APL-UW Home

Jobs
About
Campus Map
Contact
Privacy
Intranet

Scott Sandgathe

Senior Principal Oceanographer

Email

sandgathe@apl.washington.edu

Phone

541-988-0289

Research Interests

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

Biosketch

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.

Education

B.S. Physics, Oregon State University, 1972

Ph.D. Meteorology, Naval Postgraduate School, 1981

Publications

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.

More Info

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.

More Info

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.

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.

More Info

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.

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
Close

 

Close