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. 
Earth before life Marzban, C., R. Viswanathan, and U. Yurtsever, "Earth before life," Biol. Direct, 9, doi:10.1186/1745615091, 2014. 
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9 Jan 2014 

A recent study argued, based on data on functional genome size of major phyla, that there is evidence life may have originated significantly prior to the formation of the Earth. 
Variancebased sensitivity analysis: An illustration on the Lorenz '63 model Marzban, C., "Variancebased sensitivity analysis: An illustration on the Lorenz '63 model," Mon. Wea. Rev., 141, 40694079, doi:10.1175/MWRD1300032.1, 2013. 
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1 Nov 2013 

Sensitivity analysis (SA) generally refers to an assessment of the sensitivity of the output(s) of some complex model with respect to changes in the input(s). Examples of inputs or outputs include initial state variables, parameters of a numerical model, or state variables at some future time. Sensitivity analysis is useful for data assimilation, model tuning, calibration, and dimensionality reduction; and there exists a wide range of SA techniques for each. This paper discusses one special class of SA techniques, referred to as variance based. As a first step in demonstrating the utility of the method in understanding the relationship between forecasts and parameters of complex numerical models, here the method is applied to the Lorenz '63 model, and the results are compared with an adjointbased approach to SA. The method has three major components: 1) analysis of variance, 2) emulation of computer data, and 3) experimental—sampling design. The role of these three topics in variancebased SA is addressed in generality. More specifically, the application to the Lorenz '63 model suggests that the Z state variable is most sensitive to the b and r parameters, and is mostly unaffected by the s parameter. There is also evidence for an interaction between the r and b parameters. It is shown that these conclusions are true for both simple random sampling and Latin hypercube sampling, although the latter leads to slightly more precise estimates for some of the sensitivity measures. 
A method for estimating zeroflow pressure and intracranial pressure Marzban, C., P.R. Illian, D. Morison, A. Moore, M. Kliot, M. Czosnyka, and P.D. Mourad, "A method for estimating zeroflow pressure and intracranial pressure," J. Neurosurg. Anesthesiol., 25, 2532, doi:10.1097/ANA.0b013e318263c295, 2013. 
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1 Jan 2013 

BACKGROUND: It has been hypothesized that the critical closing pressure of cerebral circulation, or zeroflow pressure (ZFP), can estimate intracranial pressure (ICP). One ZFP estimation method used extrapolation of arterial blood pressure as against bloodflow velocity. The aim of this study was to improve ICP predictions. METHODS: Two revisions have been considered: (1) the linear model used for extrapolation is extended to a nonlinear equation; and (2) the parameters of the model are estimated by an alternative criterion (not least squares). The method is applied to data on transcranial Doppler measurements of bloodflow velocity, arterial blood pressure, and ICP from 104 patients suffering from closed traumatic brain injury, sampled across the United States and England. RESULTS: The revisions lead to qualitative (eg, precluding negative ICP) and quantitative improvements in ICP prediction. While moving from the original to the revised method, the ±2 SD of the error is reduced from 33 to 24 mm Hg, and the rootmeansquared error is reduced from 11 to 8.2 mm Hg. The distribution of rootmeansquared error is tighter as well; for the revised method the 25th and 75th percentiles are 4.1 and 13.7 mm Hg, respectively, as compared with 5.1 and 18.8 mm Hg for the original method. CONCLUSIONS: Proposed alterations to a procedure for estimating ZFP lead to more accurate and more precise estimates of ICP, thereby offering improved means of estimating it noninvasively. The quality of the estimates is inadequate for many applications, but further work is proposed, which may lead to clinically useful results. 
On the effect of correlations on rank histograms: Reliability of temperature and windspeed forecasts from finescale ensemble reforecasts Marzban, C., R. Wang, F. Kong, and S. Leyton, "On the effect of correlations on rank histograms: Reliability of temperature and windspeed forecasts from finescale ensemble reforecasts," Mon. Wea. Rev., 139, 295310, doi: 10.1175/2010MWR3129.1, 2011. 
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1 Jan 2011 

