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NOAA UAS Program Presents at the American Meteorological Society 97th Annual Meeting

NOAA’s Sensing Hazards with Operational Unmanned Technology (SHOUT) Project: Strategies for Improving and Augmenting Existing Satellite and Manned Aircraft Observations with Recent Pacific El Niño and Atlantic Hurricane Rapid Response Global Hawk Flights

The American Meteorological Society 97th Annual Meeting: AMS will hold its annual meeting in Seattle with a theme of “Observations Lead the Way.” Many NOAA scientists will take part in panels on a wide range of topics including the Next Generation Weather Prediction System, NOAA’s Big Data Project, transitioning social science research to weather forecast operations, advancing weather forecast models, and other topics.

The NOAA UAS Program’s Dr. Peter Black PhD presented the mission tracks and analysis from several Global Hawk flights from 2012-2016 in three high impact weather field programs have been reviewed relative to feature identification and timing: 1) HS3, 2) SHOUT Hurricane and 3) SHOUT El Niño Rapid Response. This evaluation is being driven by a shift in Global Hawk use from a research platform to an operational platform. Most past flights used one of three standard pattern types: 1) Racetrack, 2) Alpha (or ‘Figure 4’) and 3) Butterfly, the latter two being flown relative to the moving storm center. 

A review of recent studies involving use of Global Hawk flight data in TC prediction models suggest that improvements in feature structural definition and model impact can be anticipated based upon pattern re-alignment relative to: 1) supporting aircraft and satellite data coverage, 2) feature orientation, 3) feature motion, 4) environmental wind shear as well as phasing relative to: i) anticipated intensity change times, ii) feature diurnal variation and iii) model Data Assimilation time/ duration. The importance of these considerations vs issues such as observational focus on predicted uncertainty regions in various ensemble model guidance will be briefly commented upon.