1. PRODUCT DEVELOPMENT AT AMERICAN AIRLINES
Warren Qualley
Manager- Weather Services
AAWS website:

2. PRODUCT DEVELOPMENT AT AMERICAN AIRLINES
Procedural
Products for flight crews, dispatchers, airport personnel, planning managers
Programmatical
Products for flight plans, input into programs on dispatcher desks
WeatherBrowser
Project Hub-CAPS

3. Why OU and Why Hub-CAPS ??? 4/29/95 hailstorm at DFW
AA operating philosophy
Lack of any storm-scale NWP
Long lead time for gov’t R&D

4. Project Hub-CAPS
$1 M, 3-year R&D partnership between OU and American Airlines (1 July 1996 to 30 June 1999)
Goals:
to demonstrate the practicability of storm-scale numerical weather prediction for commercial aviation
to customize and integrate the CAPS forecast system into AA operations
to end with a usable system that can be run daily for or by American Airlines

5. N ational-scale products
AA Weather
Services
N ational-scale
products
Storm-scale
Hub-CAPS
computer
model
U.S. National
Weather Service
U.S. National
Weather Service
AA Weather
Services
AA Weather
Services
N ational-scale products
N ational-
scale products
Value - adde d
products
Private sector
weather
companies
(re-packaging)
Private sector
weather
companies
(re-packaging)
AA flight
operations
AA flight
operations

6. Project History and Overview of the Technology
Numerical Weather Prediction:
The use of computer models to
predict the future state of the
atmosphere

7. The $1M Question: Can We Predict These Events Explicitly and Reliably Using Computer Models?
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

8. Present NWS Operations
One-size-fits-all forecasts over entire continent;
Forecast model schedule fixed;
Forecast location fixed;
Models cannot resolve individual thunderstorms;
Models provide only standard products;

9. High-Impact Weather is LOCAL
Rain and
Snow
Fog
Rain and
Snow
Snow and
Freezing Rain
Intense
Turbulence
Severe
Thunderstorms

10. The Strategy - The Advanced Regional Prediction System (ARPS)
10 km
3 km
1 km
20 km CONUS Ensembles
Focus on large and small scales simultaneously
Run model when and where needed
Run model in desired configuration
Customized to meet needs
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

11. The Devastating 29 April 1995 Hailstorm
ARPS Model
Radar Composite
10 PM, April 29, 1995
10 PM, Apr
il 29,
1995
Oklahoma
Texas
DFW K
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

12. Philosophy of Project
Involve AA personnel in the development of the system
in order for it to meet their needs
in order for the operational forecasters to take pride in ownership
Respond quickly to problems and new ideas (rapid prototyping)
Leverage AA money against State and Federal sources
More than R&D
training and education
internships and guest lectures
special projects (TAF verifier, accident/incident investigations)

13. Evaluation of Forecasts
Model showed significant skill in both cold-season and warm-season situations
Nevertheless, the forecasts were too inconsistent to be trusted on a regular basis
Surface physics problems and lack of satellite data in initial conditions created overly warm surface temperatures
Model was slow to spin-up convection (no radar data)
Small domain size led to strong influence by lateral boundaries
Model tended to be conservative (good feature)
Forecasts not timely enough

14. Year-2 Hourly Analysis Domains
D/FW Region
ORD Region
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)
NE Corridor
Central/Eastern US

15. Sample ARPSView Products
Downburst Potential
Surface Isotachs &
Streamlines
CAPE & Helicity
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

16. User Comments
“We at the Fort Worth ARTCC Center Weather Service Unit have been using your Hub-CAPS site for some time now, and we find it to be one of the best forecast tools for the D/FW area”
Tom Hicks, Meteorologist-in-Charge, Center Weather Services Unit, Fort Worth Air Route Traffic Control Center
“We live and die by your web page”
Tom Skilling, Chief Meteorologist, WGN TV-9, Chicago

17. Web Activity
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

18. A Controversial Forecast
NWS zone and TAF forecasts, and AAWS forecasts, missed squall line
Hub-CAPS caught the event, but reliability was still an issue so the forecast was not believed (this is sometimes true for NWS models)
Hub-CAPS forecast used data that didn’t get into the NWS models
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

19. 12 h ARPS Forecast 7 h ARPS Forecast 1 h ARPS Forecast
KFWS 01Z on 5 June 1998
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

20. Storm-Scale Prediction is Hard!
KFWS 01Z on 5 June 1998
5 h, 9 km ARPS Forecast
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

21. How Good Are the Forecasts?
Actual Event
D/FW Airport
30 miles
A perfectly predicted storm having a position error
of 30 miles may be a terrible forecast on the scale
of a single airport
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

22. How Good Are the Forecasts?
This same forecast would, on a larger scale,
be viewed as exceptionally accurate

23. Sample Desired Information
With 80% frequency, Hub-CAPS correctly predicts thunderstorms to within 20 miles of their actual position 4 hours ahead of time
If thunderstorms are within 50 miles of the airport, Hub-CAPS correctly predicts their location
50% of the time 6 hours in advance
70% of the time 4 hours in advance
90% of the time 2 hours in advance

24. The Hub-CAPS System Today
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

25. The Hub-CAPS System Today
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

26. Sample ARPSView Products
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)
Hourly Station Model Plots for 10 Regions in US

27. Real Time Prediction/Analysis Domains (http://caps.ou.edu/wx)
Courtesy of the Center for Analysis and Prediction of Storms (CAPS)

28. Conclusions Hub-CAPS has saved $$$ for AA Program fast-tracked to ops
R&D concurrent- ops feedback
Link to on-going Met research