontext. Thus, a case study was developed for implementation of a supportinnatural disasterscenario simulation.
This paper presents a straightforward approach for safety impact quantification of innovative aviation concepts in early development stages. The safety impact quantification approach provides a high-level and broad overview of the accident risk reduction that may be obtained by the novel concept. The approach uses a systematic assessment of change factors for base event probabilities in a total aviation system risk model, consisting of combinations of event sequence diagrams and fault trees. The approach is illustrated in terms of the assessment of an innovative third pilot adaptive automation concept. The results indicate that this concept can effectively reduce the fatal accident risk.
The surveillance sensor that has been mainly used for target tracking in air traffic control (ATC) environment is radar. The automatic dependent surveillance – broadcasting (ADS-B), which is based on the technologies of global navigation satellite systems, is recently participating in ATC systems. Although ADS-B provides more accurate measurements than does radar, it needs careful considerations for the application of the ATC systems. This is due to the fact that the reliability of ADS-B measurements is dependent upon each aircraft whereas that of radar is not. This study proposes a practical system for the estimation fusion of multiple heterogeneous sensors, which includes radar and ADS-B, whose measurements and sensor characteristics are different from one another. A centralized fusion architecture based on three-dimensional earth-centered earth-fixed (ECEF) common coordinate system is adopted to process the data received asynchronously from multiple heterogeneous sensors. In case of the ADS-B, the validity of the sensor data for each aircraft is checked using not only the accuracy and integrity information of the aircraft, but also a comparison of the ADS-B data with the radar data. This study also proposes variable-sized measurement vectors and matrices for the tracking filter in order to dynamically reflect the availability of the additional measurements from the downlinked aircraft parameters (DAP) which can be obtained from mode-S radar and ADS-B. The simulation results indicate that the proposed fusion system can improve the tracking performance with the advantages of different types of surveillance sensors.
Weather radar systems are an important tool in commercial aviation to safeguard the safety and security of aircraft. However, the utility of weather radar systems lies in the accuracy and the reliability of the interpretations of the displays. The primary aim of this study was to determine whether experienced pilots could be clustered based on their assessments of the turbulence associated with simulated weather radar displays and whether these groups corresponded to differences in experience-related metrics. Sixty one participants completed a series of on-line scenarios in which they were asked to rate the level of turbulence associated with 11 simulated weather radar displays. They were also asked to indicate their confidence in being able to continue the flight for 80 nautical miles in the absence of an alteration in track or altitude. A cluster analysis reliably differentiated two groups of participants and these groups corresponded to differences in the capacity to discriminate between weather radar scenarios. The results also reveal both a lack of reliability in experienced pilots' interpretations of weather radar displays and difficulties associated with classifications of expertise on the basis of experienced-related metrics. At an empirical level, the outcomes have implications for assessments of expertise in domains in which ideal performance is difficult to establish. From an industry perspective, the results reveal important differences in the interpretation of weather radar displays amongst experienced, qualified pilots. This suggests a need for both more effective weather radar design, complemented by more reliable and comprehensive training that focuses on the accurate interpretation of different types of weather radar returns.
A new automated daytime cumulonimbus/towering cumulus (Cb/TCu) cloud detection method for the months of May–September is presented that combines information on cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG) satellites and weather radar reflectivity factors. First, a pixel-based convective cloud mask (CCM) is constructed on the basis of cloud physical properties [cloud-top temperature, cloud optical thickness (COT), effective radius, and cloud phase] derived from SEVIRI. Second, a logistic regression model is applied to determine the probability of Cb/TCu clouds for the collection of pixels that pass the CCM. In this model, MSG-SEVIRI cloud physical properties and weather radar reflectivity factors are used as potential predictor sources. The predictand is derived from aviation routine weather reports (METAR) made by human observers at Amsterdam Airport Schiphol for 2004–07. Results show that the CCM filters out >70% of the “no” events (no Cb/TCu cloud) and that >93% of the “yes” events (Cb/TCu cloud) are retained. Most skillful predictors are derived from radar reflectivity factors and the COT of high resolution. The derived probabilities from the combined MSG and radar method clearly show skill over sample climatology. Probability thresholds are used to convert derived probabilities into derived group memberships (i.e., yes/no Cb/TCu clouds). When comparing verification scores between the combined MSG and radar method and either the radar-only method or the MSG-only method, the combined MSG and radar method shows slightly better performance. When comparing the combined MSG and radar method with the current Royal Netherlands Meteorological Institute (KNMI) radar-based Cb/TCu cloud detection method, the two methods show comparable probability of detection, but the former shows a false-alarm ratio that is about 8% lower. Moreover, a big advantage of the newly developed method is that it provides probabilities, in contrast to the current KNMI method.
