Comparison of Radiosonde and Profiler Data with ACARS Data for Describing the Great Plains Low-Level Jet Ross W. Bradshaw Meteorology Program, Dept. of Geological and Atmospheric Sciences, Iowa State University, Ames, IA Mentor: Daryl Herzmann Dept. of Agronomy, Iowa State University, Ames, IA
Comparison of Radiosonde and Profiler Data with ACARS Data for Describing the Great Plains Low-Level Jet. Ross W. Bradshaw Meteorology Program, Dept. of Geological and Atmospheric Sciences, Iowa State University, Ames, IA Mentor: Daryl Herzmann - PowerPoint PPT Presentation
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Comparison of Radiosonde and Profiler Data with ACARS Data for Describing the Great Plains
Low-Level Jet
Ross W. Bradshaw
Meteorology Program, Dept. of Geological and Atmospheric Sciences, Iowa State University, Ames, IA
Mentor: Daryl Herzmann
Dept. of Agronomy, Iowa State University, Ames, IA
Motivation:
• General interest in aviation
• Possible decommissioning of radiosondes in favor of ACARS in near future
• Wanted to test data on a feature normally difficult to observe
ACARS:
• Aircraft Communications, Addressing, and Reporting System
• American Airlines, United Airlines, Delta Airlines, Northwest Airlines, FedEx, and UPS have sensors on all their aircraft, as well as some business jets and other airlines
• Sensors record temperature, onboard computers calculate wind speed and direction
• Used in most numerical models already - RUC heavily dependant on ACARS observations
David Helms – NOAA’s Office of Science and Technology
• Wind direction was consistent with all observations which agrees with the findings of Lord et al. (1984)
• The wind speed measurements are the most inconsistent with the radiosondes– Inconsistency most likely due to difference in
amount of observations
y = 0.8901x + 1.5449R2 = 0.6911
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0 5 10 15 20 25 30 35 40 45
ACARS Wind Speed (ms-1)
Pro
file
r W
ind
Sp
eed
(m
s-1)
Scatter plot for all cases combined of ACARSwind speed against profiler wind speed
y = 0.2273x + 3.4716
R2 = 0.0819
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ACARS Wind Speed (m/s)
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file
r W
ind
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(m
/s)
Case with least correlation:10 August 2006 – ACARS wind speed against
profiler wind speed
Case with most correlation:31 July 2006 – ACARS wind speed against
profiler wind speed
y = 1.036x - 0.6182
R2 = 0.7434
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5
10
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20
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0 5 10 15 20 25
ACARS Wind Speed (m/s)
Pro
file
r W
ind
Sp
eed
(m
/s)
Conclusions:
• Radiosondes only provide observations at 00 UTC and 12 UTC, missing most of the low-level jet occurrence
• Radiosonde network too sparse– Only 2 year-round radiosonde sites in Kansas
Conclusions:
• ACARS system failed to accurately locate and diagnose the low-level jet– Most ACARS data restricted to upper atmosphere, fails to
produce sufficient near-surface observations– Too much separation between sources to make accurate data
comparison
• Profiler network sufficient in locating the Great Plains low-level jet– 3 to 4 profilers in each Great Plains state– Observation times only separated by 6 min– Makes observation every 250 m– Proven accurate
Future Studies:
• More airports could be used in a larger study
• Wider range of data including more cases
• Study other mesoscale phenomena
Acknowledgements:
• Daryl Herzmann (Iowa State University)– For helping acquire and organize data
• Dr. Eugene Takle (Iowa State University)– For guidance in completing the project