Evaluation of the Wyoming Randomized Seeding Program Using Ensemble Modeling

The main source of water in the western U.S. is snowmelt from major mountain ranges. This resource is renewed annually through snowfall that melts in the spring. While orographic cloud seeding using silver iodide (AgI) is commonly performed over many western U.S. mountain ranges today to enhance this water supply, the likely impact on snowpack is uncertain. This is due to the fact that the likely impact of cloud seeding is a relatively small snowfall signal compared to the large and highly variable natural background precipitation, making it difficult to evaluate using current methods of evaluation (add references). Since it is impossible to know what the natural precipitation would be during a storm that is seeded through direct measurements with snowgauges, a randomized seeding program lasting multiple years using multiple correlated target areas is usually recommended to perform an evaluation of the likely amount of extra precipitation produced as a result of cloud seeding. The success of such a randomize experiment depends on the accuracy of the snowgauges used and the amount of additional snow produced by seeding. If the amount of additional snow produced is small (< 5%), the number of years needed to evaluate the effect is relatively large.

Recent advances in computational and modeling capability have changed the math on this problem. New and improved models that run on fast and efficient computers have allowed us to perform thousands of simulations using an ensemble approach to characterize the likely seeding signal in a noisy background field for even small seeding effects. Critical to the success of this approach is verification of the model results using accurate snowgauges.

This paper will describe an evaluation of the Wyoming RSE using an ensemble modeling approach.