| Abstract: | ABSTRACT Operational cloud seeding projects, those designed to produce a desired change in the weather and that are nonexperimental in nature, continue to be pursued widely in the United States. A recurring question by scientists, project sponsors, and cloud seeders has been, “was the weather altered and if so, by how much?” Evaluation of such projects is now recognized as having scientific benefits, and a four-year study has addressed various techniques and statistical methods to perform evaluations and to learn more about how to modify the weather. Most such evaluations hinge on some type of space-time comparisons, but valid comparisons can be obtained only avoiding biases in the project design and operation. Through simulated changes in weather conditions, it was determined that the principal component regression techniques were used to evaluate selected rain and hail modification projects, revealing modification in certain projects and none in others. Various relevant issues have been examined such as use of other weather variables (covariates) to increase detection power, the validity of using historical data as controls for discrete operational periods, possible randomization options during cloud seeding operations, and analyses of individual rain events versus that based on monthly or seasonal units. |
