The Red Sea is our backyard and "a tout d'une grande". We are working on studying all aspects of its variability and its role in the global climate using state-of-the-art and developing next-generation tools.

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Red Sea Modeling and Prediction Group

Ibrahim Hoteit is an associate professor in the Earth Sciences and Engineering program and the Applied Mathematics and Computational Sciences program at KAUST. He is currently the lead of the Virtual Red Sea Initiative, a joint initiative with MIT/SIO/PML.

Education and early career

Professor Hoteit earned his M.S. (1998) and Ph.D. (2002) in Applied Mathematics from the University of Joseph Fourier, France. Prior to joining KAUST in 2009, he worked as a Research Scientist at Scripps Institution of Oceanography of the University of California, San Diego.

Areas of expertise and current scientific interests

Professor Hoteit's research involves the effective use and integration of dynamical models and observations to simulate, study and predict realistic geophysical fluid systems. This involves developing and implementing numerical models and data inversion, assimilation, and uncertainty quantification techniques suitable for large scale applications. He is currently focusing on developing an integrated data-driven modeling system to study and predict the circulation and the climate of the Saudi marginal seas: the Red Sea and the Arabian Gulf, and to understand their impact of the ecosystem productivity.

Editorial activities

Professor Hoteit co-authored more than 170 scientific papers and is a co-recipient of five best conference paper awards. He is serving as associate editor of Plos One, Computational Geosciences, and Atmospheric Science Letters, and 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.

Why modeling of oceanic and atmospheric systems?

I enjoy working on challenging real-world problems with very important impacts on the population and the environment. Understanding how the environment functions and predicting its future is a great source of motivation for me.

Why KAUST?

Modeling and forecasting the ocean and atmosphere circulation and climate is the example of multidisciplinary research and of course requires great resources and supercomputing capabilities, and a smart hard-working team and group of collaborators. KAUST is one of the best places I know off to offer all these to allow me to conduct this research.

Education Profile

  • Ph.D., Université Joseph Fourrier, 1998 - 2002
  • M.S., Université Joseph Fourrier, 1997 - 1998

Awards and Distinctions

  • Honorable mention (top five papers), PacificVis Symposium, Syndey, Australia, 2013
  • Best poster award at the SIAM conference on Mathematical and Computational Issues in Earth Sciences, Long Beach, USA, 2011
  • Best poster award at the Richard Tapia Celebration of Diversity in Computing Conference, San Francisco, USA, 2011
  • Ph.D. Fellowship Award from the French Government

Selected Publications

G. Krokos, V. Papadopoulos, S. Sarantis, H. Ombao, P. Dybczak, and I. Hoteit: Natural Climate Oscillations may counter Red Sea warming over the coming decades. Geophysical Research Letters, doi:10.1029/2018GL081387, 2019.
DOI
10.1016/j.bspc.2018.08.023
Altmetrics
Siripatana, A., Mayo, T., Knio, O., Dawson, C., Maître, O. L., & Hoteit, I. (2018). Ensemble Kalman filter inference of spatially-varying Manning’s n coefficients in the coastal ocean. Journal of Hydrology, 562, 664–684. doi:10.1016/j.jhydrol.2018.05.021
Handle
10754/627919
DOI
10.1016/j.jhydrol.2018.05.021
Altmetrics
Toye, H., Kortas, S., Zhan, P., & Hoteit, I. (2018). A fault-tolerant HPC scheduler extension for large and operational ensemble data assimilation: Application to the Red Sea. Journal of Computational Science, 27, 46–56. doi:10.1016/j.jocs.2018.04.018
Handle
10754/627684
DOI
10.1016/j.jocs.2018.04.018
Altmetrics
Giraldi, L., Le Maître, O. P., Hoteit, I., & Knio, O. M. (2018). Optimal projection of observations in a Bayesian setting. Computational Statistics & Data Analysis, 124, 252–276. doi:10.1016/j.csda.2018.03.002
Handle
10754/627354
DOI
10.1016/j.csda.2018.03.002
Altmetrics
Kumar Jain, P., Mandli, K., Hoteit, I., Knio, O., & Dawson, C. (2018). Dynamically adaptive data-driven simulation of extreme hydrological flows. Ocean Modelling, 122, 85–103. doi:10.1016/j.ocemod.2017.12.004
Handle
10754/626823
DOI
10.1016/j.ocemod.2017.12.004
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Publications

Publications list on ORCID
Also view list of Publications on KAUST Repository