Publications

Probabilistic alerting systems for airborne collision avoidance often depend upon accurate estimates of the probability of conflict. Analytical, numerical approximation, and Monte Carlo methods have been applied to conflict probability estimation. The advantage of a Monte Carlo approach is the greater flexibility afforded in modeling the stochastic behavior of aircraft encounters, but typically many samples are required to provide an adequate conflict probability estimate. One approach to improve accuracy with fewer samples is to use importance sampling, where trajectories are sampled according to a proposal distribution that is different from the one specified by the model. This paper suggests several different sample proposal distributions and demonstrates how they result in significantly improved estimates.

The long-term objectives for the NextGen Weather Processor (NWP) include consolidation of today's multiple weather systems, incorporation of recent and emerging Federal Aviation Administration (FAA) infrastructure (Federal Telecommunications Infrastructure (FTI), System Wide Information Management (SWIM), NextGen Network-Enabled Weather (NNEW)), leveraging National Oceanic and Atmospheric Administraiton (NOAA) and/or commercial weather resources, and providing a solid development and runn-time platform for advanced aviation weather capabilities. These objectives are to be achieved in a staged fashion, ideally with new components coming on-line in time to replace existing capabilities prior to their end-of-life dates. As part of NWP Segment 1, a number of alternative implementations for the NWP as it might exist in the 2013 time frame have been proposed. This report examines the alternatives form a top-down technical perspective, assessing how well each maps to a high-level NWP architecture consistent with the long-term NextGen information sharing vision. Tehcnical challenges and opportunities for weather product improvements associated with each alternative are discussed. Additional alternatives consistent with the high-level NWP architecture, as well as a number of suggested follow-on analysis efforts are also presented.

Airspace encounter models, providing a statistical representation of geometries and aircraft behavior during a close encounter, are required to estimate the safety and robustness of collision avoidance systems. Prior encounter models, developed to certify the Traffic Alert and Collision Avoidance System, have been limited in their ability to capture important characteristics of encounters as revealed by recorded surveillance data, do not capture the current mix of aircraft types or noncooperative aircraft, and do not represent more recent airspace procedures. This paper describes a methodology for encounter model construction based on a Bayesian statistical framework connected to an extensive set of national radar data. In addition, this paper provides examples of using several such high-fidelity models to evaluate the safety of collision avoidance systems for manned and unmanned aircraft.

The Traffic Alert and Collision Avoidance System (TCAS) is designed to reduce the risk of mid-air collisions by providing resolution advisories to pilots. The current version of the collision avoidance logic was hand-crafted over the course of many years and contains many parameters that have been tuned to varying extents and heuristic rules whose justification has been lost. Further development of the TCAS system is required to make the system compatible with next generation air traffic control procedures and surveillance systems that will reduce separation between aircraft. This report presents a decision-theoretic approach to optimizing the TCAS logic using probabilistic models of aircraft behavior and a cost metric that balances the cost of alerting with the cost of collision. Such an approach ahs the potential for meeting or exceeding the current safety level while lowering the false alert rate and simplifing the process of re-optimizing the logic in response to changes in the airspace and sensor capabilities.

