Enhancing Weather Downscaling and Forecasting


Project Description

Global weather products can only be computed at coarse resolution, and therefore cannot resolve important sub-grid scale features such as clouds and topography. Downscaling methods are used to compute local weather forecasts at high resolution from the global products. Nudging and Spectral Nudging methods are popular techniques for constraining local models with global products. The goal of the internship is to explore and test more advanced downscaling techniques based on the recently developed continuous data assimilation framework and/or the ensemble Kalman filter.​​​​​
Program - Earth Science and Engineering
Division - Physical Sciences and Engineering
Field of Study - ​Applied Mathematics, Meteorology, or any related field​

About the

Ibrahim Hoteit

Professor, Earth Science and Engineering

Ibrahim Hoteit

My 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. I am currently focusing on developing an integrated data-driven modeling system to simulate and predict the circulation and the climate of the Saudi marginal seas: the Red Sea and the Arabian Gulf.