Impact of using satellite-derived clouds to calculate photolysis rates in the Community Multiscale Air Quality (CMAQ) Model v4.7

Recent simulations of the eastern United States using the Community Multiscale Air Quality (CMAQ) modeling system identified the summer of 2005 as a period of poor model performance for fine particulate sulfate (SO42-). The meteorological conditions during this time period included hot and dry conditions in the upper Midwest, an extreme heat wave in the southwestern U.S., and a high frequency of tropical cyclones affecting the southeastern U.S. The primary pathways for SO42- formation in the atmosphere include the gas-phase reaction of SO2 with OH, and aqueous oxidation of S(IV) to S(VI) in cloud water. An investigation using the CMAQ sulfur tracking model for this time period estimates that the aqueous pathway accounted for ~50% of the SO42- in the upper Mississippi Valley and ~75% in the southeastern U.S; the gas-phase reaction accounts for the other SO42- production. Because clouds play such an important role in the formation of SO42- (both directly by providing the cloud water for aqueous oxidation and indirectly by attenuating the solar radiation that drives the photochemistry and OH concentrations), errors in cloud cover may be affecting the model's performance during this period.

The 5th Generation Mesoscale Model (MM5) and now more recently the Weather Research and Forecasting (WRF) model include the capability of assimilating satellite-derived clouds to improve the performance of model predictions. In addition, CMAQv4.7 includes an option to use satellite-derived cloud products to calculate photolysis rates. This study will investigate the impacts of using satellite-derived products on SO42- model performance. The period of July 2005 will be simulated using two different CMAQ model configurations, one utilizing model-derived photolysis rate estimates and the other utilizing the satellite-derived estimates. The SO42- predictions from each simulation will be compared to available surface observations and the performance of each simulation assessed. This work has direct implications for many retrospective air quality simulation applications, including federal rule-making and to State Implementation Plan (SIP) development.