Naila Raboudi

PhD Student

PhD Student


Research Interests

​Naila's research involves developing and applying Bayesian data assimilation schemes for large dimensional state and parameter estimation problems. She has derived new ensemble Kalman filter (EnKF)-based methods from the One-Step-Ahead (OSA) smoothing formulation of the Bayesian filtering problem for improved data assimilation into large scale systems, and extend it to one-way coupled systems and for model bias estimation. Naila has successfully applied a Hybrid version of the EnOF-OSA for forecasting storm surge in the Gulf of Mexico using the Advanced Circulation (ADCIRC) model. She is currently focusing on implementing the developed schemes within the Data Research Testbed (DART) to test them with a high resolution MIT general circulation model (MITgcm) of the Red Sea.

Selected Publications

  • ​Ensemble Kalman filtering with one-step-ahead smoothing
    N.F. Raboudi, B. Ait-El-Fquih, I. Hoteit
    Monthly Weather Review, 146, 561--581, 2018


  • M.Sc., Earth Science and Engineering, KAUST, Thuwal, Saudi Arabia, 2016
  • Engineering Diploma from Tunisia Polytechnic School (TPS), Tunisia, 2014

Scientific and Professional Membership

  • ​Asia Oceania Geosciences Society (AOGS)

Research Interests Keywords

Data assimilation Ensemble Kalman filtering (EnKF) Ocean forecasting