Improvements to the Representation of Shallow Clouds in WRF

A new parameterization for shallow cumuli, called the Cumulus Potential (CuP) scheme, has been developed that explicitly links boundary-layer turbulence with shallow clouds. This new parameterization has been coupled with the Kain Fritsch (KF) cumulus parameterization in the Weather Research and Forecasting (WRF) model. In its default configuration, the KF scheme uses an ad-hoc temperature perturbation as a trigger function that is used to determine if convection occurs. In the coupled KF-CuP scheme the temperature perturbation has been modified to more realistically account for both sub-grid temperature and humidity variations.

In order to evaluate the performance of WRF, two specially constructed data sets have been completed. The first documents the macroscale properties of shallow cumuli over the Southern Great Plains of North America, while the second investigates the surface radiative forcing due to shallow cumuli. Both data sets make use of a cloud radar and lidar to estimate cloud boundaries and to help identify days with single-layer shallow cumuli. The surface shortwave forcing due to shallow clouds is determined using data from a network of surface radiometers.

Simulations using both the default KF and KF-CuP scheme have been completed for the summer (May-August) of 2004. Overall, the modified scheme does a much better job predicting the cloud properties than does the default scheme. The default scheme under-predicts the frequency of small amounts of cloud fraction and over-predicts the frequency of large amounts of cloud fraction. The cloud fraction predicted by the modified scheme is in much better agreement with the observations made at the Atmospheric Radiation Measurement program's Southern Great Plains Central Facility in Oklahoma, leading to better estimates of the radiative impact of shallow clouds.