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Telling North from South: An Example of an Error in Automated Aircraft Data

Telling North from South: An Example of an Error in Automated Aircraft Data Meteorological observations have become increasingly automated, which in general has improved the quality of the data. Manual observations are prone to a variety of human errors, ranging from simple typographical and transcription errors to violations of code standards ( Sparkman et al. 1981 ; Brewster et al. 1989 ); automating observations minimizes such errors. However, automated observations are not error free ( Schwartz and Benjamin 1995 ; Benjamin et al. 1999 ; Brewster et al. 1989 ). In fact, some errors are quite systematic (e.g., Pauley 2002 ; Moninger and Miller 1994 ; Bisiaux 1983 ) and can occur rarely enough that they are difficult to detect. An example of such an error was recently noted by B. Ballish (2001, personal communication) at the National Centers for Environmental Prediction (NCEP). He found that a significant number of automated aircraft winds with large vector differences with respect to the model first-guess field had reported wind directions of 360°. The following analysis of this error reveals that the actual wind was southerly in these cases. This error is a particularly pernicious example of the kind of error that can occur in automated data. Figure 1 shows a flight track from http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Weather and Forecasting American Meteorological Society

Telling North from South: An Example of an Error in Automated Aircraft Data

Pauley, Patricia M.
Weather and Forecasting , Volume 17 (2) – Aug 29, 2001
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Publisher
American Meteorological Society
Copyright
Copyright © 2001 American Meteorological Society
ISSN
1520-0434
DOI
10.1175/1520-0434(2002)017<0334:TNFSAE>2.0.CO;2
Publisher site
See Article on Publisher Site

Abstract

Meteorological observations have become increasingly automated, which in general has improved the quality of the data. Manual observations are prone to a variety of human errors, ranging from simple typographical and transcription errors to violations of code standards ( Sparkman et al. 1981 ; Brewster et al. 1989 ); automating observations minimizes such errors. However, automated observations are not error free ( Schwartz and Benjamin 1995 ; Benjamin et al. 1999 ; Brewster et al. 1989 ). In fact, some errors are quite systematic (e.g., Pauley 2002 ; Moninger and Miller 1994 ; Bisiaux 1983 ) and can occur rarely enough that they are difficult to detect. An example of such an error was recently noted by B. Ballish (2001, personal communication) at the National Centers for Environmental Prediction (NCEP). He found that a significant number of automated aircraft winds with large vector differences with respect to the model first-guess field had reported wind directions of 360°. The following analysis of this error reveals that the actual wind was southerly in these cases. This error is a particularly pernicious example of the kind of error that can occur in automated data. Figure 1 shows a flight track from

Journal

Weather and Forecasting – American Meteorological Society

Published: Aug 29, 2001

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