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Flight Deck Weather Avoidance Decision Support: Implementation and Evaluation

Weather related disruptions account for seventy percent of the delays in the National Airspace System (NAS). A key component in the weather plan of the Next Generation of Air Transportation System (NextGen) is to assimilate observed weather information and probabilistic forecasts into the decision process of flight crews and air traffic controllers. In this research we explore supporting flight crew weather decision making through the development of a flight deck predicted weather display system that utilizes weather predictions generated by ground-based radar. This system integrates and presents this weather information, together with in-flight trajectory modification tools, within a cockpit display of traffic information (CDTI) prototype. that the CDTI features 2D and perspective 3D visualization models of weather. The weather forecast products that we implemented were the Corridor Integrated Weather System (CIWS) and the Convective Weather Avoidance Model (CWAM), both developed by MIT Lincoln Lab. We evaluated the use of CIWS and CWAM for flight deck weather avoidance in two part-task experiments. Experiment 1 compared pilots' en route weather avoidance performance in four weather information conditions that differed in the type and amount of predicted forecast (CIWS current weather only, CIWS current and historical weather, CIWS current and forecast weather, CIWS current and forecast weather and CWAM predictions). Experiment 2 compared the use of perspective 3D and 21/2D presentations of weather for flight deck weather avoidance. Results showed that pilots could take advantage of longer range predicted weather forecasts in performing en route weather avoidance but more research will be needed to determine what combinations of information are optimal and how best to present them.

CWAM

Convective Weather Forecast Accuracy Analysis at Center and Sector Levels

This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in intensity and location for center forecast are relatively low. For example, 1-hour forecast intensity and horizontal location errors for ZDC center were about 0.12 and 0.13. However, the correlation between sector 1-hour forecast and actual weather coverage was weak, for sector ZDC32, about 32% of the total variation of observation weather intensity was unexplained by forecast; the sector horizontal location error was about 0.10. The paper also introduces an approach to estimate the sector three-dimensional actual weather coverage by using multiple sector forecasts, which turned out to produce better predictions. Using Multiple Linear Regression (MLR) model for this approach, the correlations between actual observation and the multiple sector forecast model prediction improved by several percents at 95% confidence level in comparison with single sector forecast.

Wang, Yao

Convective Weather Avoidance with Uncertain Weather Forecasts

Convective weather events have a disruptive impact on air traffic both in terminal area and in en-route airspaces. In order to make sure that the national air transportation system is safe and efficient, it is essential to respond to convective weather events effectively. Traffic flow control initiatives in response to convective weather include ground delay, airborne delay, miles-in-trail restrictions as well as tactical and strategic rerouting. The rerouting initiatives can potentially increase traffic density and complexity in regions neighboring the convective weather activity. There is a need to perform rerouting in an intelligent and efficient way such that the disruptive effects of rerouting are minimized. An important area of research is to study the interaction of in-flight rerouting with traffic congestion or complexity and developing methods that quantitatively measure this interaction. Furthermore, it is necessary to find rerouting solutions that account for uncertainties in weather forecasts. These are important steps toward managing complexity during rerouting operations, and the paper is motivated by these research questions. An automated system is developed for rerouting air traffic in order to avoid convective weather regions during the 20- minute - 2-hour time horizon. Such a system is envisioned to work in concert with separation assurance (0 - 20-minute time horizon), and longer term air traffic management (2-hours and beyond) to provide a more comprehensive solution to complexity and safety management. In this study, weather is dynamic and uncertain; it is represented as regions of airspace that pilots are likely to avoid. Algorithms are implemented in an air traffic simulation environment to support the research study. The algorithms used are deterministic but periodically revise reroutes to account for weather forecast updates. In contrast to previous studies, in this study convective weather is represented as regions of airspace that pilots are likely to avoid. The automated system periodically updates forecasts and reassesses rerouting decisions in order to account for changing weather predictions. The main objectives are to reroute flights to avoid convective weather regions and determine the resulting complexity due to rerouting. The eventual goal is to control and reduce complexity while rerouting flights during the 20 minute - 2 hour planning period. A three-hour simulation is conducted using 4800 flights in the national airspace. The study compares several metrics against a baseline scenario using the same traffic and weather but with rerouting disabled. The results show that rerouting can have a negative impact on congestion in some sectors, as expected. The rerouting system provides accurate measurements of the resulting complexity in the congested sectors. Furthermore, although rerouting is performed only in the 20-minute - 2-hour range, it results in a 30% reduction in encounters with nowcast weather polygons (100% being the ideal for perfectly predictable and accurate weather). In the simulations, rerouting was performed for the 20-minute - 2-hour flight time horizon, and for the en-route segment of air traffic. The implementation uses CWAM, a set of polygons that represent probabilities of pilot deviation around weather. The algorithms were implemented in a software-based air traffic simulation system. Initial results of the system's performance and effectiveness were encouraging. Simulation results showed that when flights were rerouted in the 20-minute - 2-hour flight time horizon of air traffic, there were fewer weather encounters in the first 20 minutes than for flights that were not rerouted. Some preliminary results were also obtained that showed that rerouting will also increase complexity. More simulations will be conducted in order to report conclusive results on the effects of rerouting on complexity. Thus, the use of the 20-minute - 2-hour flight time horizon weather avoidance teniques performed in the simulation is expected to provide benefits for short-term weather avoidance.

Karahan, Sinan

Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity

This paper introduces a data driven model for predicting airport arrival capacity with a look-ahead time 2-8 hour forecast. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport's arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system. Serious imbalances between demand and capacity occur most frequently when the airport capacity is severely degraded due to either bad airport terminal surface weather or inclement convective weather around airport arrival fixes. A model that predicts the weather-impacted airport arrival meter fix throughput may help ATM personnel to plan GDP operations more efficiently. This paper identifies the characteristics of air traffic flow across arrival meter fixes at Newark Liberty International Airport (EWR). The proposed approach, based on machine-learning methods, is developed to predict the weather impacted EWR arrival Meter Fix (MF) throughput. Sector forecast coverage is used to envision the weather impact on airport arrival MF flow, and the validation is accomplished by using Convective Weather Avoidance Model (CWAM) 0.5 to 2-hour and Collaborative Convective Forecast Product (CCFP) 4 to 8-hour look-ahead forecast data for the period of April-September in 2014. Furthermore, the regression tree ensemble learning of random forests approach for translating a sector forecast coverage model to an EWR arrival meter fix throughput model is examined. The results suggest that ATM decision makers in charge of MF flow control and GDP planning may benefit from adopting the airport arrival meter capacity prediction models to estimate the inclement weather impacts.

Wang, Yao X.