The fuzzy c-partition entropy has been widely adopted as a global optimization technique for finding the optimal thresholds when performing multilevel gray image segmentation. Nevertheless, existing fuzzy c-partition entropy approaches generally have two limitations, i.e., partition number c needs to be manually tuned for different input and the methods can process grayscale images only. To address these two limitations, an unsupervised multilevel segmentation algorithm is presented in this paper. The core step of our algorithm is a bi-level segmentation operator, which uses binary graph cuts to maximize both fuzzy 2-partition entropy and segmentation smoothness. By iteratively performing this bi-level segmentation operator, multilevel image segmentation is achieved in a hierarchical manner: Starting from the input color image, our algorithm first picks the color channel that can best segment the image into two labels, and then iteratively selects channels to further split each labels until convergence. The experimental results demonstrate the presented hierarchical segmentation scheme can efficiently segment both grayscale and color images. Quantitative evaluations over classic gray images and the Berkeley Segmentation Database show that our method is comparable to the state-of-the-art multi-scale segmentation methods, yet has the advantage of being unsupervised, efficient, and easy to implement.
In this work, we propose a system to track the clouds and predict relevant events based on all-sky images. To deal with the nature of variable appearance of clouds, we use clusters of feature points to perform tracking. We propose an enhanced clustering algorithm that does not require prior knowledge of number of clusters. The proposed clustering algorithm can successfully separate feature points into groups with reasonable sizes and ranges. In the tracking process, merging and splitting of clouds are handled via checking matched pairs of feature points among different clusters. Afterwards, the tracking information is utilized to predict if the sun will be covered or obscured by clouds within the prediction horizon. Features are extracted from the tracked feature points and a Markov chain model is designed to perform ramp-down event prediction. The obscuration and ramp-down events have an important impact on solar irradiance. The experiments have shown that the proposed system can substantially enhance the accuracy of solar irradiance nowcasting on a challenging dataset.
Convective clouds are among the most dangerous meteorological phenomena for aviation because they are
responsible of the presence of thunderstorms causing heavy rains, hailstorms, lightnings, wind shear, turbulence and icing phenomena. For this reason it is crucial to detect and early forecast them. In the present work different algorithms have been implemented for reaching this aim using Meteosat Second
Generation satellite data and comparing brightness temperatures of two satellite images in different channels. In addition convective clouds have been simplified in order to report on board only
the relevant information about hazardous areas to be avoided during the flight and to reduce the information weight. In order to provide forecasts in the following 24-48 hours, the algorithms developed have been applied to synthetic satellite images produced by the radiative transfer model RTTOV, a model simulating the radiances and brightness temperatures as they could be seen from satellite
The use of geostationary satellites for monitoring the development of deep convective clouds has been recently well documented. One such approach, the University of Wisconsin Cloud-Top Cooling Rate (CTC) algorithm, utilizes frequent Geostationary Operational Environmental Satellite (GOES) observations to diagnose the vigor of developing convective clouds through monitoring cooling rates of infrared window brightness temperature imagery. The CTC algorithm was modified to include GOES visible optical depth retrievals for the purpose of identifying growing convective clouds in regions of thin cirrus clouds. An automated objective skill analysis of the two CTC versions (with and without the GOES visible optical depth) versus a variety of Next Generation Weather Radar (NEXRAD) fields was performed using a cloud-object tracking system developed at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies. The skill analysis was performed in a manner consistent with a recent study employing the same cloud-object tracking system. The analysis indicates that the inclusion of GOES visible optical depth retrievals in the CTC algorithm increases probability of detection and critical success index scores for all NEXRAD fields studied and slightly decreases false alarm ratios for most NEXRAD thresholds. In addition to better identifying vertically growing storms in regions of thin cirrus clouds, the analysis further demonstrates that the strongest cooling rates associated with developing convection are more reliably detected with the inclusion of visible optical depth and that storms that achieve intense reflectivity and large radar-estimated hail exhibit strong cloud-top cooling rates in much higher proportions than they do without the inclusion of visible optical depth.
