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Caren Marzban

Limited Term Appointment - Pro Staff

Lecturer, Statistics




B.S. Physics, Michigan State University, 1981

Ph.D. Theoretical Physics, University of North Carolina, 1988


2000-present and while at APL-UW

A sensitivity analysis of two mesoscale models: COAMPS and WRF

Marzban, C., R. Tardif, and S. Sandgathe, "A sensitivity analysis of two mesoscale models: COAMPS and WRF," Mon. Wea. Rev., 148, 2997-3014, doi:10.1175/MWR-D-19-0271.1, 2020.

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6 Jul 2020

A sensitivity analysis methodology recently developed by the authors is applied to COAMPS and WRF. The method involves varying model parameters according to Latin Hypercube Sampling, and developing multivariate multiple regression models that map the model parameters to forecasts over a spatial domain. The regression coefficients and p values testing whether the coefficients are zero serve as measures of sensitivity of forecasts with respect to model parameters. Nine model parameters are selected from COAMPS and WRF, and their impact is examined on nine forecast quantities (water vapor, convective and gridscale precipitation, and air temperature and wind speed at three altitudes). Although the conclusions depend on the model parameters and specific forecast quantities, it is shown that sensitivity to model parameters is often accompanied by nontrivial spatial structure, which itself depends on the underlying forecast model (i.e., COAMPS vs WRF). One specific difference between these models is in their sensitivity with respect to a parameter that controls temperature increments in the Kain–Fritsch trigger function; whereas this parameter has a distinct spatial structure in COAMPS, that structure is completely absent in WRF. The differences between COAMPS and WRF also extend to the quality of the statistical models used to assess sensitivity; specifically, the differences are largest over the waters off the southeastern coast of the United States. The implication of these findings is twofold: not only is the spatial structure of sensitivities different between COAMPS and WRF, the underlying relationship between the model parameters and the forecasts is also different between the two models.

Sensitivity analysis of the spatial structure of forecasts in mesoscale models: Noncontinuous model parameters

Marzban, C., R. Tardif, and S. Sandgathe, "Sensitivity analysis of the spatial structure of forecasts in mesoscale models: Noncontinuous model parameters," Mon. Wea. Rev., 148, 1717-1735, doi:, 2020.

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1 Apr 2020

In a recent work, a sensitivity analysis methodology was described that allows for a visual display of forecast sensitivity, with respect to model parameters, across a gridded forecast field. In that approach, sensitivity was assessed with respect to model parameters that are continuous in nature. Here, the analogous methodology is developed for situations involving noncontinuous (discrete or categorical) model parameters. The method is variance based, and the variances are estimated via a random-effects model based on 2k–p fractional factorial designs and Graeco-Latin square designs. The development is guided by its application to model parameters in the stochastic kinetic energy backscatter scheme (SKEBS), which control perturbations at unresolved, subgrid scales. In addition to the SKEBS parameters, the effect of daily variability and replication (both, discrete factors) are also examined. The forecasts examined are for precipitation, temperature, and wind speed. In this particular application, it is found that the model parameters have a much weaker effect on the forecasts as compared to the effect of daily variability and replication, and that sensitivities, weak or strong, often have a distinctive spatial structure that reflects underlying topography and/or weather patterns. These findings caution against fine-tuning methods that disregard 1) sources of variability other than those due to model parameters, and 2) spatial structure in the forecasts.

A methodology for sensitivity analysis of spatial features in forecasts: The stochastic kinetic energy backscatter scheme

Marzban, C., R. Tardif, S. Sandgathe, and N. Hryniw, "A methodology for sensitivity analysis of spatial features in forecasts: The stochastic kinetic energy backscatter scheme," Meteorol. Appl., 26, 545-467, doi:10.1002/met.1775, 2018.

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1 Jul 2019

Stochastic kinetic energy backscatter schemes (SKEBSs) are introduced in numerical weather forecast models to represent uncertainties related to unresolved subgrid‐scale processes. These schemes are formulated using a set of parameters that must be determined using physical knowledge and/or to obtain a desired outcome. Here, a methodology is developed for assessing the effect of four factors on spatial features of forecasts simulated by the SKEBS‐enabled Weather Research and Forecasting model. The four factors include two physically motivated SKEBS parameters (the determining amplitude of perturbations applied to stream function and potential temperature tendencies), a purely stochastic element (a seed used in generating random perturbations) and a factor reflecting daily variability. A simple threshold‐based approach for identifying coherent objects within forecast fields is employed, and the effect of the four factors on object features (e.g. number, size and intensity) is assessed. Four object types are examined: upper‐air jet streaks, low‐level jets, precipitation areas and frontal boundaries. The proposed method consists of a set of standard techniques in experimental design, based on the analysis of variance, tailored to sensitivity analysis. More specifically, a Latin square design is employed to reduce the number of model simulations necessary for performing the sensitivity analysis. Fixed effects and random effects models are employed to assess the main effects and the percentage of the total variability explained by the four factors. It is found that the two SKEBS parameters do not have an appreciable and/or statistically significant effect on any of the examined object features.

More Publications

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center