Simulation of Continental Shallow Cumulus Populations Using an Observation‐Constrained Cloud‐System Resolving Model. (31st August 2020) - Tai, Sheng‐Lun; Fast, Jerome D.; Gustafson, William I.; Chand, Duli; Gaudet, Brian; Feng, Zhe; Newsom, Rob
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. Plain Language Summary: Accurate prediction of life cycle of shallow convective clouds is very challenging for the existing weather and climate models since they have difficulties in reproducing realistic atmospheric structure within a shallow layer near the Earth surface (also called boundary layer, roughly below 2‐km height in daytime). To tackle this fundamental problem, the observational data collected for operational weather prediction as well as unique boundary layer observations measured near north‐central Oklahoma are integrated to constrain the behavior of cloud‐system resolving model across different scales with an emphasis on boundary layer. The results show that the model biases in atmospheric conditions, especially humidity within boundary layer, are reduced with the modification informed by observations. As a consequence, shallow convective clouds are well reproduced in terms of cloud evolution in time and space which are verified by various cloud measurements. It also suggests that surface observation can be used to correct cold pool intensity which is closely related to the maintenance of deep convective clouds that are transitioned from shallow convective clouds. Key Points: Observations are utilized to constrain cloud‐system resolving model to improve the prediction of continental shallow cumulus populations The impact of assimilating boundary layer measurements on simulation of shallow cloud populations is especially examined The evolution of shallow clouds is better simulated as more realistic ambient environment and boundary layer are represented in the model