About Habib Toye Mahamadou Kele Habib Toye Mahamadou Kele Ph.D. Student, Applied Mathematics and Computational Science Ensemble ocean forecasting ocean forecasting High Performance Computing data assimilation Brief Biography Habib Toye is a PhD candidate at King Abdullah University of Science and Technology (KAUST), in the Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE) under the supervision of Professor Ibrahim Hoteit. His research interests include Ensemble ocean forecasting, High performance computing and Data assimilation. Research Interest Habib's research focuses on developing and implementing efficient ensemble Kalman filters for data assimilation into a high resolution model of the Red Sea. He has developed a fault-free DART-MITgcm ensemble assimilation Events Presented Events Oct 11 - Oct 17, 2020 Efficient Ensemble Data Assimilation and Forecasting of the Red Sea Circulation Habib Toye Mahamadou Kele, Ph.D. Student, Applied Mathematics and Computational Science Oct 13, 16:15 - 17:00 KAUST This dissertation presents our efforts to build an operational ensemble forecasting system for the Red Sea, based on the Data Research Testbed (DART) package for ensemble data assimilation and the Massachusetts Institute of Technology general circulation ocean model (MITgcm) for forecasting. The Red Sea DART-MITgcm system efficiently integrates all the ensemble members in parallel, while accommodating different ensemble assimilation schemes. The promising ensemble adjustment Kalman filter (EAKF), designed to avoid manipulating the gigantic covariance matrices involved in the ensemble assimilation process, possesses relevant features required for an operational setting. We developed new schemes aiming at lowering the computational burden while preserving reliable assimilation results. The ensemble data assimilation system is implemented and tested on Shaheen, our world-class supercomputer, and will form the basis of the first ever operational Red Sea forecasting system that is currently being implemented to support Saudi Aramco operations in this basin.
Efficient Ensemble Data Assimilation and Forecasting of the Red Sea Circulation Habib Toye Mahamadou Kele, Ph.D. Student, Applied Mathematics and Computational Science Oct 13, 16:15 - 17:00 KAUST This dissertation presents our efforts to build an operational ensemble forecasting system for the Red Sea, based on the Data Research Testbed (DART) package for ensemble data assimilation and the Massachusetts Institute of Technology general circulation ocean model (MITgcm) for forecasting. The Red Sea DART-MITgcm system efficiently integrates all the ensemble members in parallel, while accommodating different ensemble assimilation schemes. The promising ensemble adjustment Kalman filter (EAKF), designed to avoid manipulating the gigantic covariance matrices involved in the ensemble assimilation process, possesses relevant features required for an operational setting. We developed new schemes aiming at lowering the computational burden while preserving reliable assimilation results. The ensemble data assimilation system is implemented and tested on Shaheen, our world-class supercomputer, and will form the basis of the first ever operational Red Sea forecasting system that is currently being implemented to support Saudi Aramco operations in this basin.
Related Sites Red Sea Modeling and Prediction Group (ASSIMILATION) Applied Mathematics and Computational Science (AMCS) Related Content Events 1
