معرفی
Mohamed Iskandarani is a Professor in the Department of Ocean Sciences at the Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami. His research focuses on oceanographic modeling, data assimilation, and uncertainty quantification in geophysical fluid dynamics. He specializes in developing advanced numerical methods for climate and ocean circulation studies, including polynomial chaos frameworks, Gaussian process regression, and spectral element models. His work integrates machine learning techniques with traditional oceanography to improve predictions of submesoscale structures, storm surges, and hurricane impacts. Iskandarani has contributed to understanding Loop Current dynamics in the Gulf of Mexico, fish larval connectivity in Florida Keys, and the propagation of uncertainties in coupled atmosphere-wave-ocean systems.
Key research areas include: (1) High-resolution ocean velocity field reconstruction using Lagrangian drifter data, (2) Quantifying uncertainties in biophysical and climate models, (3) Developing statistical interpolation codes for ocean forecasting, and (4) Exploring multiscale ocean-atmosphere interactions. His methods are applied to real-world challenges like hurricane forecasting, oil spill trajectory modeling, and coastal engineering design criteria. Collaborations involve institutions such as NOAA, NASA, and international oceanographic consortia.
Published extensively in journals like Journal of Marine Science and Engineering and Monthly Weather Review, his work bridges computational science with environmental applications. Recent projects analyze submesoscale turbulence, drifter-based velocity field reconstructions, and ensemble-based statistical emulators for ocean current predictions.
