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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

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.

Model tuning with canonical correlation analysis

Marzban, C., S. Sandgathe, and J.D. Doyle, "Model tuning with canonical correlation analysis," Mon. Wea. Rev., 142, 2018-2027, doi:10.1175/MWR-D-13-00245.1, 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 well-established 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.

Variance-based sensitivity analysis: Preliminary results in COAMPS

Marzban, C., S. Sandgathe, J.D. Doyle, and N.C. Lederer, "Variance-based sensitivity analysis: Preliminary results in COAMPS," Mon. Wea. Rev., 142, 2028-2042, doi:10.1175/MWR-D-13-00195.1, 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 variance-based sensitivity analysis involving 11 parameters in the Coupled Ocean%u2013Atmosphere Mesoscale Prediction System (COAMPS). Several forecast quantities are examined: 24-h accumulated 1) convective precipitation, 2) stable precipitation, 3) total precipitation, and 4) snow. The analysis is based on 36 days of 24-h 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.

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