Shallow Convection Dataset Simulated by Three Different Large Eddy Models

Abstract

Shallow convection plays an important role in transporting heat and moisture from the near-surface to higher altitudes, yet its parameterization in numerical models remains a great challenge, partly due to the lack of high-resolution observations. This study describes a large eddy simulation (LES) dataset for four shallow convection cases that differ primarily in inversion strength, which can be used as a surrogate for real data. To reduce the uncertainty in LES modeling, three different large eddy models were used, including SAM (System for Atmospheric Modeling), WRF (Weather Research and Forecasting model), and UCLA-LES.

Results show that the different models generally exhibit similar behavior for each shallow convection case, despite some differences in the details of the convective structure. In addition to grid-averaged fields, conditionally sampled variables, such as in-cloud moisture and vertical velocity, are also provided, which are indispensable for calculation of the entrainment/detrainment rate. Considering the essentiality of the entraining/detraining process in the parameterization of cumulus convection, the dataset presented in this study is potentially useful for validation and improvement of the parameterization of shallow convection.