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AIR QUALITY FORECASTING

Pour-Biazar, A., R. T. McNider, S. J. Roselle, R. Suggs, G. Jedlovec, D. W. Byun, S. Kim, C. J. Lin, T. C. Ho, S. Haines, B. Dornblaser, R. Cameron (2007), Correcting photolysis rates on the basis of satellite observed clouds, J. of Geophys. Res., 112, D10302, doi:10.1029/2006JD007422. Full Text

In this study, we introduce a technique for using the satellite observed clouds to correct photolysis rates in photochemical models. This technique was implemented in EPA’s Community Multiscale Air Quality modeling system (CMAQ) and was tested over a ten day period in August 2000 that coincided with Texas Air Quality Study (TexAQS).

Use of satellite observed clouds significantly improved model predictions in areas impacted by clouds. The results indicated that inaccurate cloud prediction in the model can significantly exaggerate or under-predict ozone concentration. Cloud impact is acute and more pronounced over the emission source regions and can lead to large errors in the model predictions of ozone and its by-products. At some locations the errors in ozone concentration reached as high as 60 ppb which was mostly corrected by the use of our technique. 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 nitrogen oxides (NOx=NO+NO2) 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. Over Houston-Galveston Bay area, the presence of clouds altered the chemical composition of the atmosphere and reduced the net surface removal of reactive nitrogen compounds.

Errors arising from an inconsistency in the cloud fields seem to be significant and can impact the performance of photochemical models used for case studies as well as for air quality forecasting. Air quality forecast models often use the model results from the previous forecast (or some adjusted form of it) for the initialization of the new forecast. Therefore, errors due to incorrect cloud specification will affect the initialization fields and can propagate into the future forecasts. Thus, the use of observed clouds in the preparation of initial concentrations for air quality forecasting could be beneficial.

Figure 7. (b) Errors in ozone concentration due to inaccurate cloud fields can be as high as 70 ppb.  This figure shows the largest differences in O3 between assimilation and control simulations (assim-control) for the entire period of study covering from 0 GMT, August 24, 2000, to 0 GMT, September 2, 2000.

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