Ibrahim Hoteit

KAUST

Biography

Ibrahim Hoteit is a professor of Earth Sciences and Engineering and affiliated with the Applied Mathematics and Computational Sciences program at KAUST. He is currently leading the Virtual Red Sea Initiative, a joint initiative with Scripps Institution of Oceanography, MIT, NCAR and Plymouth Marine Laboratory, and serving as the director of the Climate Change Center and the Saudi Aramco Marine Environment Research Center at KAUST. Dr. Hoteit's research interests focus on the modeling of oceanic and atmospheric systems on supercomputers and the analysis of their circulation and variability, with specific interest in data assimilation and uncertainty quantification for large-scale systems. Dr. Hoteit co-authored more than 300 papers and was awarded the prestigious Kuwait Prize in Basic sciences. He is serving as associate editor of Plos One, Computational Geosciences, Mathematics of Climate and Weather Forecasting, and Atmospheric Science Letters. He is a member of the American and European Geophysical Unions, the Society of Industrial and Applied Mathematics, and an elected member of the UNESCO Center of Pure and Applied Mathematics. Dr. Hoteit earned his M.S. (1998) and Ph.D. (2002) in applied mathematics from the University of Joseph Fourier, France.

All sessions by Ibrahim Hoteit

On the development of mapping and forecasting capabilities of solar energy resources for the Kingdom
01:00 PM

This talk will present our efforts to develop mapping and real-time forecasting capabilities of solar energy resources for the Kingdom. Building on our regional high-resolution data-driven atmospheric modeling system and its long-term climatology product that we have developed at KAUST as part of the Virtual Red Sea Initiative, we are exploring the most efficient approaches, in terms of computing cost, data requirements and performances, for solar energy resources mapping and forecasting making use of all available information from in-situ and satellite observations, and physics and numerics. We are currently investigating the best combinations of the computationally demanding regional general circulation atmospheric models and the much cheaper but data-dependent Machine Learning models. I will discuss the current status of these ongoing efforts and showcase the supporting real-time online visualization-analytics tools that we are developing to provide user-friendly access to the large datasets that are outputted by the systems.

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