Dr. Milija Zupanski

Senior Research Scientist/Scholar
Fort Collins, Colorado


Milija Zupanski received his BSc in Meteorology from University of Belgrade, Serbia, and MS (1987) and PhD (1990) in Meteorology from the University of Oklahoma, with Prof. Yoshi Sasaki as advisor. His area of interest include ensemble data assimilation, hybrid variatonal-ensemble data assimilation, nonlinear and non-differentiable optimization and preconditioning, coupled data assimilation, non-Gaussian probability assumptions, predictability and chaos theory, and applied mathematics focusing on weather and climate.
He worked at the NOAA National Centers for Environmental Prediction (NCEP) from 1990-2001, where he was a principal developer of the four-dimensional variational (4D-Var) data assimilation with then operational Eta model. In 2001 Dr. Zupanski joined CIRA, where he was one of the principal developers of the 4D-Var with RAMS model, and is the principal developer of the Maximum Likelihood Ensemble Filter (MLEF) - an ensemble assimilation/prediction system with estimation of uncertainties. In recent years his work is focusing on various applications of the MLEF to weather and climate, and to coupled data assimilation. Dr. Zupanski collaborates with research groups at Colorado State University, NOAA, NASA, DOD, as well as from U.S. and international Universities and Research Labs.
Presently, Dr. Zupanski is a Senior Research Scientist and CIRA Fellow. He is the Lead of CIRA Data Assimilation research group (research web-page at http://da.cira.colostate.edu).

Recent Work

March 5, 2014

The MLEF can be derived without common differentiability and linearity assumptions (Zupanski et al. 2008). As a consequence, non-differentiable minimization algorithms can be derived as generalization of gradient-based methods, such as the nonlinear conjugate gradient (CG) and quasi-Newton (QN) methods. ...