Recent Advances in Detection of Overshooting Cloud Tops From Longwave Infrared Satellite Imagery

This paper describes an updated method for automated detection of overshooting cloud tops (OT) using a combination of spatial infrared (IR) brightness temperature patterns and modeled tropopause temperature. IR temperatures are normalized to the tropopause, which serves as a stable reference that modulates how cold a convective cloud should become within a given region. Anvil clouds are identified using histogram analysis and cold spots embedded within anvils serve as OT candidate regions. OT candidates are then assigned an OT probability, which can be interpreted as a metric of storm intensity and an estimate of confidence in a detection for a particular pixel. It is produced using an original mathematical composition of four factors: Tropopause-normalized temperature, prominence relative to the surrounding anvil, surrounding anvil area, and spatial uniformity of anvil temperature, which are calculated from empirically derived sensitivity curves. The shape of the curves is supported by independent analysis of a large sample of matched IR and radar-derived OT regions. An optimal sensitivity for each factor was determined by maximizing correlation between the OT probability and a set of human-identified OT regions. Coarser spatial resolution of GOES-13 data cause OTs to be less prominent compared to GOES-16, necessitating different sensitivities for each satellite. Detection performance is quantified for each satellite based on human OT identifications and as a function of how prominent the OT appeared in visible and IR imagery. Based on analyses of human-identified OTs, OT detection accuracy, defined by the area under a receiver operating characteristic curve, is determined to be 0.94 for GOES-16 and 0.78 for GOES-13.