Wind prediction accuracy for air traffic management decision support tools
Air traffic automation depends on accurate trajectory predictions. Flight tests show that wind errors are a large source of error. Wind-field accuracy is sufficient on average, but large errors occasionally exist that cause significant errors in trajectory-prediction. A year long study was conducted to better understand the wind-prediction errors, to establish metrics for quantifying large errors, and to validate two approaches to improve wind prediction accuracy. Three methods are discussed for quantifying large errors: percentage of point errors that exceed 10 m/s, probability distribution of point errors, and the number of hourly time periods with a high number of large errors. The baseline wind-prediction system evaluated for this study is the Rapid Update Cycle (RUC). Two approaches to improving the original RUC wind predictions are examined. The first approach is to enhance RUC in terms of increased model resolution, enhancement of the model physics, and increased observational input data. The second method is to augment the RUC output, in near-real time, through an optimal-interpolation scheme that incorporates the latest aircraft reports received since the last RUC update. Both approaches are shown to greatly reduce the occurrence of large wind errors.