Dr. Milija Zupanski
Dr. Milija Zupanski
Senior Research Scientist
Office: CIRA Room 113
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
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 Senior Research Scientist and CIRA Fellow. He 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.

Figure

 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

Carrassi, A., S. Vannitsem, D. Zupanski, and M. Zupanski, 2009: The Maximum Likelihood Ensemble Filter Performances in Chaotic Systems. Tellus, 61A, 587-600.

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 Observation Errors. Quart. J. Roy. Meteorol. Soc., 132, 2505-2519.

Jardak, M., I. M. Navon, and M. Zupanski, 2009: Comparison of Sequential Data Assimilation Methods for the Kuramoto-Sivashinsky Equation. Int. J. Num. Methods Fluids, 62, 374-402.

Kim, H. H., S. K. Park, D. Zupanski, and M. Zupanski, 2010: Uncertainty Analysis Using the WRF Maximum Likelihood Ensemble Filter System and Comparison with Dropwindsonde Observations in Typhoon Sinlaku (2008). Asia-Pacific J. Atmos. Sci., 46, 317-325.

Orescanin, B., B. Rajkovic, M. Zupanski, and D. Zupanski, 2009: Soil Model Parameter Estimation with Ensemble Data Assimilation. Atmos. Sci. Letters, 10, 127-131.

Sasaki, Y. K., and M. Zupanski, 1992: A Mechanism of Alpine Lee Cyclogenesis as Revealed by a Quasigeostrophic Variational Filter. Meteorol. Atmos. Phys., 47, 91-105.

Steward, J. L., I. M. Navon, M. Zupanski, and N. Karmitsa, 2011: Impact of Non-Smooth Observation Operators on Variational and Sequential Data Assimilation for a Limited-Area Shallow-Water Equation Model. Quart. J. Roy. Meteorol. Soc., DOI: 10.1002/qj.935.

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

Zupanski, M., 1993: A Preconditioning Algorithm for Large Scale Minimization Problems. Tellus, 45A, 578-592.

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.

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

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

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. Quart. J. Roy. Meteorol. Soc., 133, 1533-1545.

Zupanski, M., I. M. Navon, and D. Zupanski, 2008: The Maximum Likelihood Ensemble Filter as a Non-Differentiable Minimization Algrotihm. Quart. J. Roy. Meteorol. Soc., 134, 1039-1050.

Zupanski D., M. Zupanski, L. D. Grasso, R. Brummer, I. Jankov, D. Lindsey and M. Sengupta, and M. DeMaria, 2011: Assimilating synthetic GOES-R radiances in cloudy conditions using an ensemble-based method. Int. J. Remote Sensing, 32, 9637-9659. 

Zupanski, D., S. Q. Zhang, M. Zupanski, A. Y. Hou, and S. H. Cheung, 2011: A prototype WRF-based ensemble data assimilation system for downscaling satellite precipitation observations. J. Hydromet., 12, 118-134.

 

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