Short-Term Probabilistic Forecasts of Ceiling and Visibility Utilizing High-Density Surface Weather Observations

Abstract

An automated statistical system that utilizes regional high-density surface observations to forecast low ceiling and visibility events in the upper Midwest is presented. The system is based solely upon surface observations as predictors, featuring forecast lead times of 1, 3, and 6 h. A test of the forecast system on a 5-yr independent sample of events shows that for a 1-h lead time, an additional 2%?4% reduction in the mean squared error (MSE) is obtained by the high-density forecasting system compared to that for a system utilizing only the standard synoptic observations. Meanwhile, tests on a 3-h lead time reveal an additional 0%?1.5% reduction in MSE by the high-density system over the synoptic system. Little improvement is gained by the high-density system at a 6-h lead time. The results indicate that current observations-based forecasting techniques can be improved simply by utilizing a higher density of surface weather observations. With this enhanced guidance, it is likely that decisions impacted by the arrival and duration of low ceiling and visibility can be improved.