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Stephen F. Mueller, Aaron Song, William B. Noms, Shekar Gupta, and Richard T. McNider


Airflow patterns and pollution transport in the southern Appalachian Mountains region of the southeastern United States are examined using mesoscale meteorological models and a Lagrangian particle dispersion model (LPDM). The two primary goals of this work are 1) to identify a meteorological modeling methodology that can be used in regional photochemical modeling, and 2) to identify large regional ozone precursor sources that may impact the southern Appalachians during periods having high ozone levels. Four episodes characterized by measured high levels of ozone (1-h average concentrations greater than 90 ppb) at remote monitoring sites are the focus of the modeling efforts. To address the first goal, several methods of airflow modeling involving varying degrees of complexity are examined to find one that reliably simulates the complex wind patterns that occur. A hydrostatic model with homogeneous initialization, a nonhydrostatic model with homogeneous initialization, and a nonhydrostatic model with nonhomogeneous initialization and four-dimensional data assimilation (FDDA) are evaluated against available wind observations. The method using nonhomogeneous initialization and FDDA is found to best reproduce observed wind patterns. Results of a test of model sensitivity to the strength of the FDDA are described.

In addressing the second project goal, a LPDM driven by computed meteorological fields is used to simulate the potential for ozone precursor emissions (in the form of NOx) to be transported from nearby major sources toward the mountains. LPDM simulations indicate that one of the urban areas was the most likely source to influence the monitoring sites experiencing high ozone levels during three of the four episodes. However, none of the plumes are computed to be over the monitoring sites for the length of time that the high ozone concentrations were actually observed. Detailed air quality data for one episode suggest the presence of a large urban plume passing over the mountains and originating from outside the modeling domain. This implies that a larger domain is needed for photochemical modeling.

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Richard T. McNider, Aaron J. Song, Daniel M. Casey, Peter J. Wetzel, William L. Crosson, and Robert M. Rabin


An assimilation technique is described in which satellite-observed surface skin temperature tendencies are used in a model surface energy budget so that the predicted rate of temperature change in the model more closely agrees with the satellite observations. Both visible and infrared GOES satellite data are used in the assimilation. The technique is based on analytically recovering surface moisture from similarity expressions derived from an evapotranspiration residual obtained as a difference between the unadjusted model evapotranspiration and the satellite-inferred evapotranspiration. The technique has application in regional-scale models where surface parameters such as root zone moisture, soil moisture, etc., are unknown. It is assumed that the largest error in the surface energy budget is in the evapotranspiration term. Two tests are given for the technique, first, a one-dimensional test against FIFE data and, second, a three-dimensional test over Oklahoma. In these cases the technique appears to correctly adjust the model response to agree better with observations.

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