Numerical Analysis of Statistical Power in Precipitation Enhancement Experiment in Beijing and Estimation of Operational Cloud Seeding Effectiveness

The naive re-randomization test is used for numerical analysis of the statistical power in a precipitation enhancement experiment in Beijing. The results show that the statistical powers vary depending on the evaluation method; the sequence method provides the worst results. For a large number of samples, the comparison method has the highest statistical power, followed by the historical regression method and double ratio method. For a small number of samples, the historical regression method has a higher statistical power than the other two methods. The statistical power is strongly correlated with the precipitation enhancement effectiveness, historical samples, and operating samples. The statistical power increases with increasing number of historical or operating samples, but the extent of the increase is reduced as the number of units increases. Classification will not always improve the detection efficiency; the statistical power of the post-classification sample size must be greater than that of the combined sample size if the classification method is used. Here, the comparison, double ratio, and historical regression methods are used to evaluate the precipitation enhancement effectiveness in Beijing from 2002 to 2007; the results of all three methods show that the relative effectiveness is about 10% at a significance of 0.05.