An Observational Estimate of Entrainment in Thunderstorms using Gridded Radar Data

Deep moist convection (DMC) is a common process in the Earth’s atmosphere that is comprised of rising parcels of moist air forming thunderstorm updrafts. Entrainment, the process of updrafts ingesting relatively drier air from their surroundings, can negatively affect the strengths of these updrafts to a significant degree. This makes the task of forecasting the behavior of updrafts difficult. Climate models and some numerical weather prediction (NWP) models utilize cloud parameterization due to the computational difficulty in resolving length scales small enough resolve updrafts. For both applications, a proper quantization of entrainment is necessary for an improvement in predictive accuracy. In this project, we utilize a four year mesoscale convective system (MCS)/isolated deep convection (IDC) tracking algorithm to sample the environment both inside and outside convective cores, then apply these vertical soundings to an entrainment plume model to back out the entrainment rate. Preliminary results show that fractional entrainment is inversely correlated with shear and the planetary boundary layer (PBL) depth.