A key component of air quality modeling is the correct estimation of photolysis rates. Photolysis rates depend on the intensity of solar radiation in the atmosphere and the molecular properties of the molecule undergoing photodissociation. Therefore, attenuation or enhancement of radiant energy due to atmospheric absorption and scattering is important in determining the photolysis rates. Since clouds can significantly alter the solar radiation in the wavelengths affecting the photolysis rates, they can have considerable impact on the photochemistry.

In this study we use satellite retrieved cloud transmissivity, cloud top height, and observed cloud fraction to correct photolysis rates for cloud cover in Community Multiscale Air Quality (CMAQ) modeling system. We performed CMAQ simulations using this method and compared the results with a simulation that used standard MM5 predictions as input. The simulations were performed at 4- and 12-km resolution domains over Texas, extending east to Mississippi, for the period of August 24 to August 31, 2000.

The results clearly indicate that not using the cloud observations in the model can drastically alter the predicted atmospheric chemical composition within the boundary layer and exaggerate or under-predict ozone concentration. Cloud impact is acute and more pronounced over the emission source regions and can lead to drastic errors in the model predictions of ozone and its precursors. Clouds also increased the lifetime of ozone precursors leading to their transport out of the source regions and causing further ozone production down wind. Longer lifetime for NOx and its transport over regions high in biogenic hydrocarbon emissions (in the eastern part of the domain) led to increased ozone production that was missing in the control simulation.