An international research initiative supported by the UAE is employing artificial intelligence and sophisticated modeling to evaluate cloud seedability in almost real-time, with the goal of enhancing the precision and efficacy of rain enhancement. This venture is funded by the UAE Research Program for Rain Enhancement Science (UAEREP).
The Strategic Directions Committee (SDC) of UAEREP recently made a midterm visit to Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) to assess the progress of its Cycle 5 funded project, “Identification of Clouds’ Microphysical Seedability in an Actionable Manner.”
During the visit, the research team highlighted significant achievements, such as the successful completion of the first custom WRF-SBM cloud-scale simulation over the UAE, conducted on NCM’s supercomputer ‘Atmosphere’.
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Dr. Abdulla Al Mandous, Director General of NCM and President of the World Meteorological Organisation (WMO), stated: “By uniting leading institutions globally, the program advances a shared scientific vision through coordinated research efforts that accelerate the development of sustainable solutions to worldwide water security challenges.”
Alya Al Mazroui, Director of UAEREP, commented: “Integrating AI and advanced modeling into cloud seedability assessment represents a transformative leap in rain enhancement research. By utilizing satellite data, machine learning, and validated simulations, this project is crafting a decision-support tool for near real-time cloud system evaluation.”
Headed by Professor Daniel Rosenfeld from the Hebrew University of Jerusalem (HUJI), the project is being executed in collaboration with the National Center of Meteorology (NCM) and MBZUAI in the UAE, Wuhan University (WHU) in China, and the University of California San Diego (UCSD) in the USA.
The project aims to establish a real-time, data-driven system for evaluating cloud seedability at the scale of convective cloud clusters, employing satellite and meteorological data, advanced modeling, and machine learning to guide seeding decisions and assess potential impacts.
