On the Development of Real-time GOES-SRSOR Derived Flow Products of Deep Convective Cloud Tops

Observations of objectively identified flow vectors from super rapid scan operations (SRSOR) for the geostationary operational environmental satellite (GOES)-R series collection periods in 2014 have suggested that distinct and consistent flow patterns are observable at the cloud top over both ordinary and more organized and maintained types of convective storms. The 1–min resolution of SRSOR allows for cloud top divergence (CTD) and vorticity (CTV) flow fields to be resolved on a temporal scale that rivals those available with common next generation radar network techniques (2–5 min). Mesoscale Atmospheric Motion Vectors (mAMVs) are obtained by identifying trackable targets such as brightness temperature minima, maxima and gradients and correlating them through time sequences of satellite images (Velden et al. 1997, 1998). The identified mAMVs are converted to CTD and CTV fields using a Barnes objective analysis (Barnes 1973).

For this presentation, SRSOR storm case examples are presented from the two-week collection periods in late May and early August 2015. Field generation toward a preliminary SRSOR-mAMV algorithm explores the utility of background flow addition, cloud edge detection, and Barnes objective analysis optimization with both ordinary cell and supercell case studies. For comparison, the analysis of satellite flow fields are also evaluated against idealized supercell storms as simulated in the Advanced Research Weather Research and Forecasting (WRF-ARW) core model. Comparisons are also made of flow field signatures derived from mAMVs to early conceptual models of supercell structure and dynamics [e.g., those by Lemon and Doswell (1979) and Wiesman and Klemp (1982)].

Early observational results suggest that the CTV “couplet” signature does not always exist over tornadic supercells (based on collections in 2015), however all supercell cases in 2014 and 2015 exhibit strong, maintained CTD maxima values located near their respective overshooting tops. Ordinary convection CTD signals tend to be much weaker and shorter lived than supercell cases. Interestingly, supercell storms simulated in WRF-ARW develop the same CTV “couplet” signature, which when analyzed with the vorticity tendency equation, suggests that vortex tilting is the primary mechanism creating this phenomenon. Reasons for flow modification that may remove the CTV signature are also explored, such as unfavorable shear environments and buoyancy depth modification. This study shows that future products such as SRSOR-mAMV derived CTD and CTV fields are likely to prove to be useful tools for operational forecasters and Warn-On-Forecast methodologies when examined with respect to ongoing severe weather and the warning decision process.