Accounting for Spatiotemporal Variation of Rainfall Measurements when Evaluating Ground-Based Methods of Weather Modification

Weather modification trials tends to rely on radomized experimental designs.  Unfortunately, these designs have so far not demonstatrated sufficient power to detect a small weath modification signal against the large level of backgroud variation in rainfall.  Further, randomized experimental designs are generally not possible when dealing with groundbased sources of weather modification such as industrial pollution.  Statistical modeling rainfall gauge measurements that attempt to control for meteorological and orographic variation in rainfall measurements seem better suited in this regard. Evaluation would be relatively simple if we could separate the sources of variation into changes in meteorological and orographic variation in rainfall measurments that attempt to control for meteorological and orographic relatively simple is we could seperate the sources of variation into into changes in meterological conditions in time and fixed effects due to the location of rainfall gauges .  Unfortunately, large part of the natural variation in rainfall measurements is caused by a mix of spatial and temporal influences.  Meteorological conditions are not spatially homogeneous and orographic effects can depend on prevailing conditions. Importantly, exposure of rainfall gauges to an effect is generally dependent on meteorological conditions, primarily wind direction and speed. A ground-based rainfall enhancement trial was conducted using a randomized crossover design in South Australia in 2009.  The analysis presented in this paper explores the limitations imposed by ignoring spatiotemporal variation in the rainfall data collected in this trial and takes advantage of modern statistical methods to construct an appropriately specified model for these data.  The level at which analysis is performed is addressed, particularly whether it is appropriate in this situation to use guage-level data as opposed to aggregated data such as average daily rainfall, in statistical inference.  Our analysis, which accounts for the spatial and temporal correlation structure of rainfall data, suggests that there is a substantial increase in the likelihood of detecting a modification signal when the analysis is carried out at the gauge level.