Publications

For several years the NASA sponsored Convective Weather Avoidance Model (CWAM) has been under development at Lincoln Lab to correlate pilot behavior with observable weather parameters available from convective weather systems. To date, the current CWAM has focused primarily on the enroute airspace used by aircraft at cruise altitude. At these altitudes there is a strong correlation between the observable echo tops from the Corridor Integrated Weather System (CIWS) and the probability that a pilot will deviate around weather. The CWAM has lead to the development of a Weather Avoidance Field (WAF) that combines the echo tops and vertically integrated liquid (VIL) into a probabilistic forecast of the likelihood of pilot deviation. In recent years the WAF has become widely acceptance as a reliable indicator of the impact of convective weather on air traffic operations. This paper will explore the adaptation of the CWAM into the terminal airspace with a focus on the weather impact on arrival decision making. A database of convective weather impacts on several major terminals from 2009 has been collected and identification of the impact on arriving aircraft has begun. Past studies of terminal weather impact have identified aircraft that penetrated severe weather or made clear deviations around convective cells within the terminal. This study will expand the definition of an impact to identify pilot decision making occurring outside of the terminal with regard to the expected weather impact upon arrival in the terminal. Examples include rerouting to an alternate corner post, holding in enroute airspace, or diverting to an alternate airport when weather is expected along the planned terminal trajectory. These types of terminal weather avoidance decisions can often be made many miles outside of the terminal. The enroute CWAM uses spatial filters applied to the echo tops and VIL to obtain the best correlation between the weather and pilot behavior. This paper will evaluate the current CWAM filters and identify alternate spatial filters or additional weather products that may best correlate pilot decision making in the terminal. Ultimately the goal of this work is provide ATC managers and automated decision supports tools with a weather avoidance field for effective management of convective weather in terminal airspace.

A strong low pressure system moved through the Northeast United States on 25 January 2010. As the day progressed, a north-south line of convection formed ahead of an approaching cold front and intensified very rapidly as it passed over the four major New York City airports. Exceptionally strong winds and low-level shears occurred throughout the terminal areas and forced arriving aircraft to divert to alternate airports, hold in the air and on the ground, or abort the landing approach. Analysis shows that a narrow but intense squall line developed as a result of a gravity wave or buoyancy wave and caused vertical shear of the horizontal winds from the surface up through cruise flight levels throughout all of the Terminal Radar Approach Control airspace. Air traffic control planning procedures are examined because the extent and severity of the weather was underestimated; consequently, air traffic managers over-delivered aircraft which lead to excessive airborne holding in regions of known turbulence. Although not available to the operational aviation community at the time, evidence is also shown that the NOAA Earth System Research Laboratory experimental High-Resolution Rapid Refresh (HRRR) model forecasted the event. HRRR supplemental output fields could have provided the spatial and temporal resolution necessary for Managers to plan and execute an orderly reduction in air traffic demand, which, in-turn, would have improved safety and significantly reduced passenger delays. A framework for incorporating HRRR data into Air Traffic Management (ATM) Decision Support Tools and specific ATM Collaborative Decision Making guidance is offered.

MIT Lincoln Laboratory has developed the Route Availability Planning Tool (RAPT), which provides automated convective weather guidance to air traffic managers of the NYC metro region. Prior studies of RAPT have shown high-accuracy guidance from forecast weather, but further refinements to prevent forecast misclassification is still desirable. An attribute set of highly correlated predictors for forecast misclassification is identified. Using this attribute set, a variety of prediction models for forecast misclassification are generated and evaluated. Rule-based models, decision trees, multi-layer perceptrons, and Bayesian prediction model techniques are used. Filtering, resampling, and attribute selection methods are applied to refine model generation. Our results show promising accuracy rates for multi-layer perceptrons trained on full attribute sets.

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.

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.

