Evaluating the Effectiveness of Lightning Data Assimilation in Parameterized Deep Convection

Abstract Deep convection greatly influences the upper troposphere and lower stratosphere (UTLS) by rapidly transporting aerosols and chemical species from the planetary boundary layer (PBL), affecting air quality, atmospheric chemistry, and climate. This study simulates two deep convection cases from the Deep Convective Clouds and Chemistry (DC3) campaign: a supercell on 29 May 2012 and a mesoscale convective system (MCS) on 11 June 2012. The storms are simulated using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), employing established parameterizations for convection and transport. To enhance the accuracy of the simulated deep convection and subsequent transport, we employ a novel lightning data assimilation (LDA) scheme. LDA uses lightning data to continuously activate or suppress the cumulus scheme throughout the simulation, serving as a proxy for thunderstorm electrification not explicitly simulated by the model. Simulations with and without LDA are compared against observations. The LDA scheme’s advantages and limitations are evaluated in terms of basic meteorological parameters, simulated precipitation, cloud top heights, and convective transport of carbon monoxide (CO). Results indicate that LDA provides major improvements in the location and amounts of observed convective rainfall for both cases. It produces more accurate cloud top heights for the supercell case but has relatively less success in representing areal cloud coverage for the MCS case. LDA simulations improved the supercell case by producing taller cloud top heights and more precipitation. LDA also enhanced precipitation representation in the MCS. Improvements in the convective transport of CO in both cases are also described.