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Stanley G. Benjamin
,
Barry E. Schwartz
, and
Rodney E. Cole

Abstract

A collocation study of meteorological reports from commercial aircraft relayed through the Aircraft Communications, Addressing, and Reporting System (ACARS) has been performed to estimate standard deviations of observation errors for wind and temperature. ACARS observations were collected over an area in the western and central United States for a 13-month period, and this dataset was examined for pairs of reports within small spatial (⩽10 km) and temporal (⩽10 min) windows. The results showed an observation error of a single horizontal component of wind of 1.1 m s−1 and 0.5 K for temperature above the boundary layer. Within the boundary layer, the rms difference of wind and temperature between aircraft was larger, presumably due to larger small-scale variations in the atmosphere and, in the case of wind, from aircraft maneuvers. These observation error estimates are valuable for use in data assimilation and for determination of forecast error from ACARS observation-minus-forecast differences. By comparing standard deviations at different levels, estimates of mesoscale variability at a 10-km scale in the lower troposphere were also calculated. These values (rms vector error of 1.8 m s−1 for wind, rms error of 0.5 K for temperature) can be interpreted as estimates of the 10-km lower-tropospheric error of representativeness, also useful for data assimilation.

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Rodney E. Cole
,
Steve M. Green
, and
Matt R. Jardin

Abstract

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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