Advancements in Autonomous Unmanned Aircraft for Cloud Seeding: From Concept to Field Campaign

The pursuit of cloud seeding to enhance precipitation has sparked interest in accurately targeting convective cloud systems for seeding material deployment. While the theoretical foundation of cloud seeding emphasizes precise targeting, the practical implementation has been challenging. This paper presents a comprehensive overview of pioneering efforts to improve targeting by employing an autonomous vehicle as a seeding delivery method. The project had three different foci, firstly to develop the framework to navigate and control an autonomous unscrewed aircraft system, secondly to develop miniaturized in-situ sensing technology to provide real-time information to the vehicle on environmental conditions and include a seedabiliity algorithm, and lastly, a decision support system that ingests available satellite, radar and other information to navigate the vehicle to seeable areas. The study evaluates the success of manned aircraft operations guided by radar operators in identifying seedable convective elements by comparing flight tracks and radar analyses. Key findings underscore the potential for unmanned aircraft to locate suitable convective regions under radar guidance while highlighting the need for further refinement. The investigation also addresses the significance of accurate navigation and targeting for effective cloud seeding outcomes.

Additionally, the paper introduces the "Rapid Evaluation of Convective Cell Environments for Seeding" (RECCES) algorithm, a framework designed to navigate aircraft toward growing convective cells autonomously. RECCES integrates various data sources, including satellite observations, weather radar, and numerical models, to identify potential seeding targets and predict cell movement. This algorithm was validated through a field campaign in the central plains and evaluated in climatological scenarios in the United Arab Emirates. The study demonstrates the algorithm's sensitivity to aircraft operational parameters and highlights the need for tailored strategies for different aircraft types.

Overall, this paper contributes insights into the challenges of cloud seeding, advancements in autonomous navigation techniques, and the potential of leveraging diverse data sources for improved cloud targeting. The presented findings offer valuable guidance for optimizing cloud seeding projects and enhancing the effectiveness of precipitation enhancement initiatives.