Variations in meteorological conditions can dominate the effects of seeding and are often times much larger than the effect of seeding (10-100 times). These variations can occur in space and in time and can significantly affect the results from any randomized seeding experiments depending on a single statistical test assuming that the samples are randomly drawn from the same distribution of potential values (treatment application for these measurements was at random). More statistically efficient means of analysis are required if we hope to gain significant results in realistic time frames such as multivariate statistical models by including covariates that influence the precipitation processes in a region to control for natural variations in rainfall. In contrast to pure randomization analysis, this type of analysis estimates the conditional contribution to rainfall by meteorological and for example aerosol effects.
The presentation will provide an overview of methods and analyses from recent experiments to highlight some of the issues related with statistical analyses and evaluation of rainfall enhancement experiments. It will also provide a future framework for conducting randomized cloud seeding experiments.