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Scott Sandgathe

Senior Principal Oceanographer





Research Interests

Meteorological Analysis and Verification, Forecast Meteorology, Navy Technology Systems, Numerical Weather Prediction, Tropical Meteorology


Dr. Sandgathe has extensive experience in operational oceanography and meteorology including tropical meteorology, synoptic analysis and forecasting, and numerical weather prediction. He is currently technical advisor to the Navy's Tactical Weather Radar Program and NOWCAST Program. He is also developing an automated forecast verification technique for mesoscale numerical weather prediction and working on automation and visualization tools for Navy meteorologists. Dr. Sandgathe joined the Laboratory in 2001.


B.S. Physics, Oregon State University, 1972

Ph.D. Meteorology, Naval Postgraduate School, 1981


2000-present and while at APL-UW

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.

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, 1577-1590, doi:10.5194/gmd-11-1577-2018, 2018.

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19 Apr 2018

Many physics-based 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, 967-983, doi:10.1175/MWR-D-17-0275.1, 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 grid-scale 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.

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