1344 Integrating GOES-16 Resources into Air Traffic Decision-Making

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
Roland Nunez, NWS/Center Weather Service Unit, Houston, TX; and E. Zappe and L. Wood

Handout (1.1 MB)

The 2018 convective season brought an unrelenting series of weather events across the south central United States and the northern Gulf of Mexico. For CWSU forecasters at the Houston Air Route Traffic Control Center, aviation Impact Decision Support Services were continually challenged to determine where convection would impact an airspace fix for enroute, arrival or departure air traffic.

GOES-16 satellite proved to be a very helpful resource in identifying features, such as gravity waves, that would alter expected convective development and coverage. This insight would result in changes to impact forecasts previously collaborated by NWS and aviation industry partners. The combined suite of high-resolution and high-temporal imagery proved to be an asset to CWSU forecasters who had to communicate changes to FAA decision makers responsible for making tactical decisions to established plans. In one case, the interaction of gravity waves with an ongoing convective complex prolonged and expanded thunderstorm impacts on air traffic during the evening traffic push at George Bush Intercontinental Airport in Houston, Texas (IAH), resulting in substantial delays, deviations and closures. GOES-16 imagery channels, such as “Red Visible” Band and “Lower-level Water Vapor” Band, delivered detail that enabled forecasters to alert FAA decision makers. This IDSS led to a timely shutdown of IAH, other impacted airports and certain arrival/departure routes, which allowed controllers to maximize efficiency and flight safety during a challenging event for the National Airspace System.

This presentation intends to show how timeliness and resolution of GOES-16 resources helped CWSU forecasters, in a fast-paced environment, identify subtle yet impactful weather phenomena on May 20, 2018 and would change the outcome of the collaborative forecasts.

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