Dr. Milija Zupanski - CIRA - Cooperative Institute for Research in the Atmosphere

Dr. Milija Zupanski

Research Scientist III

Office: ACRC Room 002
Phone: 970-491-8298
Fax: 970-491-8241
Email:

Mailing Address:

Dr. Milija Zupanski
Cooperative Institute for Research in the Atmosphere
Colorado State University
1375 Campus Delivery
Fort Collins, CO 80523-1375

Professional Website: http://www.cira.colostate.edu/projects/ensemble/

Biography

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, nonlinear and non-differentiable optimization and preconditioning, 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 (from microscale to global scales), climate, and carbon, and on development of non-differentiable minimization algorithms. Dr. Zupanski collaborates with research groups at Colorado State University, Florida State University, NOAA, NASA, as well as from other universities and federal agencies. Presently, Dr. Zupanski is a Research Scientist and leads CIRA efforts in ensemble data assimilation (Research web-page at http://www.cira.colostate.edu/projects/ensemble/).

Recent Work

MLEF as a non-differentiable minimization algorithm

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. The non-differentiable aspect of the MLEF algorithm is illustrated in an example with one-dimensional Burgers model and simulated observations. A comparison between the generalized non-differentiable CG and the standard differentiable CG methods is shown, in an example with cubic non-differentiable observation operator. Both the cost function and the gradient norm show a superior performance of the MLEF-based non-differentiable minimization algorithm. These results indicate important advantage of the MLEF for assimilation of cloud observations and processes., which are particularly challenging due to their discontinuous nature.

Above: The cost function (left panel) and the gradient norm (right panel) for non-differentiable CG (solid blue line) and for the standard, differentiable CG (dashed red line) method.

Selected Publications

Zupanski, M., 2008: Theoretical and Practical Issues of Ensemble Data Assimilation in Weather and Climate. Chapter in the book titled “Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications”, S. K. Park, Editor, Springer (in press).

Lokupitiya, R.S., D. Zupanski, A.S. Denning, S.R. Kawa, K.R. Gurney, M. Zupanski, W. Peters, 2008: Estimation of global CO₂ fluxes at regional scale using the maximum likelihood ensemble filter. J. Geophys. Res. (in press).

Zupanski, M., I. M. Navon, and D. Zupanski, 2008: The Maximum Likelihood Ensemble Filter as a non-differentiable minimization algorithm. Q. J. R. Meteorol. Soc., 134, 1039-1050.

Carrio, G.G., W.R. Cotton, D. Zupanski, and M. Zupanski, 2008: Development of an aerosol retrieval method: Description and preliminary tests. J. Appl. Meteor. Climate., DOI: 10.1175/2008JAMC1729.1.

Fletcher, S. J., and M. Zupanski, 2008: Implications and Impacts of Transforming Lognormal Variables into Normal Variables in VAR. Met. Zeit., 16, 755-765.

Fletcher, S.J., and M. Zupanski, 2008: A study of ensemble size and shallow water dynamics with the Maximum Likelihood Ensemble Filter. Tellus, 60A, 348-360.

Uzunoglu, B., S.J. Fletcher, I. M. Navon, and M. Zupanski, 2007: Adaptive Ensemble Size Reduction and Inflation. Q. J. R. Meteorol. Soc.,133, 1281-1294.

Zupanski, M., and I.M. Navon, 2007: Predictability, Observations, and Uncertainties in Geosciences. Bull. Amer. Meteor. Soc., 88, 1431-1433.

Zupanski, D., A.Y. Hou, S.Q. Zhang, M. Zupanski, C.D. Kummerow, and S. H. Cheung, 2007: Application of information theory in ensemble data assimilation. Q. J. R. Meteorol. Soc., 133, 1533-1545.

Zupanski, D., A. S. Denning, M. Uliasz, M. Zupanski, A. E. Schuh, P. J. Rayner, W. Peters and K. D. Corbin, 2007: Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF). J. Geophys. Res., 112, D17107, doi:10.1029/2006JK008371.

Fletcher, S. J., and M. Zupanski, 2006: A Hybrid Normal and Lognormal Distribution for Data Assimilation. Atmos. Sci. Lett., 7, 43-46.

Fletcher, S. J., and M. Zupanski, 2006: A Data Assimilation Method for Log-normally Distributed Observational Errors. Q. J. Roy. Meteorol. Soc., 132, 2505-2519.

Zupanski, M., S.J. Fletcher, I.M. Navon, B. Uzunoglu, R.P. Heikes, D.A. Randall, T.D. Ringler, and D. Daescu, 2006: Initiation of Ensemble Data Assimilation. Tellus, 58A, 159-170.

Zupanski, D. and M. Zupanski, 2006: Model Error Estimation Employing Ensemble Data Assimilation Approach. Mon. Wea. Rev., 134, 1337-1354.

Zupanski, M., 2005: Maximum Likelihood Ensemble Filter: Theoretical Aspects. Mon. Wea. Rev., 133, 1710–1726.

Zupanski, M., D. Zupanski, T. Vukicevic, K. Eis, and T. Vonder Haar, 2005: CIRA/CSU four-dimensional variational data assimilation system. Mon. Wea. Rev., 133, 829-843.

Zupanski, M., D. Zupanski, D. Parrish, E. Rogers, and G. DiMego, 2002: Four-dimensional variational data assimilation for the Blizzard of 2000. Mon. Wea. Rev., 130, 1967-1988.

General Themes