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This work presents an analysis of low-visibility event persistence and prediction at Villanubla Airport (Valladolid, Spain), considering Runway Visual Range (RVR) time series in winter. The analysis covers long- and short-term persistence and prediction of the series, with different approaches. In the case of long-term analysis, a Detrended Fluctuation Analysis (DFA) approach is applied in order to estimate large-scale RVR time series similarities. The short-term persistence analysis of low-visibility events is evaluated by means of a Markov chain analysis of the binary time series associated with low-visibility events. We finally discuss an hourly short-term prediction of low-visibility events, using different approaches, some of them coming from the persistence analysis through Markov chain models, and others based on Machine Learning (ML) techniques. We show that a Mixture of Experts approach involving persistence-based methods and Machine Learning techniques provides the best results in this prediction problem.

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 ( $${

Two studies (Boeing 777 and 737 simulators) examined flight crews’ use of an Enhanced Flight Vision System (EFVS) for taxiing in low-visibility conditions in lieu of infrastructure for Low-Visibility Operations/Surface Movement Guidance and Control Systems (LVO/SMGCS). Twenty-five flight crews completed 21 short taxi scenarios under combinations of the following variables and levels: Runway visual range (RVR; 300, 500, 1000 ft); Enhanced-Flight-Vision System in head-up display (on/off); airport infrastructure - 3 levels. Two scenarios dealt with detecting obstacles near the taxi path. In both studies, the use of EFVS resulted in fewer route deviations, the majority of which occurred at 300 feet RVR with edge lights and either a standard painted centerline (Level 1) or a painted centerline with LVO/SMGCS “enhancements” but without centerline lights (Level 2). Deviation from taxiway centerline was variable, but was consistent between display conditions (EFVS/no EFVS) (mean

With special geographical and meteorological conditions, it is difficult for planes to fly at High altitude airport. Thus the rate of delay and accident remains very high. Flight accidents mostly happened when the planes was taking off or land. These are directly related with the runway visual range, which is crucial to airport operation. This paper propose a method to obtain the meteorological feature by using the deep auto-encoding network. Guided by this method, we use the auto-encoding network to study the meteorological feature and the Deep auto-coding network to explore the potential information in 2 dimensional data. We have made a real-time prediction of the runway visual range, by using the algorithm of machine learning and deep learning and the monitoring of meteorological characteristic through different dimensions. The precision rate is 91% and the TS score is up to 81.14%, 6% higher than the industry level of 75%. The method based on Ensemble Learnings multiple model fusion was studied to improve the overall performance. The final overall TS score of the fusion model have reached 82%. The study of the runway visual range prediction method in this paper help space dispatcher make a comprehensively and efficiently predict of runway visual range. It is expected to improve airport operation and reduce economic losses caused by flight accident efficiently.

This paper describes operational concepts, certification considerations, and initial user evaluations for using an avionics system for low visibility taxi operations. The ability to taxi to/from the runway safely and efficiently in low visibility conditions is becoming increasingly important as airplanes and aircrews become equipped and approved for low visibility landing and takeoff. Typically in the United States, any surface operations below 1200 ft. Runway Visual Range (RVR) generally encourage the airport authority to have an approved Low Visibility Operations / Surface Movement Guidance and Control System (LVO/SMGCS) plan which specifies certain markings, signs, lighting, and controls in the airport movement area. Operations below 600 ft. RVR, or 500 ft. RVR under U.S. Federal Aviation Administration (FAA) Order 8000.94 enablement, require additional infrastructure in movement and non-movement areas and may also require Airport Surface Detection Equipment (ASDE or ASDE-X) or a suitable substitute1 to allow the ground controllers to adequately monitor surface traffic. Surface operations below 300 ft. RVR are generally precluded (or need extra controls such as the use of a “follow-me” truck). The operational concepts described in this paper show how using on-board systems based on synthetic and enhanced vision systems may substitute for or augment an airport’s existing infrastructure. These on-board systems along with proper additional mitigations under the LVO/SMGCS concept of Protected Low Visibility Taxi Routes (PLOVTR) would enable an appropriately equipped aircraft and qualified crew to safely taxi in low visibility conditions.

