Dynamical Adjustment of Clouds in WRF based on GOES-Derived Clouds

Clouds have a profound role in photolysis activity, boundary-layer development and deep vertical mixing of pollutants and precursors. Unfortunately, numerical meteorological models still have difficulty in creating clouds in the right place and time compared to observed clouds. This is especially the case when synoptic-scale forcing is weak, as often is the case during air pollution episodes. In the current activity GOES-derived cloud fields are assimilated within Weather Research and Forecasting (WRF) model to improve model location and timing of clouds. Satellites provide the best observational platform for defining the formation and location of clouds. GOES cloud observations were used to evaluate WRF performance with respect to cloud prediction during August 2006. A technique was developed to dynamically support cloud formation/dissipation within WRF based on GOES observations. The basic assumption in the technique is that model clouds on average are associated with positive vertical motion and clear areas with negative vertical motion. This assumption was tested for a month-long simulation and appropriate statistics were developed. The technique takes advantage of model statistics of vertical motion, cloud liquid water, cloud depth, and cloud albedo with the intent that once these relations were developed then satellite observations of cloud could be inverted to create an appropriate environment for cloud formation in the model. Preliminary results from this activity will be presented.