The U.S. air traffic control system is reliant on legacy systems that artificially limit air traffic capacity. With the demand for air transportation increasing each year, the U.S. Federal Aviation Administration has introduced the Next Generation (NextGen) upgrade to modernize the air traffic control system. Automatic Dependent Surveillance-Broadcast (ADS-B), a key component of the NextGen upgrade, enables an aircraft to generate and broadcast digital messages that contain the GPS coordinates of aircraft. The incorporation of ADS-B is intended to provide enhanced accuracy and efficiency of surveillance as well as aircraft safety. The open design of the system, however, introduces some security concerns. This paper evaluates the limitations of the legacy systems currently used in air traffic control and explores the feasibility of employing format-preserving encryption, specifically the FFX algorithm, in the ADS-B environment. The ability of the algorithm to confuse and diffuse predictable message input is examined using message entropy as a metric. Based on the analysis, recommendations are provided that highlight areas which should be examined for inclusion in the ADS-B upgrade plan.
According to a recent report of the Intergovernmental Panel on Climate Change, the frequency of certain climate extremes is expected to increase under the influence of climate change. This review presents potential direct and indirect effects of such extremes as well as other severe weather and hydro-meteorological events on the occurrence of hazards in food produced by various agricultural systems. In addition, we review the applicability of early warning systems to warn of the development of food safety hazards induced by natural disasters, with climate-change-induced extreme events as case in point. Monitoring systems focused on food safety hazards may miss - or pick up with delay - the occurrence of new hazards or known hazards in food products in which they previously did not occur. We conclude that, by better use of the available information (being plant-, animal-, human disease-focused systems monitoring weather and other environmental conditions and/or systems collecting publications on the internet), the negative impact of severe natural events on food safety can be minimized.
This review describes the fundamental assumptions and current methodologies of the two main kinds of environmental forecast; the first is valid for a limited period of time into the future and over a limited space–time ‘target’, and is largely determined by the initial and preceding state of the environment, such as the weather or pollution levels, up to the time when the forecast is issued and by its state at the edges of the region being considered; the second kind provides statistical information over long periods of time and/or over large space–time targets, so that they only depend on the statistical averages of the initial and ‘edge’ conditions. Environmental forecasts depend on the various ways that models are constructed. These range from those based on the ‘reductionist’ methodology (i.e., the combination of separate, scientifically based, models for the relevant processes) to those based on statistical methodologies, using a mixture of data and scientifically based empirical modeling. These are, as a rule, focused on specific quantities required for the forecast. The persistence and predictability of events associated with environmental and turbulent flows and the reasons for variation in the accuracy of their forecasts (of the first and second kinds) are now better understood and better modeled. This has partly resulted from using analogous results of disordered chaotic systems, and using the techniques of calculating ensembles of realizations, ideally involving several different models, so as to incorporate in the probabilistic forecasts a wider range of possible events. The rationale for such an approach needs to be developed. However, other insights have resulted from the recognition of the ordered, though randomly occurring, nature of the persistent motions in these flows, whose scales range from those of synoptic weather patterns (whether storms or ‘blocked’ anticyclones) to small scale vortices. These eigen states can be predicted from the reductionist models or may be modeled specifically, for example, in terms of ‘self-organized’ critical phenomena. It is noted how in certain applications of turbulent modeling its methods are beginning to resemble those of environmental simulations, because of the trend to introduce ‘on-line’ controls of the turbulent flows in advanced flows in advanced engineering fluid systems. In real time simulations, for both local environmental processes and these engineering systems, maximum information is needed about the likely flow patterns in order to optimize both the assimilation of limited real-time data and the use of limited real-time computing capacity. It is concluded that philosophical studies of how scientific models develop and of the concept of determinism in science are helpful in considering these complex issues.
This paper presents new methods for an automated analysis of the double InterTropical Convergence Zone (dITCZ) phenomena on a daily time scale over the east Pacific. Long-term Geostationary Operational Environmental Satellite (GOES) visible and infrared data are used to spatially identify and segment the convection zones over the east Pacific basin on both sides of the equator and to track the temporal variability of the ITCZ, specifically to identify cases of dITCZs, northern or southern ITCZ, or non-presence events. For the segmentation approach, image processing techniques are developed to extract information about the spatial features of the ITCZ in both hemispheres for each satellite image. These features serve as input to a temporal classification algorithm that is based on a combination of hidden semi Markov model (HsMM) and support vector machine (SVM) methods. The performance of the proposed method is competitive with human experts and the methodology can thus be used to conduct an in-depth analysis of the dITCZ. Such an analysis could provide precise information for refining existing weather and climate models over the sparsely observed east Pacific where the dITCZ is greatly over-represented in most models.