Synthetic Vision (SV) and Enhanced Flight Vision Systems (EFVS) may serve as game-changing technologies to meet the challenges of the Next Generation Air Transportation System and the envisioned Equivalent Visual Operations (EVO) concept - that is, the ability to achieve the safety and operational tempos of current-day Visual Flight Rules operations irrespective of the weather and visibility conditions. One significant obstacle lies in the definition of required equipage on the aircraft and on the airport to enable the EVO concept objective. A motion-base simulator experiment was conducted to evaluate the operational feasibility and pilot workload of conducting departures and approaches on runways without centerline lighting in visibility as low as 300 feet runway visual range (RVR) by use of onboard vision system technologies on a Head-Up Display (HUD) without need or reliance on natural vision. Twelve crews evaluated two methods of combining dual sensor (millimeter wave radar and forward looking infrared) EFVS imagery on pilot-flying and pilot-monitoring HUDs. In addition, the impact of adding SV to the dual sensor EFVS imagery on crew flight performance and workload was assessed. Using EFVS concepts during 300 RVR terminal operations on runways without centerline lighting appears feasible as all EFVS concepts had equivalent (or better) departure performance and landing rollout performance, without any workload penalty, than those flown with a conventional HUD to runways having centerline lighting. Adding SV imagery to EFVS concepts provided situation awareness improvements but no discernible improvements in flight path maintenance.

Research, development, test, and evaluation of flight deck interface technologies is being conducted by NASA to proactively identify, develop, and mature tools, methods, and technologies for improving overall aircraft safety of new and legacy vehicles operating in Next Generation Air Transportation System (NextGen). Under the Vehicle Systems Safety Technologies (VSST) project in the Aviation Safety Program, one specific area of research is the use of small Head-Worn Displays (HWDs) as an equivalent display to a Head-Up Display (HUD). Title 14 of the US Code of Federal Regulations (CFR) 91.175 describes a possible operational credit which can be obtained with airplane equipage of a HUD or an "equivalent" display combined with Enhanced Vision (EV). If successful, a HWD may provide the same safety and operational benefits as current HUD-equipped aircraft but for significantly more aircraft in which HUD installation is neither practical nor possible. A simulation experiment was conducted to evaluate if the HWD, coupled with a head-tracker, can provide an equivalent display to a HUD. Comparative testing was performed in the Research Flight Deck (RFD) Cockpit Motion Facility (CMF) full mission, motion-based simulator at NASA Langley. Twelve airline crews conducted approach and landing, taxi, and departure operations during low visibility operations (1000' Runway Visual Range (RVR), 300' RVR) at Memphis International Airport (Federal Aviation Administration (FAA) identifier: KMEM). The results showed that there were no statistical differences in the crews performance in terms of touchdown and takeoff. Further, there were no statistical differences between the HUD and HWD in pilots' responses to questionnaires.

The rapid increase in aviation industry requires parallel effective plans, programs and designs of systems and facilities nationwide to fulfill the increasing needs for safe air transportation. Aircraft landing remains a problem for a long time all over the world. Systems that aircraft rely on in landing are unreliable to perform a precise guidance due to many limitations such as inaccuracy, unreliability and dependency. In low visibility conditions, aircrafts are diverted to another airport. However, low visibility can also affect all airports in the vicinity, forcing aircrafts to land in low visibility conditions depending on Instrument Flight Rules (IFR). Aircraft approach and landing are the most hazardous portions of flight; accidents records indicated that approximately 50 percent of the accidents occur during these portions. Aircraft landing Category IIIC is not yet in operation anywhere in the world. It requires landing with no visibility or runway visual range. Currently, Global Positioning System (GPS) is the main navigation system used all over the world for aircraft navigation, approach and landing. However, GPS accuracy is not sufficient to perform a perfect landing due to the possibility of aircraft to be drifted out of the runway. The accuracy of GPS could be improved to 3 meter by receiving correction messages. Improved accuracy has not been able to meet International Civil Aviation Organization (ICAO) standards for aircraft precision landing. In this study, aircraft landing systems characteristics, performances and accuracies have been studied and compared for the purpose of assessing limitations and drawbacks. An aircraft landing system with improved performance is proposed to meet ICAO standards for all-weather aircraft landing and to provide accurate guidance for approaching and landing aircrafts.

