245 Improved Monitoring of the Evolving Upper-Tropospheric Wind Fields Over the Core of Tropical Cyclones Aided by High Spatiotemporal Resolution GOES-16 Atmospheric Motion Vectors

Thursday, 19 April 2018
Champions DEFGH (Sawgrass Marriott)
Christopher S. Velden, CIMSS/Univ. of Wisconsin, Madison, WI; and D. Stettner

GOES-16, launched in November of 2016, represents the new generation of this nation’s geostationary weather satellites. During this past Atlantic hurricane season, the satellite was operating in ‘check-out’ mode, and providing the first opportunity to explore its new capabilities. Among those capabilities is the continuous sampling of a targeted region with super-rapid imaging refresh (30-60 sec), which dramatically improves the visual presentation of the evolving storm structures, but also allows for high spatiotemporal quantities such as derived atmospheric motion vectors (AMVs). This new source for continuous wind information can augment the sporadic aircraft in-situ measurements to provide greatly improved analyses of the hurricane-scale wind fields. This in turn should lead to better diagnostics of the dynamic and kinematic processes, and also improved initial analyses for hurricane forecast models. The advances will be enabled by applying novel processing algorithms designed to exploit the unprecedented information content of the new generation of geostationary satellites.

Given the recent occurrence of major (category 4-5) Hurricanes Harvey, Irma and Maria that impacted the United States and the Caribbean territories, the focus of this poster will be on demonstrating how the novel continuous super-rapid-scan observations from GOES-16 can be utilized to better depict the rapidly-evolving wind fields in these storms.

Specifically, the poster presentation will:

1) Demonstrate the value of continuous 30-60 sec scanning from the new GOES-16 satellite toward producing high-spatiotemporal resolution AMV fields over the core region of Hurricanes Irma, Harvey and Maria.

2) Show how novel optical flow methodologies can be applied to optimize the tracking of high-resolution cloud features to derive the AMVs.

3) Illustrate derived flow fields and diagnostics (e.g. divergence and vorticity) to deduce dynamic and kinematic processes relevant to changing wind structures and storm intensity. Can the AMVs identify mesoscyclones within the eye? Can cloud features in the inner eyewall be tracked and related to maximum winds? Can the recently-discovered diurnal pulse in hurricanes be quantitatively identified by the evolving AMV fields?

4) Report that high spatiotemporal resolution GOES-16 AMV datasets for the complete lifetime of the three hurricane cases will be available to researchers to (for example) assess the potential impact of adding the AMV data to model forecasts of track, intensity, structure and rainfall.

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