Analysis and Detection of Deep Convective Storms Through Remote Sensing Data Fusion
Abstract: Though visible and infrared (IR) wavelength satellite imagery has been collected for over 40 years, recent advances in imaging capabilities from the GOES-R series and the long-term imagery data record continues to provide new opportunities for analyzing and detecting deep convection at weather and climate time scales. Patterns atop severe convection such as overshooting, anvil-penetrating updrafts, and above-anvil cirrus plumes generated by such updrafts are now better resolved by GOES-R than the previous generation GOES- 8 to -15 data. Other datasets such as NEXRAD composites, space- and ground-based lightning mapping, and reanalyses allow us to better understand satellite-observed severe storm appearance and quantify our ability to detect these severe storm indicators. Long-term databases of overshooting convection and brightness temperature depressions from passive microwave imagers such as GPM, TRMM, AMSR, and SSM/I provide indications of when and where hailstorms could have occurred, which complement hailstorm frequency estimates derived from reanalyses and help the community understand severe storm risk over regions without sufficient weather radar coverage. This presentation will summarize recent deep convection research at NASA Langley and partner institutions, and opportunities for collaboration.
