Christman Field Latest Observations
Date Time
MST
Temp
°F
RH
%
DewPt
°F
Wind
mph
Dir
°
Gust
mph
Dir
°
Press
in Hg
Solar
W/m^2
Prec
in
2019-11-12 06:00 9.8 88.6 7.2 0.7 166 3.2 166 24.991 0.0 0.00
2019-11-12 05:55 10.1 89.3 7.6 0.3 240 1.7 333 24.992 0.0 0.00
2019-11-12 05:50 11.6 86.3 8.3 0.7 352 2.1 352 24.993 0.0 0.00
2019-11-12 05:45 13.2 88.8 10.5 0.5 352 1.5 352 24.994 0.0 0.00
2019-11-12 05:40 13.0 92.0 11.1 0.5 352 1.6 351 24.995 0.0 0.00
2019-11-12 05:35 11.9 92.8 10.2 1.1 351 2.5 352 24.995 0.0 0.00
2019-11-12 05:30 9.8 91.6 7.9 3.1 352 4.5 340 24.996 0.0 0.00
2019-11-12 05:25 8.8 89.4 6.3 3.2 340 3.8 348 24.996 0.0 0.00
2019-11-12 05:20 11.5 84.2 7.6 3.2 5 4.2 5 24.999 0.0 0.00
2019-11-12 05:15 12.1 92.6 10.3 0.3 323 2.1 322 24.999 0.0 0.00
2019-11-12 05:10 10.8 93.8 9.4 0.2 236 1.8 236 25.002 0.0 0.00
2019-11-12 05:05 10.5 93.0 8.9 0.5 290 1.1 290 25.006 0.0 0.00
2019-11-12 05:00 9.2 91.8 7.3 1.2 290 2.0 290 25.012 0.0 0.00
2019-11-12 04:55 8.9 90.4 6.6 2.2 290 4.6 296 25.015 0.0 0.00
2019-11-12 04:50 8.2 92.4 6.5 1.3 295 4.9 299 25.017 0.0 0.00
2019-11-12 04:45 6.3 89.2 3.8 0.5 168 1.7 168 25.020 0.0 0.00
2019-11-12 04:40 7.5 87.4 4.5 1.1 168 2.0 168 25.021 0.0 0.00
2019-11-12 04:35 8.1 88.4 5.3 1.9 105 3.5 25 25.023 0.0 0.00
2019-11-12 04:30 9.4 87.4 6.4 1.2 25 3.0 25 25.024 0.0 0.00
2019-11-12 04:25 10.2 91.8 8.3 0.7 171 1.6 171 25.021 0.0 0.00
2019-11-12 04:20 9.2 90.9 7.1 0.5 171 1.6 171 25.023 0.0 0.00
2019-11-12 04:15 9.3 90.9 7.2 1.2 170 3.4 170 25.032 0.0 0.00
2019-11-12 04:10 7.8 92.7 6.1 1.3 231 3.2 231 25.037 0.0 0.00
2019-11-12 04:05 7.2 88.6 4.6 1.2 231 3.2 231 25.040 0.0 0.00
2019-11-12 04:00 7.9 91.1 5.8 0.4 22 1.5 22 25.032 0.0 0.00
2019-11-12 03:55 7.9 90.0 5.5 1.5 22 2.1 22 25.032 0.0 0.00
2019-11-12 03:50 8.9 89.2 6.4 1.9 22 2.7 22 25.036 0.0 0.00
2019-11-12 03:45 9.4 91.5 7.4 1.4 357 3.1 357 25.048 0.0 0.00
2019-11-12 03:40 9.8 92.4 8.0 0.0 339 0.0 339 25.052 0.0 0.00
2019-11-12 03:35 8.9 92.5 7.1 0.6 339 1.5 340 25.056 0.0 0.00
CIRA

Cooperative Institute for Research in the Atmosphere

Dr. Milija Zupanski

CIRA Fellow

Job Title:
Senior Research Scientist, CIRA, Colorado State University
Phone Number:

970-491-8298

Fax Number:

970-491-8241

Mailing Addresss:
Dr. Milija Zupanski
Cooperative Institute for Research in the Atmosphere
Colorado State University
1375 Campus Delivery
Fort Collins, CO 80523-1375
Office Location:
CIRA Room 113
About Me:

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 leads CIRA Data Assimilation Team. (Research web-page at http://da.cira.colostate.edu)

Research Interests:

His areas 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. Dr. Milija Zupanski is a principal developer of the Maximum Likelihood Ensemble Filter (MLEF).

 

Current Research Projects:

1) Ensemble Data Assimilation for Nonlinear and Nondifferentiable Problems in Geosciences (NSF): Extend the applicability of ensemble data assimilation to nonlinear and nondifferentiable processes, such as cloud and precipitation, by developing/adopting nondifferentiable minimization algorithms. This research is done in collaboration with Prof. Michael Navon of Florida State University.

2) Ensemble-based assimilation and downscaling of the GPM-like satellite precipitation information (NASA): Develop a level-4 precipitation product for NASA GPM satellite, by downscaling precipitation observations via ensemble data assimilation.

3) Ensemble data assimilation for hurricane forecasting (NOAA-NCEP): Examine regional data assimilation of all-sky microwave satellite radiances for improvement of hurricane prediction. The NCEP’s operational hurricane WRF (HWRF) model is used.

4) Utility of GOES-R instruments for hurricane data assimilation and forecasting (JCSDA): Evaluate the impact of the future GOES-R ABI and GLM observations on hurricane data assimilation, using the NCEP’s operational HWRF system. This research is done in collaboration with Dr. Jun Li of CIMSS, University of Wisconsin-Madison.

5) Utility of GOES-R geostationary lightning mapper (GLM) using hybrid variational-ensemble data assimilation in regional applications (NOAA-NESDIS): Evaluate the impact of the future GOES-R GLM observations in severe weather on data assimilation, using the NCEP’s operational WRF-NMM model.

6) Ensemble data assimilation research for wind forecasting (Precision Wind, Inc.): Collaborate with private industry in developing a practical system for renewable energy applications.

Professional Website:

http://da.cira.colostate.edu/

Past Work

MLEF as a non-differentiable minimization algorithm

Wednesday, March 5, 2014

Zupanski_plotsThe 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.

Images: 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.

    Publications

    New GOES-R Risk Reduction Activities at CIRA

    Published Date: 2017
    Published By: Conference

    Hybrid variational-ensemble assimilation of lightning observations in a mesoscale mode

    Published Date: 2014
    Published By: Geoscientific Model Development