Studies of Statistical Techniques to Evaluate Weather Modification

Operational cloud seeding projects, those designed to produce a desired change in the weather and that are non-experimental in nature, continue to be pursued widely in the United States. Evaluation of such projects is now recognized as having scientific benefits. This 2-year study addressed various techniques and statistical methods to perform evaluations and to learn more about how to modify weather. Through simulated changes in weather conditions, some of the most promising techniques were compared. It was determined that the principal component regression technique was the most powerful evaluation method under more circumstances than others. If a small increase (10% or so) of precipitation is expected, one should opt for multi-responses permutation procedures. If the expected effects of seeding are non-constant, use of simple non-parametric tests will yield higher powers. Several operational projects were selected for testing in the project. The use of synoptic weather information in evaluating two large-area projects, the Muddy Road aircraft seeding project and a project in northwestern Oklahoma using ground generators, helps delineate the seeding effects of these two programs. Efforts using historical data in evaluating a number of operational projects serve the purpose of testing the findings from the simulation studies, and the results are generally consistent. Various relevant issues examined during the project appear in this report. They include study of statistical bases for using historical data in evaluations; the potential biases in the evaluation process; and the combination of several tests of significance.

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