Towards a Better Understanding of Convective Growth Parameters using GOES-R Mesoscale Imagery

Earth System Models (ESMs) poorly simulate the diurnal cycle of convective clouds. ESMs treat cumulus clouds as simplified larger clusters constrained by large-scale parameters, known as cumulus parameterization. The cumulus parameterization in ESMs tend to initiate convection of cloud systems several hours compared to observations, leading to a systematic bias in precipitation timing. This issue is most notable in the Southeastern (SE) U.S. To investigate the critical factors controlling the initiation, growth and diurnal cycle of convective clouds, geostationary satellite observations over the SE U.S. The Geostationary Operational Environmental Series-R (GOES-R) satellites have two mesoscale sectors each that allow imaging every minute across a 1000 km x 1000 km area. The 1-minute temporal resolution of imagery allows for the tracking of convective cells. The brightness temperature in the GOES Level 2 Cloud and Moisture Imagery product can be used to retrieve cloud top temperatures. As the cloud top height grows from the convective growth of the updraft, the temperature decreases. By finding the time rate of change of the measured brightness temperature (BT) the convective temperature difference can be assumed to be equal to the brightness temperature change, and by using a cloud top lapse rate of 5.5 K/km, the convective growth rate can be calculated. The relationship between the convective growth rate calculated from GOES mesoscale imagery, and environmental variables such as convective available potential energy (CAPE) obtained from reanalysis data with be studied. By better understanding the environmental factors that influence convection growth, the cumulus parameters in ESMs can be improved to increase the accuracy of convective initiation and precipitation timing predictions.