7.1 Utilization of Atmospheric Motion Vectors for Deep Convection Cloud-Top Flow Analysis Using GOES-16 Imagery

Thursday, 11 January 2018: 3:30 PM
Ballroom G (ACC) (Austin, Texas)
Jason Apke, Univ. of Alabama, Huntsville, AL; and J. R. Mecikalski and C. P. Jewett

The image spatial resolution (500 m) and scanning rate (30 sec) of the geostationary operational environmental satellite (GOES)-16 Advanced Baseline Imager (ABI) allows for the retrieval of objective image-based gridded wind flow-features at storm-scales (< 10 km) over transient cloud scenes such as the bubbling tops of deep convection. Experiments with 1-min super rapid scan operations for GOES-R data from GOES-14 provided evidence to suggest that objective analysis of photogrammetric derived atmospheric motion vector (AMV) algorithms over mature deep convection yields a distinct large cloud-top divergence (CTD) maximum and in some cases a cloud-top vorticity (CTV) minimum and maximum downstream over the anvils of severe storms, especially those observed in the Great Plains. Derived CTD with GOES-14 has been shown to highlight regions of strong ongoing updrafts even with spatially and temporally inferior datasets to those available with the ABI. The same techniques have yet to be tested on the new GOES-16 system for cloud-top flow derivation and updraft quantification. Updated AMV techniques involving clustered target tracking and optical flow are applied to deriving cloud-top flow fields using multiple supercells and ordinary cells collected within the GOES-16 beta testing period, and results are shown on the impact of changing from the heritage target tracking retrieval of cloud-top flow relevant to deep convection. New CTD signals are compared to datasets collected with total lightning from ground based mapping arrays and the geostationary lightning mapper within range of multi-Doppler radar networks in an effort to quantify updraft strength from satellite based flow derivation data sources. Updated results will be shown on the correspondence between CTD and high-CTD area compared to total lightning and multi-Doppler derived updraft volume. In addition, experimentation with cloud-terrain modified flow is presented, utilizing other GOES satellites to create stereoscopic cloud-top height profiles and correct for the presence of environmental vertical wind shear. The results from GOES-16 flow derivation system are compared to idealized numerical solutions of cloud-top flow, including experiments on the impact of the presence of above-anvil cirrus plumes. The relationship between measured cloud-top vorticity and helicity in the numerical solutions is also explored. The improved spatial and temporal resolution of GOES-16 is found to significantly increase the number of derivable flow vectors over mature deep convection, which is found to improve the spatial resolution of derivable flow features over deep convection. Initial experiments have shown a weak positive correlation between updraft volume and CTD maxima, although this correlation can be affected by the presence of above-anvil cirrus plumes. The forecast benefits of the GOES-16 flow-field derivation system will also be presented, including observations of hail size estimation and timing of tornado formation compared to the change in CTD and CTV fields.
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