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Improving RUC-1 wind estimates by incorporating near-real-time aircraft reports

Published in:
Weather For., Vol. 15, No. 4, August 2000, pp. 447-460.

Summary

A verification study of wind accuracy is presented for wind nowcasts generated by augmenting Rapid Update Cycle (RUC) wind forecasts with near-real-time aircraft reports using the Integrated Terminal Weather System (ITWS) gridded winds algorithm. Aircraft wind reports collected between the end of the RUC data collection interval and the time each RUC forecasts is valid are available for use in augmenting the RUC wind forecast to form a wind nowcast. The 60-km resolution, hourly RUC-1 wind forecasts are used. ITWS-based nowcast wind errors and RUC forecast wind errors are examined statistically over a 1-yr dataset. The addition of the recent aircraft reports significantly reduces the rms vector error and the 90th percentile vector error. Also reduced is the number of hours of sustained large errors and the correlation among errors. The errors increase with increasing wind speed, in part due to an underestimation of wind speed that increases with increasing wind speed. The errors in the augmented wind fields decrease with increasing numbers of Aircraft Communications Addressing and Reporting System reports. Different types of weather are also seen to influence wind field accuracy.
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Summary

A verification study of wind accuracy is presented for wind nowcasts generated by augmenting Rapid Update Cycle (RUC) wind forecasts with near-real-time aircraft reports using the Integrated Terminal Weather System (ITWS) gridded winds algorithm. Aircraft wind reports collected between the end of the RUC data collection interval and the time...

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Wind prediction accuracy for air traffic management decision support tools

Published in:
Proc. 3rd Int. Air Traffic Management R&R Seminar, 13-16 June 2000, pp. 1-9.

Summary

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.
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Summary

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...

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