Utilization of Satellite Observations for Improving Cloud Simulation in WRF

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. A poor representation of clouds impacts the photochemical model's ability to predict ozone. 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. Three different approaches (two statistical and one analytical) for estimating the target vertical velocity and moisture were tested. While all three approaches improved model-simulated clouds, the analytical approach produced the best results (7-10% improvement in the agreement index). The technique proved to be effective regardless of the convective parameterization scheme used. Preliminary results from this activity will be presented.