Spatial Correlations of Monthly Rainfall: Applications in Climatology and Weather Modification Experiments

A. A. N. Patrino Engineering Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830

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N. C. J. Chen Engineering Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830

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R. L. Miller Engineering Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830

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Abstract

Spatial correlations based on monthly rainfall totals from northwest Georgia for the period 1949–77 are studied. This work, a part of the Meteorological Effects of Thermal Energy Releases (METER) Program, determines natural variability rainfall trends and assists the field studies of potential precipitation effects of the Bowen Electric Generating Plant near Cartersville, Georgia. The spatial correlations, based on the overall record as well as the stratified data in terms of “wet” and “dry” seasons, are investigated with regard to distance between stations, wind direction and topography. The results indicate a strong dependence of the spatial correlation patterns on the prevailing storm tracks in the area.

A method is developed using the spatial correlation as an indicator of effects in weather modification experiments. This method is based on the generation of empirical distribution functions by randomization for various sample sizes. The application of this technique to the Plant Bowen study in a control-target statistical design reveals preliminary positive evidence of rainfall modification in the target area.

Abstract

Spatial correlations based on monthly rainfall totals from northwest Georgia for the period 1949–77 are studied. This work, a part of the Meteorological Effects of Thermal Energy Releases (METER) Program, determines natural variability rainfall trends and assists the field studies of potential precipitation effects of the Bowen Electric Generating Plant near Cartersville, Georgia. The spatial correlations, based on the overall record as well as the stratified data in terms of “wet” and “dry” seasons, are investigated with regard to distance between stations, wind direction and topography. The results indicate a strong dependence of the spatial correlation patterns on the prevailing storm tracks in the area.

A method is developed using the spatial correlation as an indicator of effects in weather modification experiments. This method is based on the generation of empirical distribution functions by randomization for various sample sizes. The application of this technique to the Plant Bowen study in a control-target statistical design reveals preliminary positive evidence of rainfall modification in the target area.

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