The efficacy of projects intended to mitigate the severity of hailstorms remains indeterminate. Statistical assessments of certain operational projects indicate successful reduction of crop hail damage, but scientific establishment of cause and effect are incomplete. Results of various operational and experimental projects provide a range of outcomes. Some suggest decreases in hailfall, but others have produced inconclusive results, and some suggest increases. Given the diversity of conceptual models, cloud seeding criteria, seeding agents, delivery techniques, assessment methods, and the storms themselves, this is not unexpected. It is a direct reflection of storm complexity as well as the spatial and temporal variability of hail.
Statistical evaluations using hail characteristics (i.e., kinetic energy, hailstone size, and area of hailfall) have often yielded inconclusive or inconsistent results. Historic trends in crop hail damage have been used to evaluate many operational programs, but these data can be unreliable and so must be used cautiously.
Our understanding of hailstorms is not yet sufficient to allow confident prediction of the effects of seeding individual storms, and the most appropriate seeding methodology has not been determined. The possibility of increasing or decreasing both hail and rain in some circumstances is recognized, but numerical cloud models have recently affirmed that the desirable outcome, that is, a decrease in hail and an increase in rain, is possible.
Hail results in significant economic losses worldwide; thus, research on hail suppression continues. As with precipitation augmentation efforts, increased in situ observations, remote sensing (e.g., multiparameter radar), and numerical cloud modeling capabilities continue to improve our understanding of hailstorms as a foundation for more effective scientific endeavors to suppress hail.