Automated OT detection methods have been developed in recent years at NASA Langley Research Center using support primarily from the NOAA GOES-R program. OTs appear anomalously cold in satellite infrared (IR) imagery and highly textured in visible imagery, signatures that can be reliably detected using pattern recognition methods. Imagery-based pattern recognition is combined with 1) NWP tropopause and equilibrium level analyses and 2) a large sample of both OT and non-OT anvil regions identified within 0.25 km MODIS imagery to develop a model to statistically differentiate between the two populations and arrive at an OT Probability product. Co-located high OT Probability values and visible texture detection are quite reliable indicators that an OT is truly present.
A combination of immediate access to an online GOES Imager data archive from the University of Wisconsin-Madison Space Science and Engineering Center via the McIDAS-X software and the extremely efficient LaRC OT detection processing framework has enabled development of a 20-year climatology of OT and deep convective anvil clouds over much of the Western Hemisphere (60° N-S, 30°-180° W). GOES imagery collected at 30-min intervals has been processed over this domain and compiled into hourly analyses, revealing the climatological behavior of deep convection throughout the diurnal cycle at high spatial resolution. This presentation will describe this unique and comprehensive dataset, highlighting daily, regional, and seasonal storm distributions and trends across the 20-year period.