In the scientific literature, severe storm visual observations have been incorporated into a wide range of research on deep convection, supercells and their flanking lines, tornadoes, hurricanes, microbursts, etc. Some of these visual observations have been used to stratify supercell types (i.e. low precipitation, classic, and high precipitation) and establish conceptual models amongst a complex spectrum of storm types and visual appearances. Nevertheless, the wide range of visual structures of deep convection and severe storms has yet to be fully explored and linked to the dynamics of convection. The ever-growing images of severe convection regularly documented on the internet by professional and casual storm chasers over the last decade provides an unprecedented opportunity to incorporate this visual documentation into a multi-media database that can add a new dimension for analysis of severe storms.
This paper documents a wide variety of visual characteristics of severe storms and convection/precipitation using digital photography available from storm chasers. These characteristics are evaluated for a large number of storms, along with environmental sounding profiles for some select storms. Methods are examined and developed to integrate digital media and observational data into a multi-media database. In addition, meta-data needs and structures are evaluated to assure broad usability of the data. Finally, the dynamics of convection are reviewed to pose questions of how these visual cues may relate to storm dynamics that could be more thoroughly explored with future observational research programs such as the Verification of the Origins of Rotation Experiment 2 (VORTEX2) or high-resolution numerical models. Improved understanding of the visual aspects of severe convection can be useful for 1) establishing more detailed conceptual models of severe storms, 2) improved training relating radar analysis to observations, 3) guiding future research and improvements in storm spotting for NWS warning decision making, and 4) evaluating the capability of numerical models to represent observed phenomena.