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This paper characterizes wind forecast errors and evaluates their impact on times of arrival estimation within a few hundred miles of the airport. The specific wind forecast product analyzed in this paper is the Rapid Update Cycle 1-hr forecast with 40km resolution. The forecast data is compared to the data reported by the aircraft using the Aircraft Communications Addressing and Reporting System. The data are compared for the complete year of 2011 by analyzing arrival operations within 200 nmi of the Phoenix Sky Harbor International Airport. The paper describes two metrics: (i) wind magnitude metric, and (ii) wind uncertainty metrics. These metrics are defined in terms of the effect the wind has on the aircraft’s Estimated Time-of-Arrival (ETA) during its arrival phase of flight. Both metrics are computed for all the hourly forecasts between 6am to 10pm local time in the year 2011. The study illustrates the seasonal dependence of wind and wind uncertainty. It serves as the basis for selecting test days for NextGen concept evaluations in simulations.

The goal of icing forecasting research being conducted at the National Center for Atmospheric Research (NCAR) for the Federal Aviation Administration’s (FAA) Aviation Weather Research Program (AWRP) is to provide timely and accurate forecasts of inflight icing conditions. The flying public wants to know not only where icing conditions are likely to reside, but also the probability of their occurrence and expected severity. Automated diagnosis and forecast icing products have been developed at NCAR and deployed at the Aviation Weather Center (AWC), where they provide this information to pilots, forecasters, and dispatchers. These products, known as the Current and Forecast Icing Products (CIP and FIP, respectively), have been approved for operational decision making by these groups. Recently, changes were made to both algorithms to accommodate the transition in numerical weather prediction (NWP) models, from the Rapid Update Cycle (RUC) to the Weather Research and Forecasting Rapid Refresh (WRF-RAP). This transition required some changes to the algorithms to handle updated model information and a verification of the results. The verification study confirmed that the new model had the desired effects on the icing products and also brought to light some interesting information on the handling of convection and supercooled large drops (SLD). During this transition other upgrades and changes were also made to the algorithms dealing with icing severity at night, the use of radar data, and the development of an algorithm testbed.

As a part of NASA’s NextGen research effort, the focus area of Airspace Super-Density Operations (ASDO) is researching scheduling tools to increase the efficiency of terminal area operations. It is critical that the schedules generated be robust to uncertainties such as wind. The objective of the current research is to evalute the robustness of an “optimal schedule” to wind uncertainty. A queuing model of the terminal area suitable for performing stochastic analysis of terminal area operations is developed for this purpose. The queuing model is simulated using a discrete-event simulation framework. A detailed wind model consisting of nominal and uncertain components is used. Rapid Update Cycle (RUC) data was used for modeling the nominal wind component. An autoregressive model from existing literature is used to model the the uncertain component of the wind. An elegant feature of the current queuing model involves generating a “zero-conflict” solution by post-processing of the discrete-event simulation data. In this work the “zero-conflict solution” is used as a place holder for other optimal schedule planners that are currently being developed by NASA and its collaborators. Monte-Carlo simulations are conducted to evaluate the robustness of the schedule to wind uncertainties and performance metrics such as number of conflicts and delay are recorded.

The purpose of this study is to examine the wind forecast uncertainty correlation and its efiect on aircraft con∞ict detection. The spatiotemporal correlation structure of the wind prediction uncertainty of Rapid Update Cycle - 60 (RUC-1) forecasts is described and it is discussed how it can be implemented for simulation purposes. We measure the impact of this correlation by examining for several encounters the aircraft con∞ict probability estimation errors in cases where this structure is partially or completely ignored and the wind is instead modeled as white noise. The study concludes that wind correlation may have a signiflcant efiect under particular encounter geometries; the efiect is most pronounced for planar encounters.

Knowledge of present and future winds and temperature is important for air traffic operations in general, but is crucial for Decision Support Tools (DSTs) that rely heavily on accurately predicting trajectories of aircraft. One such tool is the CenterTRACON Automation System (CTAS) developed by NASA Ames Research Center. The Rapid Update Cycle (RUC) system is presently the principal source of weather information for CTAS.' RUC provides weather updates on an hourly basis on a nationwide grid with horizontal resolution of 40 km and vertical resolution of 25 mb in pressure. However, a recent study of RUC data availability showed that the NWS and NOAA servers are subject to frequent service interruptions. Over a 210 day period (4/19/0011/11/00), the availability of two NOAA and one NWS RUC server was monitored automatically. It was found that 60 days (29%) had periods of one hour or more where at least one server was out, with the longest outage lasting 13 hours on 9/21/00. In addition, there were 9 days (4%) for which all three servers were simultaneously unavailable, with the longest outage lasting 6 hours on 5/7/00. Moreover, even longer outages have been experienced with the RUC servers over the past several years. RUC forecasts are provided for up to 12 hours, but these are not currently used in CTAS as back up sources (except that the 1 or 2 hour forecasts are used for the current winds to compensate for transmission delays in obtaining the RUC data). Since RUC outages have been experienced for longer than 12 hours, it is therefore necessary to back RUC up with another weather source providing long-range forecasts. This paper examines the use of the Eta model forecasts as a back-up weather source for CTAS. A specific *This work was performed for the Federal Aviation Administration under Air Force Contract No. F19628-00-C-0002. tCopyright © 2001 by M.I.T. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. output of the Eta 32 km model, namely Grid 104, was selected for evaluation because its horizontal and vertical resolution, spatial extent and output parameters match most closely those of RUC. While RUC forecasts for a maximum of 12 hours into the future, Eta does so for up to 60 hours. In the event that a RUC outage would occur, Eta data could be substituted. If Eta data also became unavailable, the last issued forecasts could allow CTAS to continue to function properly for up to 60 hours. The approach used for evaluating the suitability of the Eta model and RUC forecasts was to compare them with the RUC analysis output or 0 hour forecast file, at the forecast time. Not surprisingly, it was found that the RUC model forecasts had lower wind magnitude errors out to 12 hours (the limit of the RUC forecasts) than the Eta model had. However, the wind magnitude error for the Eta model grew only from 9 ft/s at 12 hours (comparable with RUC) to 11 ft/s at 48 hours. We therefore conclude that RUC forecasts should be used for outages up to 12 hours and Eta model forecasts should be used for outages up to 60 hours.

The National Aeronautics and Space Administration (NASA), working with the Federal Aviation Administration (FAA), is developing a suite of decision support tools, called the Center/TRACON Automation System (CTAS). CTAS tools such as the Traffic Management Advisor (TMA) and Final Approach Spacing Tool (FAST) are designed to increase the efficiency of the air traffic flow into and through Terminal airspace. A core capability of CTAS is the Trajectory Synthesis (TS) software for accurately predicting an aircraft's trajectory. In order to compute these trajectories, TS needs an efficient access mechanism for obtaining the most up-to-date and accurate winds. The current CTAS weather access mechanism suffers from several major drawbacks.1 First, the mechanism can only handle a winds at a single resolution (presently 40-80 km). This prevents CTAS from taking advantage of high resolution wind from sources such as the Integrated Terminal Weather System (ITWS). Second, the present weather access mechanism is memory intensive and does not extend well to higher grid resolutions. This potentially limits CTAS in taking advantage of improvements in wind resolution from sources such as the Rapid Update Cycle (RUC). Third, the present method is processing intensive and limits the ability of CTAS to handle higher traffic loads. This potentially could impact the ability of new tools such as Direct-To and Multi-Center TMA (McTMA) to deal with increased traffic loads associated with adjacent Centers.