695 Accounting for spatiotemporal variation of rainfall measurements in detecting ground-based sources of weather modification

Wednesday, 26 January 2011
4E (Washington State Convention Center)
Raymond Chambers, University of Wollongong, Wollongong, NSW, Australia; and S. Beare and S. Peak

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

Weather modification trials tend to rely on randomized experimental designs. Unfortunately, these designs have so far not demonstrated sufficient power to detect a small weather modification signal against the large level of background variation in rainfall. Further, randomized experimental designs are generally not possible when dealing with ground-based sources of weather modification such as industrial pollution. Statistical modeling of 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 conditions in time and fixed effects due to the location of rainfall gauges. Unfortunately, a large part of the natural variation in rainfall measurements is a caused by a mix of spatial and temporal influences. Meteorological conditions are not spatially homogenous and orographic effects can depend on prevailing conditions. Importantly, exposure of the 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. This analysis presented in this paper explores the limitations imposed by ignoring the spatiotemporal variation in the rainfall data collected for the trial, and takes advantage of modern statistical methods to construct an appropriately specified model for these data. In doing so, a key issue that is addressed is the level of analysis, and particularly whether it is appropriate to use gauge level as opposed to aggregated data such as average daily rainfall in statistical inference in this situation. Our results suggest that there is a substantial increase in the likelihood of detecting a modification signal when the analysis is carried out at the gauge level, and accounts for the spatial and temporal correlation structure of rainfall data.

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