The rank histogram (RH) is a visual tool for assessing the reliability of ensemble forecasts (i.e., the degree to which the forecasts and the observations have the same distribution). But it is already known that in certain situations it conveys misleading information. Here, it is shown that a temporal correlation can lead to a misleading RH, but such a correlation contributes only to the sampling variability of the RH, and so it is accounted for by producing a RH that explicitly displays sampling variability. A simulation is employed to show that the variance within each ensemble member (i.e., climatological variance), the correlation between ensemble members, and the correlation between the observations and the forecasts, all have a confounding effect on the RH, making it difficult to use the RH for assessing the climatological component of forecast reliability. It is proposed that a "residual" quantilequantile plot (denoted RQQ plot) is better suited than the RH for assessing the climatological component of forecast reliability. Then, the RH and RQQ plots for temperature and wind speed forecasts at 90 stations across the continental United States are computed. A wide range of forecast reliability is noted. For some stations, the nonreliability of the forecasts can be attributed to bias and/or underor overclimatological dispersion. For others, the difference between the distributions can be traced to lighter or heavier tails in the distributions, while for other stations the distributions of the forecasts and the observations appear to be completely different. A spatial signature is also noted and discussed briefly. 
Optical flow for verification Marzban, C., and S. Sandgathe, "Optical flow for verification," Weather Forecast., 25, 14791494, doi:10.1175/2010WAF2222351.1, 2010. 
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1 Oct 2010 

Modern numerical weather prediction (NWP) models produce forecasts that are gridded spatial fields. Digital images can also be viewed as gridded spatial fields, and as such, techniques from image analysis can be employed to address the problem of verification of NWP forecasts. One technique for estimating how images change temporally is called optical flow, where it is assumed that temporal changes in images (e.g., in a video) can be represented as a fluid flowing in some manner. Multiple realizations of the general idea have already been employed in verification problems as well as in data assimilation. 
Three spatial verification techniques: Cluster analysis, variogram, and optical flow Marzban, C., S. Sandgathe, H. Lyons, and N. Lederer, "Three spatial verification techniques: Cluster analysis, variogram, and optical flow," Weather Forecast., 24, 14571471, 2009. 
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1 Dec 2009 

Three spatial verification techniques are applied to three datasets. The datasets consist of a mixture of real and artificial forecasts, and corresponding observations, designed to aid in better understanding the effects of global (i.e., across the entire field) displacement and intensity errors. The three verification techniques, each based on wellknown statistical methods, have little in common and, so, present different facets of forecast quality. It is shown that a verification method based on cluster analysis can identify "objects" in a forecast and an observation field, thereby allowing for objectoriented verification in the sense that it considers displacement, missed forecasts, and false alarms. A second method compares the observed and forecast fields, not in terms of the objects within them, but in terms of the covariance structure of the fields, as summarized by their variogram. The last method addresses the agreement between the two fields by inferring the function that maps one to the other. The map — generally called optical flow — provides a (visual) summary of the "difference" between the two fields. A further summary measure of that map is found to yield useful information on the distortion error in the forecasts. 
Using labeled data to evaluate change detectors in a multivariate streaming environment Kim, A.Y., C. Marzban, D.B. Percival, and W. Stuetzle, "Using labeled data to evaluate change detectors in a multivariate streaming environment," Signal Process., 89, 25292536, doi:10.1016/j.sigpro.2009.04.011, 2009. 
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1 Dec 2009 

We consider the problem of detecting changes in a multivariate data stream. A change detector is defined by a detection algorithm and an alarm threshold. A detection algorithm maps the stream of input vectors into a univariate detection stream. The detector signals a change when the detection stream exceeds the chosen alarm threshold. We consider two aspects of the problem: (1) setting the alarm threshold and (2) measuring/comparing the performance of detection algorithms. 
Verification with variograms Marzban, C., and S. Sandgathe, "Verification with variograms," Weather Forecast., 24, 11021120, doi: 10.1175/2009WAF2222122.1, 2009. 
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1 Aug 2009 

