Date of Award
December 2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Atmospheric Sciences
First Advisor
Marwa Majdi
Abstract
This study assesses the effectiveness of cloud seeding for hail suppression as part of the North Dakota Cloud Modification Project. Using a radar-based hail size retrieval algorithm, storm data from 2016-2018 were analyzed to compare observed hail sizes with forecasted values from atmospheric models and indices. Algorithm output is compared with model proximity sounding analysis including two hail-centric indices, five severe weather indices, and HAILCAST output to determine the forecasted hail size. Results show that the majority of unseeded cases have matching forecasted and observed hailstone diameter. In contrast, in seeded storms, observed hail sizes were consistently smaller than forecasted. The difference in false alarms between unseeded and seeded cases can be quantified as 20.8% of cases in which seeding was effective. These findings support the potential of cloud seeding to mitigate hail damage, enhance agricultural resilience, and justify its continued use in hail-prone regions like Western North Dakota.
Recommended Citation
Rosolino, Lynnlee Storm, "Analyzing The Effects Of Cloud Seeding On Hail Suppression During The North Dakota Cloud Modification Project" (2024). Theses and Dissertations. 6566.
https://commons.und.edu/theses/6566