Use of Cloud Observations by Geostationary Satellite to Correct Photolysis Reaction Rates

Abstract submitted to "30th EARSeL Symposium: Remote Sensing for Science, Education and Culture"
Use of Cloud Observations by Geostationary Satellite to Correct Photolysis Reaction Rates
Arastoo Pour Biazar
UAH/NSSTC/ESSC
United States
Richard McNider
UAH/NSSTC/ESSC/USRA
United States
Keywords: GOES, Geostationary satellite, Air Quality, Photolytic reactions, photolysis rate, cloud radiative impact
Presentation preference: oral

A key component in 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 in the USEPA’s Community Multiscale Air Quality (CMAQ) modeling system. To test and evaluate the technique, CMAQ simulations for August of 2000 and 2006 for a variety of domains and grid resolutions were performed. The results from simulations using this method were compared with simulations using model clouds as input and were evaluated against USEPA’s surface monitors and special observations during Texas Air Quality Studies.

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.

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