The verification of a gridded forecast field, for example, one produced by numerical weather prediction (NWP) models, cannot be performed on a gridpointbygridpoint basis; that type of approach would ignore the spatial structures present in both forecast and observation fields, leading to misinformative or noninformative verification results. A variety of methods have been proposed to acknowledge the spatial structure of the fields. 
Towards predicting intracranial pressure using transcranial Doppler and arterial blood pressure data Mourad, P.D., C. Marzban, and M. Kliot, "Towards predicting intracranial pressure using transcranial Doppler and arterial blood pressure data," J. Acoust. Soc. Am., 125, 2514, 2009. 
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1 Apr 2009 

Pressure within the cranium (intracranial pressure, or "ICP") represents a vital clinical variable whose assessment — currently via invasive means — and integration into a clinical exam constitutes a necessary step for adequate medical care for those patients with injured brains. In the present work we sought to develop a noninvasive way of predicting this variable and its corollary — cerebral perfusion pressure (CPP), which equals ICP minus arterial blood pressure (ABP). 
An objectoriented verification of three NWP model formulations via cluster analysis: An objective and a subjective analysis Marzban, C., S. Sandgathe, and H. Lyons, "An objectoriented verification of three NWP model formulations via cluster analysis: An objective and a subjective analysis,". Mon. Weather Rev., 136, 33923407, 2008. 
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1 Sep 2008 

Recently, an objectoriented verification scheme was developed for assessing errors in forecasts of spatial fields. The main goal of the scheme was to allow the automatic and objective evaluation of a large number of forecasts. However, processing speed was an obstacle. Here, it is shown that the methodology can be revised to increase efficiency, allowing for the evaluation of 32 days of reflectivity forecasts from three different mesoscale numerical weather prediction model formulations. It is demonstrated that the methodology can address not only spatial errors, but also intensity and timing errors. The results of the verification are compared with those performed by a human expert. 
Cluster analysis for objectoriented verification of fields: A variation Marzban, C., and S. Sandgathe, "Cluster analysis for objectoriented verification of fields: A variation," Mon. Weather Rev., 136, 10131025, doi:10.1175/2007MWR1994.1, 2008. 
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1 Mar 2008 

In a recent paper, a statistical method referred to as cluster analysis was employed to identify clusters in forecast and observed fields. Further criteria were also proposed for matching the identified clusters in one field with those in the other. As such, the proposed methodology was designed to perform an automated form of what has been called objectoriented verification. Herein, a variation of that methodology is proposed that effectively avoids (or simplifies) the criteria for matching the objects. The basic idea is to perform cluster analysis on the combined set of observations and forecasts, rather than on the individual fields separately. This method will be referred to as combinative cluster analysis (CCA). CCA naturally lends itself to the computation of false alarms, hits, and misses, and therefore, to the critical success index (CSI). 
Ceiling and visibility forecasts via neural networks Marzban, C., S. Leyton, and B. Colman, "Ceiling and visibility forecasts via neural networks," Wea. Forecasting, 22, 466479, 2007. 
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1 Jun 2007 

Statistical postprocessing of numerical model output can improve forecast quality, especially when model output is combined with surface observations. In this article, the development of nonlinear postprocessors for the prediction of ceiling and visibility is discussed. The forecast period is approximately 2001–05, involving data from hourly surface observations, and from the fifthgeneration Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model. The statistical model for mapping these data to ceiling and visibility is a neural network. A total of 39 such neural networks are developed for each of 39 terminal aerodrome forecast stations in the northwest United States. These postprocessors are compared with a number of alternatives, including logistic regression, and model output statistics (MOS) derived from the Aviation Model/Global Forecast System. It is found that the performance of the neural networks is generally superior to logistic regression and MOS. Depending on the comparison, different measures of performance are examined, including the Heidke skill statistic, crossentropy, relative operating characteristic curves, discrimination plots, and attributes diagrams. The extent of the improvement brought about by the neural network depends on the measure of performance, and the specific station. 
Bottomup forcing and the decline of Stellar sea lions (Eumetopias jubatas) in Alaska: Assessing the ocean climate hypothesis Trites, A.W., et al. (including C. Marzban), "Bottomup forcing and the decline of Stellar sea lions (Eumetopias jubatas) in Alaska: Assessing the ocean climate hypothesis," Fish. Oceanogr., 16, 4667, doi:10.1111/j.13652419.2006.00408.x, 2007. 
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1 Jan 2007 

