Using Synthetic Control to Assess the Climatological Significance of Cloud Seeding in the Western US with a Structured Data Set of Reported Activities from 2000 – 2025

Since 1972, the US Weather Modification Reporting Act has required federal reporting of any cloud seeding activities taking place in the United States to NOAA. Leveraging these historical records, we used OpenAI’s o3 large language model to extract information about the project name, year, season, state, operator, seeding agent, apparatus used for deployment, stated purpose, target area, control area, start date and end data of all publicly reported cloud seeding activities (Donohue and Lamb, 2025). This method was validated through the performance of the data extraction pipeline on a manually labeled subset of 200 records, achieving an average accuracy of 98.38% across all fields. This structured data set, encompassing 832 distinct operational periods from 2000 – 2025, represents the first large-scale, publicly available data set of cloud seeding activities for the US that can facilitate large-scale historical analysis.

Using this data set, we use synthetic control, a statistical method to estimate the effect of an intervention, to understand whether cloud seeding is an effective strategy for augmenting snowpack and precipitation from a climatological perspective. Using the reported locations and times of historical cloud seeding operations, along with historical datasets of precipitation and snowpack, we analyze whether cloud seeding can have a climatologically significant impact in augmenting precipitation and snowpack regionally. Our approach makes it possible to rigorously evaluate cloud seeding effectiveness on a climatological scale across the US for the first time.