A statistical system that uses surface observations and radar data to provide 1-, 3-, and 6-h forecasts of temperature, dewpoint depression, and wind speed is developed. Application of the system to independent data demonstrates that the radar data result in a 3% - 6% reduction in the mean-squared error (MSE) of the temperature forecasts when compared with that when only surface observations are used as predictors. The addition of the radar data similarly provides a 2% - 4% reduction in MSE of the dewpoint depression forecasts. However, there is no difference in skill when the radar data are included in forecasts of wind speed. The majority of the radar-data predictors selected by the stepwise regression algorithm for inclusion in the forecast system are from areas that are poorly sampled by surface observations. This result supports the argument that more uniform sampling ( better coverage) of the environment is playing a role in the improvement of the forecasts. Moreover, analysis of individual events reveals that much of the reduction in MSE ( as a result of the inclusion of the radar data) occurs on days for which the "surface only'' system has large errors. In general, the addition of the radar data improves the surface-only-system forecasts of cases in which precipitation is occurring without creating a less accurate forecast when precipitation is not occurring.
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