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A new study has demonstrated that uncrewed aircraft systems (UAS) could make cloud-seeding operations more effective as it could improve the operational targeting of seeding material.
Representing a first implementation of an engineering approach utilising autonomous UAS technology for cloud-seeding programmes, a team led by professor Eric Frew of University of Colorado, Boulder — and the 3rd cycle awardee of the UAE Research Program for Rain Enhancement Science (UAEREP) — worked in collaboration with the National Centre of Meteorology on the feasibilty project.
The study noted that a key challenge when attempting to improve the effectiveness of cloud-seeding through the current standard practice involves identifying and utilising sub-cloud features in real-time to deliver the seeding material to the right time and location within the identified cloud.
Overcoming this challenge is crucial for maximising the operational and outreach benefits of cloud seeding while working within the constraints of nature.
The autonomous system design, implementation, and field deployment were based on a dispersed autonomy architecture that simultaneously incorporated sensors, algorithms, operators, and observers from around the world to search, identify, carry out, monitor, and evaluate cloud seeding operations to enhance precipitation.
The team conducted a 3-week long field campaign in the US Great Plains during August 2021 to demonstrate and validate the Concept of Operations (CONOPS) and autonomous system implementation.
They initially used a single-aircraft CONOPS to ensure the proper functioning and communication of all system components. After successfully completing the single-aircraft CONOPS, a two-aircraft CONOPS was deployed for tandem cloud measurements and seeding maneuvres.
A total of 9 flights on 8 different days were conducted, accumulating over 8 hours of flight time, including approximately three hours of multi-aircraft flights.
The dual UAS mission demonstrated the required level of capability in a typical rain enhancement seeding operational environment. Similarly, a single UAS successfully flew to a region of interest derived from remote sensing data, identified a seedable region, initiated the seeding maneuvre, and continued seeding while measuring seedable conditions and conducting evaluation maneuvres multiple times.
These results highlight the potential utility of UAS for cloud-seeding operations, positioning the technology at a readiness level between Prototype and Near-Operational environments. Another advantage compared to the current standard practice is the potential for improved targeting efficiency, leading to enhanced results with higher confidence in the seeding effect.
The technology transfer of the UAS and customised sensor payloads to the UAE's NCM was completed during the first quarter of 2023.
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