Abstract
In regional-scale air-pollution models probably no other source of uncertainty ranks higher than the current ability to specify clouds and soil moisture. Because modeled clouds are highly parameterized, the ability of models to predict the magnitude and spatial distribution of radiative characteristics is highly suspect and subject to large error. While considerable advances have been made in the assimilation of winds and temperatures into regional models (Stauffer and Seaman, 1990), the poor representation of cloud fields from point measurements at National Weather Service stations and the almost total absence of observations of surface moisture availability has made assimilation of these variables difficult if not impossible. Yet, the correct inclusion of clouds and surface moisture are of first-order importance in regional-scale photochemistry. Consider the following points relative to these variables.
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McNider, R.T., Norris, W.B., Casey, D.M., Pleim, J.E., Roselle, S.J., Lapenta, W.M. (1998). Assimilation of Satellite Data in Regional Air Quality Models. In: Gryning, SE., Chaumerliac, N. (eds) Air Pollution Modeling and Its Application XII. NATO • Challenges of Modern Society, vol 22. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9128-0_3
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