A Monte Carlo Technique for Designing Cloud Seeding Experiments

Abstract

The desigu of a field experiment in rainfall augmentation requires prior estimates of the duration of the experiment and the density of raingages. A ?Monte Carlo? method was developed to generate synthetic climatological rainfall data for various time periods and densities of raingages. The method was applied to a hypothetical cloud seeding experiment. Rainfall data for reporting networks were simulated and the resulting data were used to estimate the change in error variance induced by varying the density in a raingage network and the length of the experiment. The ?t? test was applied to the simulated nontransformed data which were skewed and to data normalized by a transformation. In addition, the generalized likelihood ratio test was used to test for differences in location parameters of the seeded and nonseeded gamma distributions having a common shape factor. The applicability and limitations of the method are discussed. With proper consideration of the limitations and with additional research on the problems encountered, it should be possible to obtain a preliminary estimate of the error variance of a proposed experimental design for many areas and various conditions.