Abstract: | The increasing demand for freshwater resources in arid regions such as the United Arab Emirates necessitates innovative approaches to precipitation enhancement. Traditional cloud seeding methods using crewed aircraft are limited by safety concerns, high operational costs, and inflexibility. This research proposes a solution that integrates advanced electric Vertical Take-Off and Landing (eVTOL) Unmanned Aerial Systems (UAS) equipped with advanced meteorological and avionics sensors, artificial intelligence (AI), and precipitation enhancement algorithms, to revolutionize precipitation enhancement operations.
Our eVTOL UAS is equipped with an array of cutting-edge meteorological sensors, including Cloud Droplet Probe (CDP), Cloud Aerosol Spectrometer (CAS), Printed Optical Particle Spectrometer (POPS), Multi-Angle Imaging SpectroRadiometer (MIPS), Precipitation Imaging Probe (PIP), Cloud Imaging Probe (CIP), and a Backscatter Cloud Probe with Polarization Detection (BCPD). A high-resolution multi-spectral camera is also integrated to detect detailed cloud features and characteristics. A critical aspect of the eVTOL UAS design is a comprehensive de-icing system that prevents ice buildup on the propeller, and leading edges, ensuring stability and performance in freezing conditions prevalent at high altitudes. We have test the eVTOL in China and the specifications and parameters are given at https://github.com/Kazimbalti/eVTOL_Cloud_Seeding/tree/main .
We will employ the Real-Time Evaluation and Collaborative Control of Environmental Systems (RECCES) algorithm combined with the Lidar Radar Open Software Environment (LROSE) to acquire real-time cloud data from weather radar within five minutes. Data from rain gauges, numerical weather prediction models, historical records, and the eVTOL UAS's sensors data are aggregate or integrate. Using AI and computer vision techniques, we will process this multifaceted data to detect, estimate, and extract precipitation enhancement (cloud seeding) features and characteristics. The AI-driven system determines optimal cloud seeding times, locations, and flight paths, which are transmitted in real-time to the eVTOL UAS via a 900 MHz datalink. The UAS's onboard computer processes this information using a built-in cloud seedability algorithm, path-following, and control methodology. This enables the UAS to autonomously navigate and accurately deploy seeding materials, enhancing precipitation efficiency.
In future developments, we plan to combine cloud seeding with cloud zapping technology, by utilizing our eVTOL UAS and materials entirely designed within the UAE. Cloud zapping involves the use of directed energy beams, such as high-powered lasers or microwave pulses, to ionize atmospheric particles, inducing cloud formation and precipitation. By integrating this capability with our advanced eVTOL UAS technology, AI-based data analysis, and real-time operational control, we aim to set a new benchmark in precipitation enhancement techniques. This approach will enable more precise and controlled atmospheric interventions, significantly contributing to precipitation enhancement in the world especially in the UAE. |
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