Aircraft conflict probe sensitivity to weather forecasts errors

Abstract

This study investigated the User Request Evaluation Tool's (URET) prediction sensitivity to weather forecast error. A quantitative experiment was designed and performed by the Federal Aviation Administration's Conflict Probe Assessment Team (CPAT) to evaluate the impact of weather forecast errors on URET trajectory and conflict predictions. The experiment used approximately two hours of traffic data recorded at the Indianapolis en route center in May 1999. The flights were time shifted to generate a sufficient number of test conflicts using a genetic algorithm technique developed by CPAT. The resulting scenario was input into the URET Prototype System. To induce weather forecast error, the weather input file (Rapid Update Cycle, RUC) was altered by adding 20 or 60 knots to the wind magnitude, 45 or 90 degrees to the wind direction, and 5 or 15 degrees Kelvin to the air temperature. This produced seven URET runs for the experiment -the unaltered control run and six treatment runs. The analysis compared the control run against the treatment runs. A methodology was developed to compare the trajectory and conflict prediction accuracy of these runs. A statistical analysis provided evidence that the forecast errors in wind magnitude and direction had significant effect on the longitudinal trajectory error and a modest impact on retracted false alerts, which caused at most an increase in the false alert probability by six percent. It also showed that the air temperature runs did not have a significant effect. Based on this experiment, a controller suspecting errors in the input wind forecast should expect only a modest impact on URET predictions. The impact would mainly be a moderate increase in the number of retractions of its conflict predictions (defined in this study as a retracted false alert).

Safety Evaluation of Advanced Self-Separation Under Very High En Route Traffic Demand

“…Although the accuracy of wind forecasts has improved in recent years, it is known that occasional large errors can occur, which are known to significantly affect the performance of trajectory prediction tools [37]. Figure 12 shows the impacts upon the risk curve of systematic wind errors of 10, 20, and 30 m∕s (60 kt).…”

Preliminary investigation of trajectory prediction impact on decision support automation

“…Some efforts have been relatively comprehensive, examining a wide range of metrics or DSTs. [30][31][32][33] Most efforts have focused on specific aspects of the problem, such as in the presence of meteorological forecasting uncertainty, [34][35][36][37][38] in particular phases of flight, [39][40][41][42] due to departure uncertainty, [43] and improvements due to data link. [44][45][46] These research efforts have laid the groundwork toward a better understanding of how trajectory prediction impacts DST performance.…”