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Ibrahim Hoteit
Researcher at King Abdullah University of Science and Technology
Publications - 373
Citations - 7598
Ibrahim Hoteit is an academic researcher from King Abdullah University of Science and Technology. The author has contributed to research in topics: Data assimilation & Ensemble Kalman filter. The author has an hindex of 39, co-authored 316 publications receiving 5869 citations. Previous affiliations of Ibrahim Hoteit include Scripps Institution of Oceanography & University of California, San Diego.
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Modelling the spatial and temporal variability of the Cretan Sea ecosystem
TL;DR: In this paper, the authors explored the ecosystem function of the oligotrophic Cretan Sea through the development and application of a 3D ecological model, which comprises of two on-line coupled submodels: the 3D Princeton Ocean Model (POM) and the 1D European Regional Seas Ecosystem Model (ERSEM) adapted to the Cretin Sea.
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Multiple stressor effects on coral reef ecosystems
Joanne I. Ellis,Joanne I. Ellis,Tahira Jamil,Holger Anlauf,Darren J. Coker,João Cúrdia,Judi E. Hewitt,Burton H. Jones,George Krokos,Benjamin Kürten,Dasari Hariprasad,Florian Roth,Susana Carvalho,Ibrahim Hoteit +13 more
TL;DR: These synergistic interactions suggest that the negative effects of fishing pressure and eutrophication may exacerbate the impact of climate change on corals, and whether managing local stressors, such as nutrient enrichment and fishing activities, may help mitigate global drivers of change.
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Estimating model-error covariances in nonlinear state-space models using Kalman smoothing and the expectation–maximization algorithm
Denis Dreano,Pierre Tandeo,Manuel Pulido,Boujemaa Ait-El-Fquih,Thierry Chonavel,Ibrahim Hoteit +5 more
TL;DR: This work proposes an iterative expectation–maximization (EM) algorithm to estimate the model‐error covariances using classical extended and ensemble versions of the Kalman smoother and shows that, for additive model errors, the estimate of the error covariance converges.
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The eddy kinetic energy budget in the Red Sea
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Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKF
TL;DR: In this paper, an efficient serial scheme is proposed to mitigate the impact of observations' perturbations sampling in the analysis step of the EnKF, which should provide more accurate ensemble estimates of the analysis error covariance matrices.