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 
On the effect of model parameters on forecast objects Marzban, C., C. Jones, N. Li, and S. Sandgathe, "On the effect of model parameters on forecast objects," Geosci. Model Dev., 11, 15771590, doi:10.5194/gmd1115772018, 2018. 
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19 Apr 2018 

Many physicsbased numerical models produce a gridded, spatial field of forecasts, e.g., a temperature "map". The field for some quantities generally consists of spatially coherent and disconnected "objects". Such objects arise in many problems, including precipitation forecasts in atmospheric models, eddy currents in ocean models, and models of forest fires. Certain features of these objects (e.g., location, size, intensity, and shape) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on the features of forecast objects. The main ingredients of the methodology include the use of (1) Latin hypercube sampling for varying the values of the model parameters, (2) statistical clustering algorithms for identifying objects, (3) multivariate multiple regression for assessing the impact of multiple model parameters on the distribution (across the forecast domain) of object features, and (4) methods for reducing the number of hypothesis tests and controlling the resulting errors. The final "output" of the methodology is a series of box plots and confidence intervals that visually display the sensitivities. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model. 
Sensitivity analysis of the spatial structure of forecasts in mesoscale models: Continuous model parameters Marzban, C., X. Du, S. Sandgate, J.D. Doyle, Y. Jin, and N.C. Lederer, "Sensitivity analysis of the spatial structure of forecasts in mesoscale models: Continuous model parameters," Mon. Weather Rev., 146, 967983, doi:10.1175/MWRD170275.1, 2018. 
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1 Apr 2018 

A methodology is proposed for examining the effect of model parameters (assumed to be continuous) on the spatial structure of forecasts. The methodology involves several statistical methods of sampling and inference to assure the sensitivity results are statistically sound. Specifically, Latin hypercube sampling is employed to vary the model parameters, and multivariate multiple regression is used to account for spatial correlations in assessing the sensitivities. The end product is a geographic "map" of p values for each model parameter, allowing one to display and examine the spatial structure of the sensitivity. As an illustration, the effect of 11 model parameters in a mesoscale model on forecasts of convective and gridscale precipitation, surface air temperature, and water vapor is studied. A number of spatial patterns in sensitivity are found. For example, a parameter that controls the fraction of available convective clouds and precipitation fed back to the grid scale influences precipitation forecasts mostly over the southeastern region of the domain; another parameter that modifies the surface fluxes distinguishes between precipitation forecasts over land and over water. The sensitivity of surface air temperature and water vapor forecasts also has distinct spatial patterns, with the specific pattern depending on the model parameter. Among the 11 parameters examined, there is one (an autoconversion factor in the microphysics) that appears to have no influence in any region and on any of the forecast quantities. 
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 


Inventions
System and Methods for Tracking Finger and Hand Movement Using Ultrasound Record of Invention Number: 47931 
Disclosure

10 Jan 2017
