Simulation of Continental Shallow Cumulus Populations Using an Observation‐Constrained Cloud‐System Resolving Model

Abstract Continental shallow cumulus (ShCu) clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land‐Ecosystems (HI‐SCALE) field campaign are simulated by using an observation‐constrained cloud‐system resolving model. On this day, ShCu forms over Oklahoma and southern Kansas and some of these clouds transition to deeper, precipitating convection during the afternoon. We apply a four‐dimensional ensemble‐variational (4DEnVar) hybrid technique in the Community Gridpoint Statistical Interpolation (GSI) system to assimilate operational data sets and unique boundary layer measurements including a Raman lidar, radar wind profilers, radiosondes, and surface stations collected by the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory into the Weather Research and Forecasting (WRF) model to ascertain how improved environmental conditions can influence forecasts of ShCu populations and the transition to deeper convection. Independent observations from aircraft, satellite, as well as ARM's remote sensors are used to evaluate model performance in different aspects. Several model experiments are conducted to identify the impact of data assimilation (DA) on the prediction of clouds evolution. The analyses indicate that ShCu populations are more accurately reproduced after DA in terms of cloud initiation time and cloud base height, which can be attributed to an improved representation of the ambient meteorological conditions and the convective boundary layer. Extending the assimilation to 18 UTC (local noon) also improved the simulation of shallow‐to‐deep transitions of convective clouds