A simplified method for the detection of convection using high resolution imagery from GOES-16

Abstract. The ability to detect convective regions and assimilating the proper heating in these regions is the most important skill in forecasting severe weather systems. Since radars are most directly related to precipitation and are available in high temporal resolution, their data are often used for both detecting convection and estimating latent heating. However, radar data are limited to land areas, largely in developed nations, and early convection is not detectable from radars until drops become large enough to produce significant echoes. Visible and Infrared sensors on a geostationary satellite can provide data that are less sensitive to drop size, but they also have shortcomings: their information is almost exclusively from the cloud top. Relatively new geostationary satellites, GOES-16 and GOES-17, along with Himawari-8, can make up for some of this lack of vertical information through the use of very high spatial and temporal resolutions. This study develops two algorithms to detect convection at different life stages using 1-minute GOES-16 ABI data. Two case studies are used to explain the two methods, followed by results applied to one month of data over the contiguous United States. Vertically growing clouds in early stages were detected using decreases in brightness temperatures over ten minutes. Of the detected clouds, the method correctly identifies 71.0 % to be convective. For mature convective clouds which no longer show decreases in brightness temperature, the lumpy texture, and rapid temporal evolution can be observed using 1-minute high spatial resolution reflectance data. The algorithm that uses texture and evolution for mature convection detects with an accuracy of 85.8 %. 54.7 % of clouds that are identified as convective by the ground-based radars are missed by the satellite. These convective clouds are largely under optically thick cloud shields.