Dozens of Ground Delay Programs (GDPs) are implemented each summer for San Francisco International Airport (SFO) in order to cope with reduced capacity caused by the presence of warm-season stratus in the approach zone. The stratus prevents the use of dual approaches to SFO's closely-spaced parallel runways, which essentially reduces the arrival capacity by half. In 2004, a prototype system for providing probabilistic stratus forecast guidance was transitioned from the research community to NWS Monterey. This system was intended to be used as a tool for improving the daily forecast of stratus clearing time from the approach zone, and correspondingly improve the efficiency of GDP implementation strategy. Since its transition to the NWS in 2004, the automated forecast guidance system has continued to produce reliable forecasts of daily stratus clearing time. However, this success has not adequately translated to a marked improvement in GDP efficiency. Analysis by the NWS indicates that the existing mechanisms for introducing the forecast guidance information into the GDP decision process, as well as the GDP implementation strategy itself, are not suited for taking full advantage of the forecast skill demonstrated by the system. A historical examination of SFO GDP implementation based on the probabilistic forecasts provided by the automated forecast guidance system is currently in process, with the objective being a recommendation for a more effective GDP strategy. An important consideration is understanding the risk/reward associated with the decision process. In this instance, the reward is increased efficiency seen as reduced aircraft delays, at the risk of creating increased delay, aircraft diversions, and controller workload in the event that an incorrect optimistic forecast results in the premature release of ground-held aircraft. This investigation is being performed in concert with the weather-integration objectives of the current FAA modernization program, particularly the integration of weather information that is delivered in a probabilistic format. Shortcomings within the current GDP strategy are described to provide context for potential improvements that exploit the probabilistic forecasts currently emerging from the research community.

Air traffic congestion caused by convective weather in the US has become a serious national problem. 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 and FAA REDAC Report 2007). Yet, there are currently several forecast systems available to strategic planners across the National Airspace System (NAS) that are not fully meeting Air Traffic Management (ATM) needs. Furthermore, the use of many forecasting systems increases the potential for conflicting information in the planning process, which can cause situational awareness problems between operational facilities. One of the goals of the Next Generation Air Transportation System (NextGen) is to consolidate these redundant and sometimes conflicting forecast systems into a Single Authoritative Source (SAS) for aviation uses. The FAA initiated an effort to begin consolidating these systems in 2006, which led to the establishment of a collaboration between MIT Lincoln Laboratory (MIT LL), the National Center for Atmospheric Research (NCAR) Research Applications Laboratory (RAL), the NOAA Earth Systems Research Laboratory (ESRL) Global Systems Division (GSD) and NASA, called the Consolidated Storm Prediction for Aviation (CoSPA; Wolfson et al. 2008). The on-going collaboration is structured to leverage the expertise and technologies of each laboratory to build a CoSPA forecast capability that not only exceeds all current operational forecast capabilities and skill, but that provides enough resolution and skill to meet the demands of the envisioned NextGen decision support technology. The current CoSPA prototype for 0-6 hour forecasts is planned for operation as part of the NextGen Initial Operational Capability (IOC) in 2013. CoSPA is funded under the FAA's Aviation Weather Research Program (AWRP). The first CoSPA research prototype demonstration was conducted during the summer of 2008. Technologies from the Corridor Integrated Weather System (CIWS; Evans and Ducot 2006), National Convective Weather Forecast (NCWF; Megenhardt et al. 2004), and NOAA’s Rapid Update Cycle (RUC; Benjamin et al. 2004) and High Resolution Rapid Refresh (HRRR; Benjamin et al. 2009) models were consolidated along with new technologies into a single high-resolution forecast and display system. Historically, forecasts based on heuristics and extrapolation have performed well in the 0-2 hour window, whereas forecasts based on Numerical Weather Prediction (NWP) models have shown better performance than heuristics past 3-4 hours (Figure 1). One of the goals of CoSPA is to optimally blend heuristics and NWP models into a unified set of aviation-specific storm forecast products with the best overall performance possible. The CoSPA prototype demonstration began in July 2008 with 2-6 hr forecasts of Vertically-Integrated Liquid water (VIL) that seamlessly matched with the 0-2 hr VIL forecasts available in CIWS. These real-time forecasts have been made available to the research team and FAA management only through a web-based interface. This paper discusses the system infrastructure, the forecast display, the forecast technology and performance of the 2-6 hr VIL forecast. Our early assessment based on the 2008 demonstration is that CoSPA is showing tremendous promise for greatly improving strategic storm forecasts for the NAS. Early user feedback during CoSPA briefings suggested that the 6 hr forecast time horizon be extended to 8 hours to better meet their planning functions, and that forecasts of Echo Tops must also be included.

