Caren Marzban Principal Physicist Lecturer, Statistics marzban@stat.washington.edu Phone 2062214361 
Education
B.S. Physics, Michigan State University, 1981
Ph.D. Theoretical Physics, University of North Carolina, 1988
Publications 
2000present and while at APLUW 
Mixture models for estimating maximum blood flow velocity Marzban, C., G. Wenxiao, and P.D. Mourad, "Mixture models for estimating maximum blood flow velocity," J. Ultrasound Med., 35, 93101, doi:10.7863/ultra.14.05069, 2016. 
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1 Jan 2016 


Model tuning with canonical correlation analysis Marzban, C., S. Sandgathe, and J.D. Doyle, "Model tuning with canonical correlation analysis," Mon. Wea. Rev., 142, 20182027, doi:10.1175/MWRD1300245.1, 2014. 
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1 May 2014 

Knowledge of the relationship between model parameters and forecast quantities is useful because it can aid in setting the values of the former for the purpose of having a desired effect on the latter. Here it is proposed that a wellestablished multivariate statistical method known as canonical correlation analysis can be formulated to gauge the strength of that relationship. The method is applied to several model parameters in the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) for the purpose of "controlling" three forecast quantities: 1) convective precipitation, 2) stable precipitation, and 3) snow. It is shown that the model parameters employed here can be set to affect the sum, and the difference between convective and stable precipitation, while keeping snow mostly constant; a different combination of model parameters is shown to mostly affect the difference between stable precipitation and snow, with minimal effect on convective precipitation. In short, the proposed method cannot only capture the complex relationship between model parameters and forecast quantities, it can also be utilized to optimally control certain combinations of the latter. 
Variancebased sensitivity analysis: Preliminary results in COAMPS Marzban, C., S. Sandgathe, J.D. Doyle, and N.C. Lederer, "Variancebased sensitivity analysis: Preliminary results in COAMPS," Mon. Wea. Rev., 142, 20282042, doi:10.1175/MWRD1300195.1, 2014. 
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1 May 2014 

Numerical weather prediction models have a number of parameters whose values are either estimated from empirical data or theoretical calculations. These values are usually then optimized according to some criterion (e.g., minimizing a cost function) in order to obtain superior prediction. To that end, it is useful to know which parameters have an effect on a given forecast quantity, and which do not. Here the authors demonstrate a variancebased sensitivity analysis involving 11 parameters in the Coupled Ocean%u2013Atmosphere Mesoscale Prediction System (COAMPS). Several forecast quantities are examined: 24h accumulated 1) convective precipitation, 2) stable precipitation, 3) total precipitation, and 4) snow. The analysis is based on 36 days of 24h forecasts between 1 January and 4 July 2009. Regarding convective precipitation, not surprisingly, the most influential parameter is found to be the fraction of available precipitation in the Kain%u2013Fritsch cumulus parameterization fed back to the grid scale. Stable and total precipitation are most affected by a linear factor that multiplies the surface fluxes; and the parameter that most affects accumulated snow is the microphysics slope intercept parameter for snow. Furthermore, all of the interactions between the parameters are found to be either exceedingly small or have too much variability (across days and/or parameter values) to be of primary concern. 
Inventions
System and Methods for Tracking Finger and Hand Movement Using Ultrasound Record of Invention Number: 47931 
Disclosure

10 Jan 2017