Declines of Steller sea lion (Eumetopias jubatus) populations in the Aleutian Islands and Gulf of Alaska could be a consequence of physical oceanographic changes associated with the 1976–77 climate regime shift. Changes in ocean climate are hypothesized to have affected the quantity, quality, and accessibility of prey, which in turn may have affected the rates of birth and death of sea lions. Recent studies of the spatial and temporal variations in the ocean climate system of the North Pacific support this hypothesis. Ocean climate changes appear to have created adaptive opportunities for various species that are preyed upon by Steller sea lions at midtrophic levels. The east–west asymmetry of the oceanic response to climate forcing after 1976–77 is consistent with both the temporal aspect (populations decreased after the late 1970s) and the spatial aspect of the decline (western, but not eastern, sea lion populations decreased). These broadscale climate variations appear to be modulated by regionally sensitive biogeographic structures along the Aleutian Islands and Gulf of Alaska, which include a transition point from coastal to openocean conditions at Samalga Pass westward along the Aleutian Islands. These transition points delineate distinct clusterings of different combinations of prey species, which are in turn correlated with differential population sizes and trajectories of Steller sea lions. Archaeological records spanning 4000 yr further indicate that sea lion populations have experienced major shifts in abundance in the past. Shifts in ocean climate are the most parsimonious underlying explanation for the broad suite of ecosystem changes that have been observed in the North Pacific Ocean in recent decades. 
Cluster analysis for verification of precipitation fields Marzban, C., and S. Sandgathe, "Cluster analysis for verification of precipitation fields," Weather Forecast., 21, 824838, 2006. 
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1 Oct 2006 

A statistical method referred to as cluster analysis is employed to identify features in forecast and observation fields. These features qualify as natural candidates for events or objects in terms of which verification can be performed. The methodology is introduced and illustrated on synthetic and real quantitative precipitation data. First, it is shown that the method correctly identifies clusters that are in agreement with what most experts might interpret as features or objects in the field. Then, it is shown that the verification of the forecasts can be performed within an eventbased framework, with the events identified as the clusters. The number of clusters in a field is interpreted as a measure of scale, and the final "product" of the methodology is an "error surface" representing the error in the forecasts as a function of the number of clusters in the forecast and observation fields. This allows for the examination of forecast error as a function of scale. 
MOS, Perfect Prog, and reanalysis Marzban, C., S. Sandgathe, and E. Kalnay, "MOS, Perfect Prog, and reanalysis," Mon. Weather Rev., 134, 657663, doi:10.1175/MWR3088.1, 2005. 
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1 Feb 2006 

Statistical postprocessing methods have been successful in correcting many defects inherent in numerical weather prediction model forecasts. Among them, model output statistics (MOS) and perfect prog have been most common, each with its own strengths and weaknesses. Here, an alternative method (called RAN) is examined that combines the two, while at the same time utilizes the information in reanalysis data. The three methods are examined from a purely formal/mathematical point of view. The results suggest that whereas MOS is expected to outperform perfect prog and RAN in terms of mean squared error, bias, and error variance, the RAN approach is expected to yield more certain and biasfree forecasts. It is suggested therefore that a realtime RANbased postprocessor be developed for further testing. 
Inventions
System and Methods for Tracking Finger and Hand Movement Using Ultrasound Record of Invention Number: 47931 
Disclosure

10 Jan 2017
