The Indian Institute of Technology (IIT) Kanpur announced on Wednesday that the planned cloud seeding in Delhi has been postponed because of “insufficient moisture in the clouds.” The decision came a day after two seeding operations failed to trigger rainfall over the national capital.

In an official statement, IIT Kanpur explained that successful cloud seeding depends entirely on suitable weather conditions. “The process is highly dependent on the right atmospheric conditions,” the institute said.

Tuesday’s Cloud Seeding Attempts Cost ₹1.28 Crore

On Tuesday, the Delhi government and IIT Kanpur jointly conducted two cloud seeding trials. According to reports, the operations cost around ₹1.28 crore in total.

The experiment aimed to induce artificial rain to reduce air pollution levels in the city. However, IIT Kanpur confirmed that both attempts were unsuccessful due to low moisture content.

“While rainfall could not be triggered yesterday because moisture levels were around 15 to 20%, the trial delivered valuable insights,” the institute said.

Failed Attempts Still Yield Positive Data

Despite the failed outcome, IIT Kanpur highlighted that the experiments were not a complete loss. The trials helped researchers collect real-time environmental data that showed a noticeable drop in pollution levels.

“Monitoring stations set up across Delhi captured real-time changes in particulate matter and moisture levels. The data shows a measurable reduction of 6 to 10 percent in PM2.5 and PM10 concentrations, indicating that even under limited moisture conditions, cloud seeding can contribute to improved air quality,” the institute stated.

IIT Kanpur Gained Key Insights for Future Operations

IIT Kanpur said the experience will help refine future cloud seeding operations. The data collected will help identify conditions where artificial rain can be most effective.

“These observations strengthen our planning for future operations and allow us to better identify conditions where this intervention can deliver maximum benefit. Such learnings form the foundation for more effective deployments ahead,” the statement added.