Relationships between Deep Convection Updraft Characteristics and Satellite-Based Super Rapid Scan Mesoscale Atmospheric Motion Vector–Derived Flow

Abstract

Rapid acceleration of cloud-top outflow near vigorous storm updrafts can be readily observed in Geostationary Operational Environmental Satellite-14 (GOES-14) super rapid scan (SRS; 60 s) mode data. Conventional wisdom implies that this outflow is related to the intensity of updrafts and the formation of severe weather. However, from an SRS satellite perspective, the pairing of observed expansion and updraft intensity has not been objectively derived and documented. The goal of this study is to relate GOES-14 SRS-derived cloud-top horizontal divergence (CTD) over deep convection to internal updraft characteristics, and document evolution for severe and nonsevere thunderstorms. A new SRS flow derivation system is presented here to estimate storm-scale (<20 km) CTD. This CTD field is coupled with other proxies for storm updraft location and intensity such as overshooting tops (OTs), total lightning flash rates, and three-dimensional flow fields derived from dual-Doppler radar data. Objectively identified OTs with (without) matching CTD maxima were more (less) likely to be associated with radar-observed deep convection and severe weather reports at the ground, suggesting that some OTs were incorrectly identified. The correlation between CTD magnitude, maximum updraft speed, and total lightning was strongly positive for a nonsupercell pulse storm, and weakly positive for a supercell with multiple updraft pulses present. The relationship for the supercell was nonlinear, though larger flash rates are found during periods of larger CTD. Analysis here suggests that combining CTD with OTs and total lightning could have severe weather nowcasting value.

Towards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imagery

“…Deriving two-dimensional flow information at every point in the imagery would require either modification of previous AMV schemes or post-processing of the AMV data via objective analysis (e.g., Apke et al, 2018). The latter typically will not capture motion field discontinuities, resulting in incorrect flows near feature edges (Apke et al, 2016).…”

“…Motion-discontinuity-preserving optical flow will also benefit several current algorithms for monitoring deep convection in satellite imagery. Objective deep convection cloud-top flow field algorithms (Apke et al, 2016(Apke et al, , 2018 will particularly benefit when sharp cloud edges and ground pixels are present in an image scene. Systems that use infrared cloud-top cooling or emissivity differences for deep convection nowcasting will also improve with better estimates of pre-convective cumulus motion (Cintineo et al, 2014;Mecikalski and Bedka, 2006).…”

Severe Hail Fall and Hailstorm Detection Using Remote Sensing Observations

“…Bedka and Khlopenkov (2016) developed an objective OT identification/probability and a product quantifying OT texture in visible imagery (named ''visible texture rating''), which we employ for analysis in this study. Dynamical products based on mAMVs were derived through satellite feature tracking in 1-min visible and IR imagery via the algorithms outlined in section 2 of Bedka and Mecikalski (2005), section 2a of Bedka et al (2009), and section 3c of Apke et al (2018). Cloud-top vorticity (CTV) and cloud-top divergence (CTD) based on the mAMVs are retained for analysis when 1-mintemporal-resolution imagery is available.…”

“…Despite these limitations, many satellite signatures have been identified and linked to severe weather occurrence including rapid cloud-top cooling during storm initiation (Cintineo et al 2013), overshooting tops (OTs; cloud tops extending above the equilibrium level of deep convection) and the ''enhanced V'' in infrared (IR) imagery, now understood to represent the occurrence of aboveanvil cirrus plumes (AACPs-clouds injected into the lower stratosphere via gravity-wave breaking in the vicinity of OTs;McCann 1983;Brunner et al 2007;Setvák et al 2010;Dworak et al 2012;Homeyer 2014;Punge et al 2014;Bedka et al 2015;Homeyer et al 2017). Bedka et al (2018) recently found that AACPs are strong binary satellite indicators of significant severe hail ($2 in. in the largest dimension)-producing storms.…”

Observations of lightning in relation to transitions in volcanic activity during the 3 June 2018 Fuego Eruption

“…The Fuego eruption from 3 June 2018 provides another example of how the lightning data can fill these gaps in anticipating plume growth. The temporal resolution of the GOES-16 full disk advanced baseline imager on 3 June 2018 over Guatemala was 15 min, which is too coarse to track rapid changes in cloud top features53,54 .…”