Modeling the distribution of sub-grid moisture variability for cloud parameterizations in Large Scale Models
Poster PDF
Authors
Lazaros Oreopoulos — NASA
Peter M. Norris — NASA - GMAO/UMBC - GEST
Arlindo Da Silva — NASA - Goddard Space Flight Center
Description
Large Scale Models (LSM) with statistical sub-grid cloud parameterizations require realistic parametric forms for the distribution of sub-grid moisture. A vertical column of such distributions, together with vertical overlap assumptions, can be used to generate an ensemble of sub-column cloud fields for each LSM grid-column. These can then be operated on by Independent Column Approximation (ICA) radiative transfer to yield more accurate radiative averages for each grid-column.
We examine various candidate layer probability density functions (PDFs) for modeling total moisture content from Cloud Resolving Model (CRM) simulations over the ARM SGP site. We include both symmetric and skewed distributions. We also consider the effect of grid-scale trends on these PDFs. Such trends can sometimes dominate sub-grid small scale variability, particularly outside of the boundary layer.
Finally, we examine the use of ARM MICROBASE condensate retrievals to constrain the parameters of candidate total water PDFs via a maximum likelihood tail-fitting procedure.
