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).