PRODUCT DEVELOPMENT AT AMERICAN AIRLINES

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Presentation transcript:

PRODUCT DEVELOPMENT AT AMERICAN AIRLINES Warren Qualley Manager- Weather Services AAWS website: http://rampages.onramp.net/~aametro/

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

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

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

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

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

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)

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;

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

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)

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)

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)

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

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

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

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

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

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)

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)

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)

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)

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

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

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

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

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

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

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