Unmanned aircraft systems for precipitation enhancement: Advancements, challenges, and future prospects.
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| Title: | Unmanned aircraft systems for precipitation enhancement: Advancements, challenges, and future prospects. |
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| Authors: | Kazim, Muhammad1 (AUTHOR) muhammad.kazim@ku.ac.ae, Azzam, Rana1 (AUTHOR), Burger, Roelof2 (AUTHOR), Wehbe, Youssef3 (AUTHOR), Zweiri, Yahya1,4 (AUTHOR), Seneviratne, Lakmal1 (AUTHOR), Werghi, Naoufel1,5 (AUTHOR) |
| Source: | Atmospheric Research. Jan2026, Vol. 327, pN.PAG-N.PAG. 1p. |
| Subjects: | Drone aircraft, Rain-making, Multisensor data fusion, Robotics, Meteorological databases, Legal compliance, Precipitation (Chemistry) |
| Geographic Terms: | United Arab Emirates |
| Abstract: | The increasing demand for freshwater resources has intensified interest in precipitation enhancement technologies, particularly in arid and semi-arid regions such as the United Arab Emirates (UAE). Traditional cloud seeding methods that utilize crewed aircrafts have limitations in terms of safety, cost, and operational flexibility. Unmanned Aircraft Systems (UAS) offer promising complementary platforms by providing enhanced maneuverability, reduced risks, and cost-effectiveness. Furthermore, UAS can gather in situ high-resolution atmospheric data that are currently inaccessible via remote sensing techniques or crewed aircraft. This article provides a detailed introduction, history of UAS usage, and recent advancements in small UAS for cloud seeding, as well as presents the impacts, challenges, and future prospects. It then examines the different types of aircraft used for cloud seeding and discusses their specifications, functions, and sensors, as well as their pros and cons. Subsequently, it provides a comprehensive analysis of the small UAS used for precipitation enhancement, focusing on the types of small UAS suitable for cloud seeding, onboard equipment and sensors, ground station equipment and software, communication protocols, and methodologies, such as cloud seedability algorithms (CSA), path planning, and control autonomy. The integration of the Rapid Evaluation of Convective Cell Environments for Seeding (RECCES) algorithm built on top of the Lidar Radar Open Software Environment (LROSE) is also discussed for real-time cloud data acquisition and analysis, which are important for UAS cloud seeding missions. Advanced control-and-command communication systems between the UAS and ground stations are discussed, along with the regulatory requirements of the UAS for cloud seeding. The advantages, advancements, and challenges of UAS in precipitation enhancement are highlighted, and future prospects such as swarm technology, advanced sensor integration, centralized autonomy, de-icing technology, Artificial Intelligence (AI) and Machine Learning, regulatory framework development, and environmental and ethical considerations are proposed in detail to pave the way for more efficient and effective UAS-based precipitation enhancement for cloud seeding operations. • This paper reviews the evolution of Unmanned Aircraft Systems (UAS) in cloud seeding, from early trials to advanced autonomous technologies, and analyzes key historical milestones and innovations in seeding methodologies. • Critically compares cloud seeding aircraft and small UAS platforms, evaluating specifications, sensor integrations, performance, and operational efficiency. • Examines UAS-mounted sensors for high-resolution atmospheric data collection and reviews advanced path planning, estimation, and autonomous control methods that improve cloud seeding precision. • Analyzes ground station and communication system advancements enabling autonomous dispersal and real-time data integration, alongside software frameworks for dynamic cloud data acquisition and analysis. • Highlights key challenges, regulatory limits, flight endurance, and sensor miniaturization outlining future directions and synthesizing progress and research needs in UAS-driven precipitation enhancement. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
| Abstract: | The increasing demand for freshwater resources has intensified interest in precipitation enhancement technologies, particularly in arid and semi-arid regions such as the United Arab Emirates (UAE). Traditional cloud seeding methods that utilize crewed aircrafts have limitations in terms of safety, cost, and operational flexibility. Unmanned Aircraft Systems (UAS) offer promising complementary platforms by providing enhanced maneuverability, reduced risks, and cost-effectiveness. Furthermore, UAS can gather in situ high-resolution atmospheric data that are currently inaccessible via remote sensing techniques or crewed aircraft. This article provides a detailed introduction, history of UAS usage, and recent advancements in small UAS for cloud seeding, as well as presents the impacts, challenges, and future prospects. It then examines the different types of aircraft used for cloud seeding and discusses their specifications, functions, and sensors, as well as their pros and cons. Subsequently, it provides a comprehensive analysis of the small UAS used for precipitation enhancement, focusing on the types of small UAS suitable for cloud seeding, onboard equipment and sensors, ground station equipment and software, communication protocols, and methodologies, such as cloud seedability algorithms (CSA), path planning, and control autonomy. The integration of the Rapid Evaluation of Convective Cell Environments for Seeding (RECCES) algorithm built on top of the Lidar Radar Open Software Environment (LROSE) is also discussed for real-time cloud data acquisition and analysis, which are important for UAS cloud seeding missions. Advanced control-and-command communication systems between the UAS and ground stations are discussed, along with the regulatory requirements of the UAS for cloud seeding. The advantages, advancements, and challenges of UAS in precipitation enhancement are highlighted, and future prospects such as swarm technology, advanced sensor integration, centralized autonomy, de-icing technology, Artificial Intelligence (AI) and Machine Learning, regulatory framework development, and environmental and ethical considerations are proposed in detail to pave the way for more efficient and effective UAS-based precipitation enhancement for cloud seeding operations. • This paper reviews the evolution of Unmanned Aircraft Systems (UAS) in cloud seeding, from early trials to advanced autonomous technologies, and analyzes key historical milestones and innovations in seeding methodologies. • Critically compares cloud seeding aircraft and small UAS platforms, evaluating specifications, sensor integrations, performance, and operational efficiency. • Examines UAS-mounted sensors for high-resolution atmospheric data collection and reviews advanced path planning, estimation, and autonomous control methods that improve cloud seeding precision. • Analyzes ground station and communication system advancements enabling autonomous dispersal and real-time data integration, alongside software frameworks for dynamic cloud data acquisition and analysis. • Highlights key challenges, regulatory limits, flight endurance, and sensor miniaturization outlining future directions and synthesizing progress and research needs in UAS-driven precipitation enhancement. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01698095 |
| DOI: | 10.1016/j.atmosres.2025.108333 |
