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TEAM 4 Halm, Bard
Model Initialization Both models agree with significant features like high and low pressures and the overall height field. On the WV image, the ETA and GFS 300mb heights match up almost identically 1.) around the low pressure center near the Hudson Bay and 2.) along a jet streak in western Kansas. The IR image shows the ETA and GFS MSLP contour alignment are almost identically in pressure gradient associated with the 300mb jet streak over western Kansas and Nebraska. Otherwise, the flows associated with the jet streams and other features are contoured similarly across the continental US, Canada and adjacent oceans but are not identical. The differences in heights are much larger over the southern Gulf of Mexico and the Caribbean Sea. In finer detail, the ETA model picked more specific features of the atmosphere than did the GFS model. For example, the ETA model shows a 300mb short-wave ridge in the Central Plains where the GFS did not. Also ETA located more and contoured better MSLP extrema than GFS. Both satellite images verify such places including the surface high in the Bahamas, the jet streak stretching from North Dakota through southern Ontario, and the convection in the South-central Plains. The GFS model, meanwhile, shows a more generalized contouring of pressure and height fields. So it appears that the ETA model depicted the initialization more accurately then the GFS model.
48-Hour Forecast Both models located the general 300mb height contours. These features, particularly verified on the WV image, the closed low just off the California coast, the short-wave troughs in the Ohio Valley and northern mountain states, and the low in eastern Canada. The more detailed features were a bit more of a challenge to forecast. To name an explicit difference, the amplitude of the short-wave trough in Montana and Idaho forecast by the GFS is not as large as the one predicted by the ETA. But the phase of the trough on the ETA model is more north-south oriented as opposed to the more northeast-to-southwest tilt on the model initialization. On the MSLP map, the GFS model more accurately depicted the high pressure center in the Central Plains. It even detected a low pressure center in South Carolina and Georgia where the ETA model did not. There also seems to be a discrepancy as to where the surface low(s) are located in the Desert Southwest – the ETA model at least forecasts a closed extremum in southern California just south of the one on the initialization. Though the ETA model had a better initialization, the GFS had a better / more accurate forecast. Perhaps the more broad-scale weather patterns the GFS recognizes actually occurred and thus had a better forecast. Had there been more pronounced short waves, the ETA might have done better with the forecast.
Poor Man’s Ensemble • The ETA and GFS models show that the weather is not easy to predict. Each model indicates different aspects of what is currently happening in the atmosphere and how these features develop and move with time. We can use these differences in the models to create a more balanced forecast and hopefully a more accurate one. This is where probabilistic forecasting comes into play. By finding conflicting data, we can measure the amount of uncertainty we can issue in forecasts. Likewise, where we find concurrencies in the model predictions, we can feel confident that the models are forecasting a probable event.