Up to 60% of all delays in aviation are related to weather. Consequently, the quality and availability of meteorological information has a major economic impact on all stakeholders in the aviation system. However, the wide variety of critical weather phenomena such as heavy thunderstorms (including strong wind and wind shear), lightning, turbulence, sleet, hail and snow poses still a challenge to detection technologies, requiring a variety of different observation sensors to provide integrated information to the operational decision-makers in ATC. This paper gives an introductional overview about recent dual polarization weather radar technologies with respect to the potential to enhance the information content for air traffic controllers. Furthermore other relevant sensors sources such as netted dual polarization weather radars, radiosondes and their possible combination with LLWAS systems are described in brief. The possibility to generate classified sensor combination products in the Eurocontrol Asterix format (all purpose structured eurocontrol surveillance information exchange) will be presented.
Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and climatological characteristics of the various cyclone formation basins are not similar, which entail that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global forecasting techniques need to be applied together with basin-specific techniques to increase the forecast accuracy. As cyclone track is governed by a range of factors variations in weather conditions, wind pressure, sea surface temperature, air temperature, ocean currents, and the earth's rotational force―the coriolis force, it is a formidable task to combine these parameters and produce reliable and accurate forecasts. In recent years, the availability of suitable data has increased and more advanced forecasting techniques have been developed, in addition to old techniques having been modified. In particular, artificial neural network based techniques are now being considered at meteorological offices. This new technique uses freely available satellite images as input, can be run on standard PCs, and can produce forecasts with good accuracy. For these reasons, artificial neural network based techniques seem especially suited for developing countries which have limited capacity to forecast cyclones and where human casualties are the highest.
SESAR is Europe's ‘Single European Sky Air traffic Research system’. NextGen is the USA's ‘Next Generation Air Transport System’. SESAR and NextGen are developments targeted at post 2020. The common vision is to integrate and implement new technologies to improve air traffic management (ATM) performance – a ‘new paradigm’. SESAR and NextGen combine increased automation with new procedures to achieve safety, economic, capacity, environmental, and security benefits. The systems do not have to be identical, but must have aligned requirements for equipment standards and technical interoperability.
A key component is a ‘cooperative surveillance’ model, where aircraft are constantly transmitting their position (from navigational satellites), flight path intent, and other useful aircraft parameters – known as ADS-B (Automatic Dependent Surveillance-Broadcast). The focus for planning and executing system operations will increasingly be aircraft 4D trajectories: a 4D trajectory is the aircraft path, three space dimensions plus time, from gate-to-gate, i.e. including the path along the ground at the airport.
In analysing potential major ATM system changes, a simple division into five Key Test areas might be: Safety Credibility, Operational Concept, Technological Feasibility, Benefits and Costs, and Transition Path. The main attention here is on Benefits and Costs of SESAR. The strategic challenge will be to convince customers and stakeholders of the benefits of paradigm shift expenditure, given the associated impacts on future user charges, aircraft equipment investments and public expenditure. The analysis here shows that the existing cost benefit analysis results for SESAR are not particularly robust, possibly over-estimating Net Present Values by some tens of € billions.
The existing algorithm Cb-TRAM was developed to identify, track, and nowcast
thunderstorm clouds in different development stages (ZINNER et al., 2008).
The tracking of deteced clouds is based on the so-called ”pyramidal image matcher”.
Applying this tracking algorithm also to radar data resulted in the development of
the redar tracker Rad-TRAM. Rad-TRAM is introduced here and used in parallel
with Cb-TRAM for three cases of thunderstorm occurrence in Central Europe.
The purpose of the study is threefold. Firstly, to test the ability of both trackers
to track these features. Secondly, to compare position and tracks of cloud and
radar cells by overlaying cell strcutures of both sytems thereby enabling both a
visual and a statistical analysis. Thirdly, to test the nowcasting performance of
both trackers against extrapolations in time based on Lagrangian persistence. It
is found that both trackers are able to detect and track the thunderstorms. For all
observation times a percentage of about 70 % of the satellite detected clouds in
development stage ”mature” overlap with radar cells when applying a minimum
overlap criterion, i.e. one pixel of both cell types. Furthermore, it is found that
Cb-TRAM as well as Rad-TRAM nowcasts for 15, 30, 45, and 60 minutes outperform
extrapolations based on persistence. The results are discussed taking into
account the different data bases used and specific thresholds in both algorithms.
