Investigating the Scale Dependence of SCM Simulated Precipitation and Clouds by Using 3D Forcing at the ARM SGP site
Poster PDF
Authors
Shuaiqi Tang — Pacific Northwest National Laboratory
Shaocheng Xie — Lawrence Livermore National Laboratory
Minghua Zhang — Stony Brook University
Category
Convective clouds, including aerosol interactions
Description
Large-scale forcing data, such as vertical velocity and advective tendencies, are required to drive single-column models (SCM), cloud-resolving models, and large-eddy simulations. Previous studies suggest that some model discrepancies could be attributed to the lack of spatial heterogeneity in the large-scale forcing fields. The LLNL ARM project group has derived gridded large-scale forcing data by using a 3D variational analysis approach. We will use the 3D analysis of the March, 2000 IOP at the ARM SGP site to investigate the spatial variability of the large-scale forcing and its impact on SCM simulated clouds and precipitation. It shows that the large-scale forcing fields have large spatial variability especially when there are frontal systems passing through. Domain-mean forcing misrepresents the environment condition when the spatial variability is large. The use of gridded large-scale forcing data helps SCM better capture the characteristics of the frontal system with large spatial heterogeneity.