When operationally significant weather affects the National Airspace System (NAS) a Severe Weather Avoidance Program (SWAP) is initiated. Each SWAP event is a unique mix of demand, weather conditions, traffic flow management (TFM) initiatives and traffic movement. Following a SWAP, the day's events are reviewed and the TFM initiatives used are evaluated to understand their impact on the traffic flows, benefits, and disadvantages. These analyses require an accurate representation of the conditions during SWAP and objective, data-driven metrics to determine the effectiveness of the implemented TFM initiatives, and to identify opportunities for improved decision making in future events. As part of the ongoing development and evaluation of the Route Availability Planning Tool (RAPT), a departure management decision support prototype currently deployed in New York, several detailed metrics were developed to streamline these analyses. This paper focuses on metrics that address the most significant concern regarding departure flows from New York airports: the timely reopening of departure routes that have been closed due to convective weather impacts. These metrics are derived from two datasets: flight tracks from the Enhanced Traffic Management System (ETMS) to monitor the flight traffic, and route blockage from the Route Availability Planning Tool (RAPT) to determine the impact of weather on routes. RAPT automatically identifies Post-Impact-GREENs (PIGs) - the period of time when routes are clear ('GREEN') after being blocked by convective weather. Identifying PIGs early is a key element of the RAPT concept of operations, which enables traffic managers to restart traffic flow sooner along these routes, alleviating backed up ground conditions and reducing delay times for waiting flights. An automated system, that correlates PIGs identified by RAPT with departure traffic flows, calculates both the time from the appearance of each PIG until the first departure along the PIG route, and the departure rate on the route during the PIG period. Short times to first departure and high departure rates during PIGs indicate efficient departure management during SWAP. Arrival aircraft deviating into departure airspace is also managed by closing the departure route until the danger from incurring flights has passed. Arrival incursions are sometimes recorded in the National Traffic Management Log (NTML), but the extent to which the deviations occur is unmeasured. Lack of details regarding deviations limits evaluation of implemented responses and alternative actions. New algorithms comparing clear weather vs. SWAP traffic flows enables the locations and durations of incursions to be identified. Exact figures detailing incursions allows for thorough review as well as recognition of areas of frequent incursions and the potential for developing a targeted response for like situations. Full flight tracks of arriving and departing flights provide significant insight into the status of the NAS. During SWAP when the airspace capacity is decreased and airport operation rates are limited, airborne aircraft by protocol receive priority. Arrival numbers can completely dominate operations at these times both in the air and on the ground, draining the resources available for departures in particular flows or for an entire region. To convey cases where departure infrequency results from these conditions, arrival and departure counts grouped according to direction of travel are calculated on an hourly basis. Results from the automated analysis are made available on the RAPT Evaluation and Post Event Analysis Tool (REPEAT) website by 7AM ET for the FAA Northeast tactical review teleconferences, and are being tracked over the convective season for further analysis of operational performance. This paper will present the techniques used in the automated system and initial results from the analysis of operational data.

Convective storms are responsible for causing a predominant number of delays in the summer when air traffic peaks. Several studies have shown that there is a critical need for timely, reliable, and high-quality forecasts of precipitation and echo tops with forecast time horizons of up to 12 hours in order to predict airspace capacity (Robinson et al. 2008; Evans et al. 2006; FAA 2007). While a variety of convective weather forecast systems are available to strategic planners of the National Airspace System (NAS), these products don't meet Air Traffic Management (ATM) needs fully. In addition, a multitude of forecast products increases the potential of having conflicting information available in the planning process, which can cause situational awareness problems between the operational facilities, ultimately leading to more potential delays and perhaps safety problems.

The effective management of convective weather in congested air space requires decision support tools that can translate weather information available to air traffic managers into anticipated impact on air traffic operations. The Convective Weather Avoidance Model (CWAM) has been under development at Lincoln Lab under sponsorship of NASA to develop a correlation between pilot behavior and observable weather parameters. To date, the observable weather parameters have been the Corridor Integrated Weather System (CIWS) high resolution Vertically Integrated Liquid (VIL) precipitation map and the CIWS Echo Top product. The CWAM was dependent upon a crude model to define pilot deviations based upon finding weather encounters and then comparing the distance between the planned and actual flight trajectories. Due to a large number of false deviations from this crude model, a significant amount of hand editing was required to use the database. This paper will focus on two areas of work to improve the performance of the enroute convective weather avoidance models. First, an improved automated algorithm to detect weather-related deviations that significantly reduces the percentage of false deviation detections will be presented. This new model includes additional information on each deviation, including the location the decision was made to deviate. The additional information extracted from this algorithm can be used to evaluate the conditions at the decision time which may impact the severity of weather pilots are willing to penetrate. The new deviation detection algorithm has also reduced the amount of hand editing required by removing short cuts taken to reduce the flight time, deviations that occur well past the decision time, and non-weather related reroutes. The second focus of this paper will be the comparison of three different convective weather avoidance models that have been proposed, based upon the analysis of an expanded database of flight deviations. Six weather impact days from 2007 and 2008 have been added to the existing case set from 2006, tripling the number of flight trajectories that can be used in validating the models. In addition to validating the existing CWAM, we will look at additional parameters that may improve the performance of the CWAM.

