The goal of this project is to explore new directions for developing advanced data assimilation techniques with focus on high dimensional and strongly nonlinear models. We are interested in both the deterministic variational and the stochastic Bayesian assimilation approaches.

The goal of this project is to combine the dynamics of an eddy-resolving configuration of the MIT general circulation ocean model (MITgcm) with all available data in the Red Sea to determine the most accurate and complete estimates of the past and future circulation and variability of the Red Sea. We are using the outputs of these simulations to better understand the climate and the circulation of the Red Sea. ​​​

Prediction of coastal flooding due to hurricanes, tropical storms and tsunamis is a problem of international importance. We are working with Prof. Clint Dawson's group from the University of Texas at Austin on developing an advanced data assimilation system for predicting storm surge based on the ADCIRC model and ensemble Kalman filtering techniques.

We are working with Dr. George Triantafyllou from HCMR to develop a 3D coupled physical-biogeochemical model at fine-scale and with multiple depth layers to simulate the ecosystem of the Red Sea. The model will efficiently simulate the pathways of dissolved inorganic nutrients, the fate of particulate organic matter, and the variability of the living functional groups (phyto/zooplankton, bacteria, etc).​​​

The focus of this collaborative project between KAUST and the University of Texas A&M is to develop a unified Bayesian framework for inverse and data assimilation problems, with applications to estimation and optimization problems of ocean general circulation model in mind. ​

We are working with Dr. Johan Valstar from Deltares (Netherlands) on developing an ensemble Kalman-based data assimilation system for management of groundwater contamination. We aim at assimilating any available data to a coupled subsurface flow and contaminant transport model. Another goal of the project is to define an efficient strategy for optimizing the design of an observational system.​​​​​​​

The goal of this project is to develop efficient and fully nonlinear Bayesian filters capable of assimilating all available reservoir data to monitor and manage the state of complex reservoirs. We are focusing on assimilation of seismic data, but our long-term goal is to utilize all available reservoir data, including wells ata, EM data, remote sensing data, etc

​The objective of the project is to advance the ability of climate scientists and oceanographers to quantify uncertainties stemming from parameterizations of highly non-linear phenomena. In particular, we are working on developing an innovative strategy for quantifying uncertainties and improving the skill of the KPP (“K profile parameterization”) that is used to represent vertical mixing processes within surface boundary layer of the ocean (Large et al., 1994). ​​

Using 30 years of satellite remotely sensed Sea Surface Temperature (AVHRR) and ocean colour (CZCS & SeaWiFS) data, we are looking for evidence of intense warming and its potential impact on the Red Sea biology (phytoplankton and fisheries).​​​​