UAEREP Reviews Midterm Progress Of Cycle 5 Project On AI-Driven Cloud Seedability Assessment

(MENAFN- Mid-East Info) -p decoding="async" class="CToWUd" title="photo1" src="#" alt="photo1" width="620" data-bit="iit" />The Strategic Directions Committee (SDC) of the UAE Research Program for Rain Enhancement Science (UAEREP) conducted a midterm site visit to Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) to evaluate the progress of its Cycle 5 awardee project titled“Identification of Clouds' Microphysical Seedability in an Actionable Manner.”

The project, led by Professor Daniel Rosenfeld from the Hebrew University of Jerusalem (HUJI), is being implemented through a collaborative effort between 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 United States. It aims to develop a real-time, data-driven system for assessing cloud seedability at the scale of convective cloud clusters, using satellite and meteorological data, advanced modeling, and machine learning to guide seeding decisions and estimate potential impact.

The collaborative nature of the project, spanning institutions across four countries, demonstrates the importance UAEREP places on global partnerships in advancing impactful, data-driven solutions for rain enhancement. A key enabler of this collaboration is the comprehensive technical support and state-of-the-art facilities provided by NCM, facilitating continuous knowledge exchange and capacity building across borders to achieve long-term water security in the UAE and beyond.

His Excellency Dr. Abdulla Al Mandous, Director General of the National Center of Meteorology (NCM) and President of the World Meteorological Organization (WMO), said:“This project highlights UAEREP's commitment to advancing rain enhancement research through international collaboration. By bringing together leading institutions from across the globe, the program is driving a shared scientific vision through coordinated research efforts that accelerate the development of sustainable solutions to global water security challenges. This collaborative model not only strengthens the quality and impact of our research, but also reinforces the UAE's role as a global convener in addressing water security challenges through innovation and partnership.”

For her part, Alya Al Mazroui, Director of UAEREP, said:“The integration of AI and advanced modeling into cloud seedability assessment marks a transformative step in rain enhancement research. By leveraging satellite data, machine learning, and validated simulations, this project is developing a decision-support tool that enables near real-time evaluation of cloud systems. These innovations build upon the scientific foundations laid by previous UAEREP cycles, while also contributing to capacity building and the development a new generation of researchers in data-driven rain enhancement research.”

During the visit, the research team presented the project's key achievements including the completion of the first customized WRF-SBM cloud-scale simulation over the UAE, executed on the National Center of Meteorology's (NCM) supercomputer“Atmosphere”. This simulation provides critical input for the downstream development of an AI-powered Seedability Guidance Tool in collaboration with UCSD. The team also showcased progress in satellite image enhancement at MBZUAI, where super-resolution techniques are being applied to improve the quality of Meteosat geostationary satellite imagery, thereby enabling more accurate detection of seedable cloud features over the UAE.

In parallel, Wuhan University has developed satellite-based software capable of automating the sampling and visualization of cloud microphysical properties essential for seedability assessment. The project also demonstrated strong academic engagement, with graduate students and postdoctoral researchers actively contributing across HUJI, MBZUAI, WHU, and UCSD, helping to build a strong pipeline of early-career talent in the field of rain enhancement science.

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