NASA Langley Research Center and the FAA collaborated in an effort to evaluate the effect of Enhanced Vision (EV) technology display in a commercial flight deck during low visibility surface operations. Surface operations were simulated at the Memphis, TN (FAA identifier: KMEM) air field during nighttime with 500 Runway Visual Range (RVR) in a high-fidelity, full-motion simulator. Ten commercial airline flight crews evaluated the efficacy of various EV display locations and parallax and mini cation effects. The research paper discusses qualitative and quantitative results of the simulation experiment, including the effect of EV display placement on visual attention, as measured by the use of non-obtrusive oculometry and pilot mental workload. The results demonstrated the potential of EV technology to enhance situation awareness which is dependent on the ease of access and location of the displays. Implications and future directions are discussed.

Conducting gate to gate operations during reduced visibility conditions is a major impediment to scheduled and unscheduled flight operations in the National Airspace System (NAS). Takeoff and landing minima are predicated on aircraft equipage and airport visibility (e.g., at some major airports, operations are terminated when visibility is below 700 ft runway visual range (RVR). Although some aircraft can land with zero-zero visibility, there are no ground or flight deck systems that allow them to taxi under low visibility conditions. A map display system designed to support low visibility taxi was evaluated by 12 B-747 flight crews in NASA's Crew Vehicle System Research Facility (CVSRF). Three taxi-map conditions were compared: paper map only, basic moving map, and advanced moving map. Crews landed and taxied along 24 different taxi routes under three visibility conditions: unlimited visibility, 700 ft RVR, and 300 ft RVR. Taxi time, errors and workload were collected for each taxi operation. Video tape recordings captured crew interactions and head-up and head-down times. Taxi times and errors were significantly better for crews with electronic maps than for crews with a paper map. Although crews with the advanced map experienced significantly more head-down time, the head down interval was significantly less than with the paper map and crew workload was significantly less. During the post-flight design review, pilots identified improvements in procedures and formatting that might enhance performance. They developed a procedure for safely switching from the NAV display to the map; and in general their comments were very favorable.

Traditionally, surface weather observations have been made by human observers. It has been their difficult task to estimate subjective elements such as sky cover (in tenths) and the heights of clouds (often to the nearest100 ft). Automation has moved slowly to replace subjective methods in the field and, even then, usually only in special areas such as runway visual range (RVR) and runway visibility (RVV). Recent work has demonstrated that automated observations of other subjective elements are feasible and are already operational at two points in the United States. The paper reviews the concepts of RVR and RVV, and discusses in detail new methods for the automation of cloud and visibility observations. The automated observations are compared with human products made at the same time and location. The paper points out the difficulties of assessing the success of objective techniques in comparison with subjective methods.

Low visibility and/or ceiling conditions have a strong impact on airports' traffic and their prediction is still a challenge for meteorologists. In this paper, the local characteristics of Low Visibility Procedure (LVP) conditions are investigated and the artificial neural network (ANN) based on resilient backpropagation as supervised learning algorithm is used to predict such meteorological conditions at Mohamed V international airport, Casablanca, Morocco. This article aims to assess the ANN ability to provide accurate prediction of such events using the meteorological parameters from the Automated Weather Observation Station (AWOS) over the period from January 2009 to March 2015. First, LVP conditions were classified according to their classes (fog LVP and no fog LVP) and their sources (Runway Visual Range -RVR LVP-, Ceiling -HCB LVP- or both) for both runway end points (35R and 17L). It is found that most of LVP conditions are associated with fog and are often due to decreasing of RVR below 600m. Next, Eleven ANNs were developed to produce LVP prediction for consecutive hourly valid forecast times covering the night and early morning. The Multi-Layer Perceptron (MLP) architecture with one hidden layer is used in this study. Results show that ANNs are able to well predict the LVP conditions and are robust to errors in input parameters for a relative error below 10%. Furthermore, it is found that the ANN's skill is less sensitive to LVP type being predicted.

