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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 - 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
• FY08 – Start elimination of redundant soundings• Example: Southwest Airlines
– 450 Boeing 737’s– 8 destinations daily (16 soundings daily)– Total of 7,200 soundings per day
• Expand sensors to record water vapor, turbulence, icing, and air quality
• Available to public in near real-time• NWS cost reduction of 4 million dollars per year
Radiosonde (purple) and WVSSII (black) Comparison April 26, 2005
12 Hour time-lapse of United StatesACARS measurements
68,000 Observations/Day
ACARS Sensor
Methods:
• Checked climatological data from Southeast Nebraska for nocturnal thunderstorm occurrences
• Used Iowa State’s meteorology data archive to acquire wind profiler data
• Found ten cases with low-level jet occurrence in great plains for 2005 and 2006 warm seasons
24 June 2005 - Haviland, KS profileras viewed through Gempak
Alti
tude
(m
)
Time (UTC)
Low-level jet instances evaluated duringwarm seasons of 2005 and 2006
Date Location Time (UTC) Time (LST)
24 June 2005 Haviland, KS 0300 – 1500 2100 – 900
28 June 2005 Haviland, KS 0300 – 1500 2100 – 900
17 July 2005 McCook, NE 0300 – 1500 2100 – 900
25 July 2005 Haviland, KS 0300 – 1500 2100 – 900
26 July 2005 Vici, OK 0000 – 1500 1900 – 900
27 May 2006 Vici, OK 0000 – 1200 1800 – 600
31 July 2006 Haviland, KS 0000 – 1800 1800 – 1200
01 August 2006 Haviland, KS 0000 – 1500 1800 – 900
02 August 2006 Haviland, KS 0000 – 1200 1800 – 600
10 August 2006 Hillsboro, KS 0000 – 1800 1800 – 1200
Methods:
• Wichita Mid-Continent Airport in Wichita, KS chosen as the ACARS reference point
• ACARS data acquired from Earth Systems Research Lab, Global Systems Division (ESRL, GSD)
Wichita, KSAirport
Hillsboro, KSProfiler
Haviland, KSProfiler
McCook, NEProfiler
Vici, OKProfiler
Data and Analysis:
• Radiosonde and profiler data collocated with ACARS by altitude
• Comparisons made with data separation, altitude of airplane, and wind speeds for each observation source
Data Point Separation
• Schwartz and Benjamin (1995) found that distance separation of 60 km or more create too much difference in wind speeds
• The overall average distance separation of this study was 187 km with a standard deviation of 48 km
• This is outside of what Schwartz and Benjamin consider acceptable
24 June 2005 – Distance separation betweenHaviland, KS profiler and ACARS observation
0
50
100
150
200
250
300
Time (UTC)
Dis
tan
ce
(k
m)
Airplane Altitude
• In overall study, the airplane altitude:– Mean was 8,770 m (~325 hPa)– Median was 10,556 m (~240 hPa)– Standard deviation was 3,530 m
• Most low-level jets exist below 2,500 m
• In a comparison of altitude vs. observed wind from the ACARS data, near surface observations showed sharp increase in wind speed
31 July 2006 – ACARS reported altitude andACARS observed wind speed
0
2000
4000
6000
8000
10000
12000
14000
Time (UTC)
Alt
itu
de
(m
)
0
5
10
15
20
25
Win
d S
pe
ed
(m
/s)
Airplane Altitude ACARS Wind Speed
Observed Winds
• 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
0
5
10
15
20
25
30
35
40
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
0
2
4
6
8
10
12
14
16
18
20
0 2 4 6 8 10 12 14 16 18
ACARS Wind Speed (m/s)
Pro
file
r W
ind
Sp
eed
(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
0
5
10
15
20
25
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
Thank you both very much!
