The Impact of Improved Cloud Characterization in the Weather Research & Forecasting (WRF) Model on Air Quality Simulations

In air quality simulations, clouds have a significant role as they modulate photolysis rates, impact boundary-layer development, lead to deep vertical mixing of pollutants and precursors, and induce aqueous phase chemistry. 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 Southeast United States. In turn, a poor representation of clouds impacts the photochemical model's ability in simulating the air quality. In the current activity the Geostationary Operational Environmental Satellite (GOES) derived cloud fields are assimilated within Weather Research and Forecasting (WRF) model to improve simulated clouds. A technique was developed to dynamically support cloud formation/dissipation within WRF based on GOES observations. Satellites provide the best observational platform for defining the formation and location of clouds. 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. Thus, the technique uses observations to identify model cloud errors, estimates a target vertical velocity and moisture to create/remove clouds, and adjust the flow field accordingly. The technique was implemented and tested in WRF for a month-long simulation during August 2006. The results show 7-10% improvement in model cloud simulation. The technique proved to be effective regardless of the convective parameterization scheme used. Furthermore, the impact of these improvements on air quality simulations was investigated. Preliminary results from this activity will be presented.