In this paper, we develop a novel way to measure the accuracy of weather forecasts based upon the impact on air traffic flows. This method uses new techniques developed as part of the CWAM that consider the complicated interaction between pilots, air traffic controllers and weather. This technique, known as the blockage model (Martin et al., 2006), differentiates between minor deviations performed by pilots around convective weather and their larger deviations due to fully blocked air routes that require air traffic control interaction. This blockage model is being used by the automated Route Availability Planning Tool (RAPT) to predict route blockage for NYC departures. RAPT integrates the Corridor Integrated Weather Systems (CIWS) deterministic 0-2 hour forecasts of precipitation and echo tops into route specific forecasts of impact on air traffic in the congested east coast corridor. Applying the blockage model to the entire CIWS weather domain as a metric for scoring the performance of the forecast algorithms is shown to be an excellent approach for measuring the adequacy of the forecast in predicting the impact of the convective weather on air traffic operations.

It is generally agreed that effective management of convective weather in congested airspace requires decision support tools that translate the weather products and forecasts into forecasts of ATC impacts and then use those ATC impact forecasts to suggest air traffic management strategies. In future trajectory-based operations, it will be necessary to automatically generate flight trajectories through or around convective weather that pilots will find acceptable. A critical first step, needed in both today's air traffic management environment and in the highly automated systems of the future, is a validated model for airspace that pilots will seek to avoid. At the 12th Conference on Aviation, Range and Aerospace Meteorology (Atlanta, 2006), we reported on an initial Convective Weather Avoidance Model (CWAM1) (DeLaura and Evans; 2006). The CWAM1 outputs are three dimensional deterministic and probabilistic weather avoidance fields (WAFs). CWAM1 used Corridor Integrated Weather System (CIWS) VIL and echo top fields and National Lightning Detection Network (NLDN) data to predict aircraft deviations and penetration. CWAM1 was developed using more than 500 aircraft-convective weather encounters in the Indianapolis Air Route Traffic Control Center (ZID ARTCC) airspace. CWAM1 gave the greatest weight to the difference between flight altitude and the 18 dbZ radar echo top with precipitation intensity playing a secondary role. The deviation prediction error rate in CWAM1 was approximately 25%. This paper presents a new model (CWAM2), based on the analysis of trajectories from several ARTCCs [Indianapolis (ZID), Cleveland (ZOB) and meteorological deviation predictors. Additional weather factors that are considered include vertical storm structure (upper level reflectivity and the height of the VIL centroid derived from the NSSL 3D reflectivity mosaic), vertical and horizontal storm growth, the spatial variation in VIL and echo top fields and storm motion.

Thunderstorm-related delays dominate the overall U.S. airspace delay statistics and continue to increase, even though a number of new weather information systems and air traffic management (ATM) decision support tools have been deployed since 1999. Operational decision makers must mitigate the network congestion that arises from rapidly varying capacity loss in both en route and terminal airspace. Improving decision making in such an environment requires explicitly considering airspace structure, network impacts, forecast uncertainty and pilot preferences for weather avoidance. To date, the NextGen initiative has focused on envisioning an operational concept and research agenda for 2025 where it is assumed that aircraft separation and weather avoidance is accomplished using a high degree of automation. In this paper, we consider research to achieve significant improvements in the near term (2010-2015) where aircraft separation is provided largely by controllers and hazardous weather avoidance is accomplished by pilots using visual cues, reports from other aircraft, on board weather radar and ATC advisories. We briefly review the current status of work in key areas and then suggest major near term initiatives. Key elements of the research program to be discussed include: 1. Translation of convective weather products into ATC impacts (including handling of uncertainty in the convective weather forecasts). Initial models for en route pilot avoidance of storms and sector capacity in convective weather have shown promising results, but clearly much more research is needed in this area. 2. Determining when and where available capacity was not appropriately utilized during convective events, based on both facility observations during storm events and computations of avoidable delay. Preliminary results suggest that much of today's delay is in fact avoidable. 3. Developing integrated weather-ATM decision support tools (DST) to enable decision makers to more fully utilize available capacity. The accuracy of contemporary convective weather forecasts is a key issue in the design of such systems. Initial operational experience with a departure decision support tool will be discussed to illustrate key points. 4. Explicitly considering the human side of convective weather ATM [e.g., how individuals make real time decisions in collaboration with other decision makers (e.g., ATC, airlines, dispatch, pilots)]. Recent results from field usage of convective weather decision support tools will be interpreted in the context of recent literature on how people actually make decisions and perform cognitively complex functions in demanding situations.