Time-based flow metering (TBFM) of traffic to capacity-constrained areas such as airport runways and arrival fixes is considered a key element of the Next Generation (NextGEN) Air Transportation System operational concept for managing high density air traffic. The principal operational TBFM system today is the Traffic Management Advisor (TMA). TMA is used to optimize the flow of aircraft through various control points (e.g., arrival fixes, final approach fixes, and runway thresholds) so as to maximize airspace capacity without compromising safety. TMA makes continuous predictions of aircraft Estimated Time of Arrivals (ETAs) at various metering points along the flight's trajectory. Scheduling algorithms use the ETAs to compute Scheduled Times of Arrival (STAs) for each aircraft to specific scheduling points. The desired change in aircraft arrival time to the meter fix is provided to en route controllers who then accomplish speed and/or trajectory changes such that the plane passes over the arrival fix at the desired time. The required arrival fix time adjustment is continually updated as the plane proceeds to the arrival fix to provide closed loop control. During non-convective weather, TMA usage has resulted in increased capacity, reduced aircraft fuel burn, and decreased delay. If significant convective weather is present, the TMA software currently still assumes that an aircraft will fly the normal fair weather trajectory to a metering fix. However, if an aircraft deviates around a storm, the flying time to a metering point will generally be different from the fair weather flight time. Therefore, the TMA ETAs will be in error. Currently, the TMA usage is often halted during convective weather events because the arrival time adjustments provided to the controllers may be unmanageable or in error. A study is underway to determine the potential benefits derived from various approaches to integrating weather information from the Corridor Integrated Weather System (CIWS) with TMA. Our focus is on near term weather-TMA integration capabilities that would provide enhanced decision support for the operational community that is successfully utilizing TMA in non-severe weather and/or seeking to increase its operational utility in severe weather. As part of this study, and in conjunction with case study analyses of TMA actions and air traffic operations during convective weather, Subject Matter Experts (SME) from the National TMA Workgroup and select FAA facilities were interviewed to determine TMA fair-weather practices and to identify current TMA capabilities and limitations during weather impact events. The SMEs were also asked to prioritize TMA weather integration needs and to discuss specific weather integration options for the TMA displays. Real-time observations of TMA operations during convective weather were also conducted at Atlanta (ZTL), Boston (ZBW), and Chicago (ZAU) Air Route Traffic Control Centers (ARTCC) to examine (a) the common TMA control actions executed to meter flows during adverse weather, (b) when and why the TBFM becomes unusable during convective weather, and (c) which approaches to providing integrated weather-TMA information would most effectively extend the current TMA capabilities and increase ATM efficiency. The paper will describe initial results of the study including specific options for TMA-CIWS integration and the anticipated operational benefits for these options.

Recently, Lovejoy et al. (2009) argued that the steep ~k-3 atmospheric kinetic energy spectrum at synoptic scales (>~1000km) observed by aircraft is a spurious artefact of aircraft following isobars instead of isoheights. Without taking into account the earth's rotation they hypothesize that the horizontal atmospheric energy spectrum should scale as k?5/3 at all scales. We point out that the approximate k?3- spectrum at synoptic scales has been observed by a number of non-aircraft means since the 1960s and that general circulation models and other current models have successfully produced this spectrum. We also argue that the vertical movements of the aircraft are far too small to cause any strong effect on the measured spectrum at synoptic scales.

Classification of primary surveillance radar tracks as either aircraft or non-aircraft is critical to a number of emerging applications, including airspace situational awareness and collision avoidance. Substantial research has focused on target classification of pre-processed radar surveillance data. Unfortunately, many non-aircraft tracks still pass through the clutter-reduction processing built into the aviation surveillance radars used by the Federal Aviation Administration. This paper demonstrates an approach to radar track classification that uses only post-processed position reports and does not require features that are typically only available during the pre-processing stage. Gaussian mixture models learned from recorded data are shown to perform well without the use of features that have been traditionally used for target classification, such as radar crosssection measurements.