Adverse weather conditions are hazardous to flight and contribute to re-routes and delays. This has a negative impact on the National Airspace System (NAS) due to reduced capacity and increased cost. In today’s air traffic control (ATC) system there is no automated weather information for air traffic management decision-support systems. There are also no automatic weather decision-support tools at the air traffic controller workstation. As a result, air traffic operators must integrate weather information and traffic information manually while making decisions. The vision in the Next Generation Air Transportation System (NextGen) includes new automation concepts with an integration of weather information and decision-making tools. Weather-sensitive traffic flow algorithms could automatically handle re-routes around weather affected areas; this would optimize the capacity during adverse conditions. In this paper, we outline a weather probe concept called automatic identification of risky weather objects in line of flight (AIRWOLF). The AIRWOLF operates in two steps: (a) derivation of polygons and weather objects from grid-based weather data and (b) subsequent identification of risky weather objects that conflict with an aircraft’s line of flight. We discuss how the AIRWOLF concept could increase capacity and safety while reducing pilot and air traffic operator workload. This could translate to reduced weather-related delays and reduced operating costs in the future NAS.
In this paper, we develop a novel severe weather-modeling paradigm to be applied within the context of a large-scale Airspace Planning and collaborative decision-making model in order to reroute flights with respect to a specified probability threshold of encountering severe weather, subject to collision safety, airline equity, and sector workload considerations. This approach serves as an alternative to the current practice adopted by the Federal Aviation Administration (FAA) of adjusting flight routes in accordance with the guidelines specified in the National Playbook. Our innovative contributions in this paper include (a) the concept of “Probability-Nets” and the development of discretized representations of various weather phenomena that affect aviation operations; (b) the integration of readily accessible severe weather probabilities from existing weather forecast data provided by the National Weather Service; (c) the generation of flight plans that circumvent severe weather phenomena with specified probability threshold levels, and (d) a probabilistic delay assessment methodology for evaluating planned flight routes that might encounter potentially disruptive weather along its trajectory. Additionally, we conduct an economic benefit analysis using a k-means clustering mechanism in concert with our delay assessment methodology in order to evaluate delay costs and system disruptions associated with variations in probability-net refinement-based information. Computational results and insights are presented based on flight test cases derived from the Enhanced Traffic Management System data provided by the FAA and using weather scenarios derived from the Model Output Statistics forecast data provided by the National Weather Service.
An evolutionary system was developed for generation of complete tracks of northern midlatitude synoptic-scale storm systems based on optical flow and cloud motion analyses of global satellite-based datasets produced by the International Satellite Cloud Climatology Project (ISCCP). The tracking results were compared with low sea level pressure anomaly (SLPA) tracks obtained from the NASA Goddard Institute for Space Studies (GISS). The SLPA tracks were produced at GISS by analysis of meteorological, ground-based National Center for Environmental Prediction (NCEP) datasets. Results from the evolutionary system were also compared with results from using (a) the k-nearest neighbor rule (k-NN) and (b) self-organizing maps (SOM) to determine correspondences between consecutive locations within a track. The consistency of our evolutionary storm tracking results with the behavior of the low sea level pressure anomaly tracks, the ability of our evolutionary system to generate and evaluate complete tracks, and the close comparison between the results obtained by the evolutionary, k-NN, and SOM analyses of the ISCCP-derived datasets at tracking steps in which proximity or optical flow information sufficed to determine movement, demonstrate the applicability and the potential of evolutionary systems for tracking midlatitude storm systems through low-resolution ISCCP cloud product datasets.
Adverse weather conditions have a major impact on National Airspace System (NAS) operations. They create safety hazards for pilots, constrain the usable airspace for air traffic control (ATC), and reduce the overall capacity of the NAS. A system-wide dissemination of weather information to controllers could theoretically improve safety and efficiency.
However, it is currently unclear what weather information would be beneficial for tactical operations. Furthermore, no previous research has empirically evaluated optimal presentation designs for ATC weather displays. Ill-designed weather displays can cause safety hazards by presenting redundant information (i.e., by increasing the cognitive load) and display clutter (e.g., by interfering with the visual extraction of traffic data).
In the present paper, we outline our use of cognitive work analysis (CWA) techniques for the assessment of weather information needs for terminal controllers.
Specifically, we describe how the CWA modeling tools helped us reveal instances in the terminal domain where weather information is lacking or insufficiently disseminated. We used our CWA results to drive the development of weather display concepts and to set up a high-fidelity simulation capability.
By means of high-fidelity simulations, we can empirically evaluate controller weather information needs in order to propose weather displays for increased aircraft safety and efficiency of terminal operations.
Comparison of the SESAR and NextGen Concepts of Operations
- E Ulfbratt
- J Mcconville
Weather services in the NextGen Era
- J Harrington
El futuro de la gestión, la gestión del futuro
- J C Martinez Tejerina
Manual de procedimientos operativos de meteorologia aeronautica
- D De
- N Aerea