The Federal Aviation Administration (FAA) is working to reduce runway incursions in the National Airspace System (NAS). This effort to enhance safety at our nation’s airports is one of the Agency’s highest priorities. As each airport is unique, the FAA maintains standards to ensure consistency for airport visual aids from airport to airport in the NAS. These critical tools serve as visual cues to pilots during airport surface movement. To support the Agency’s safety goals, the FAA Surface Systems Office is conducting research to identify new airport visual aids that may increase overall airport surface safety. This research includes an Enhanced Airport Lighting (EAL) research and development project to assess the use of runway guard lights (RGLs) as runway incursion prevention tools. Introduction RGLs have been part of the NAS since 1992. These flashing yellow lights located at the runway holding position provide visual cues that alert both pilots and vehicle operators that they are about to enter a runway. The current FAA requirement is to use RGLs with less than 1200 feet runway visual range (RVR) as a part of the Surface Movement Guidance and Control System (SMGCS). The EAL project has been undertaken to identify any potential benefits that may be obtained by using RGLs under non-SMGCS conditions, specifically to increase situational awareness. The EAL Project The EAL project promotes the use of airport visual aids to increase overall situational awareness on the airport surface, more specifically in the airport movement area. The current standard for visual cues to help pilots identify runway holding positions includes painted surface holding position markings and holding position signs. In some cases, various airport configurations, visibility conditions, and other obstructions may complicate a pilot’s ability to determine the location of the runway holding position. To address these conditions, the EAL project has two goals: (1) to identify additional visual aids to increase pilot’s ability to recognize the runway holding position and (2) to identify technologies that will increase overall situational awareness at our nation’s airports. The test EAL system, which requires no Air Traffic Control (ATC) intervention, has been installed at North Las Vegas Airport (VGT). The system consists of three different configurations of runway guard lights: • Elevated runway guard lights • In-pavement runway guard lights • In-pavement “T” configuration of runway guard lights. The lights are collocated with the standard runway holding position paint markings and signage at all 29 runway/taxiway holding positions throughout the entire airport. Controlled studies conducted at VGT by the Air Safety Technology Office of William J. Hughes Technical Center have quantitatively demonstrated the effectiveness of RGLs as a runway incursion prevention tool. In one case, on Taxiway Alpha 1, the pilot acquisition range more than doubled (209%) from 750 feet to 1170 feet during nighttime taxi operations with the addition of RGLs. In addition to the test at VGT, the EAL Team conducted an overall data assessment of other airports that have RGLs. By looking at the runway incursion history of these airports both before and after RGL installation, several trends are apparent. Although the total number of incursions was not reduced, RGL installation has helped 1 ATO Office of Technology Development, 800 Independence Ave, SW AIAA 5th Aviation, Technology, Integration, and Operations Conference (ATIO) 26 28 September 2005, Arlington, Virginia AIAA 2005-7332 This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. to reduce the severity of incursions. The data shows fewer Category A and B incursions, with a corresponding increase in Category C or D incursions. This equates to improved safety, and is a uniform finding across the entire range of airports. As a result of the findings derived from the above efforts, the EAL Team is prepared to offer a series of recommendations: 1. The FAA application of RGLs should be modified to include their use as a runway incursion prevention tool independent of weather conditions. 2. Airports that currently have RGLs should turn them on during all operations. 3. RGLs should be powered during installations such that they can be operated independently of other lighting systems. 4. The FAA should consider making RGLs available to airports specifically for runway incursion prevention when recommended by the local Runway Safety Action Team (RSAT).