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  • Author or Editor: Robert G. Flocchini x
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Lowell L. Ashbaugh, Leonard O. Myrup, and Robert G. Flocchini

Abstract

The transport patterns of fine sulfur aerosols in the western United States are shown. The large-scale resultant horizontal flux was computed in terms of that contributed by the mean flux versus that contributed by a turbulence, or eddy, component. The large-scale eddy component of the resultant flux is shown to be important in many cases. In the northern Great Plains the eddy flux often has a greater magnitude than the mean flux and its direction is from the east, opposite the mean flux. In the southwestern United States, the transport is accomplished primarily by the mean flow and the direction is from the south. This indicates that high sulfur concentrations are carried into the northern Great Plains from the east as periodic episodes, while high concentrations in the south are caused by sources to the south which are within the mean flow field.

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Mark C. Green, Robert G. Flocchini, and Leonard O. Myrup

Abstract

Temporal principal components analysis was applied separately to monthly long-term wind, temperature, and precipitation data for Southern California. Physical explanations of the significant eigenvectors are presented. Cluster analysis of the component loadings was then used to form groups of months (seasons) having similar spatial patterns. The resulting groupings of months differed from the conventional definition of seasons. The wind and temperature analyses grouped the same months, with long summers, moderately long winters, short springs, and very short autumns. The precipitation analysis formed a long season, including the winter months, representing synoptic systems occasionally passing through the area, a summer thunderstorm season associated with influx of moisture from the south, and dry transitional periods separating these seasons. The purpose of the analysis was to pregroup two years of hourly wind data to remove most of the annual signal before applying spatial eigenvector analysis for a mesoscale climatological classification study. The approach is expected to be most useful when applied to mesoscale areas with significant seasonal variation in spatial patterns of climatic variables.

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