A Bayesian Analysis of a Multiplicative Treatment Effect in Weather Modification

Bayesian techniques are developed to investigate a mldtiplicative treatment effect. when data samples on a control and a treatment are available. The random variables are assumed to follow highly skewed gamma distributions with the same shape parameters. The data sample is from a randomized airborne pyrotechnic seeding experiment, on 52 isolated cumulus clouds in sorlth Florida, half of which were massively injected with silver iodide smoke and the other untreated half were identically observed. The physical hypothesis is that massive seeding can, under specified conditions, invigorate clorld growth and thereby cause increased precipitation. The random variables compared are seeded and unseeded rain volume, measured by means of calibrated radar.