Statistically post-processed numerical model output has long been a primary source of guidance for forecasting surface weather conditions. In particular, Model Output Statistics (MOS) derived from the Nested Grid Model (NGM) and, more recently, the Aviation Model (AVN) provide forecasts of weather parameters at three-hour intervals out to 60 hours. These models are run four times daily, allowing MOS forecasts to become six hours old before an updated product is made available. Therefore, it may be possible to improve conventional guidance for short-term (< 6 h) forecasts of selected parameters if observations taken after the time that the MOS guidance is derived are used to produce updated forecasts.

With this in mind, Vislocky and Fritsch (1997) developed an “observations-based” system for producing short-term forecasts. Specifically, a network of surface observations was used as predictors in a multiple linear regression technique. It was demonstrated that this approach could improve the accuracy of ceiling and visibility forecasts for the hours between the times that the output from the twice-daily operational runs is released.

Following the development of observations-based forecasting systems using reports from the standard synoptic surface observing network, increasing amounts of surface weather data became available as a result of upgrades in the coverage of Automated Surface Observation Systems (ASOS) and Automated Weather Observation Systems (AWOS). A study was undertaken to quantify whether or not the increased spatial resolution of surface observing networks yields improvements in short-term forecasts of ceiling and visibility. The following strategy was employed to evaluate the effects of higher-density observing networks. First, a baseline forecasting system was developed using only data from the stations available prior to the increase in the observing density. Second, an alternative forecasting system was developed using all available data, including the high-density observations. Finally, the results of this new forecasting system were compared to the results of the baseline system.

It was found that additional observing sites improved the system’s skill. Typically, mean square errors were reduced by an additional two to five percent above that produced by the baseline system. For most of the standard ceiling and visibility categories (e.g., ceiling < 1000 feet; visibility < 1 mile, etc.), there were improvements of 10 to about 30% compared to the performance of persistence climatology. It is noteworthy that most of the improvements occurred for the very short-term forecasts, i.e., the one-hour and three-hour forecasts.

Inspection of individual events indicates that the improvements in the very short-term forecasts stem from the ability of the higher-density observing network to more correctly characterize a given event. For example, while it is true that there are instances wherein large areas are uniformly and persistently blanketed with low ceiling and visibility, analyses of individual events revealed that some events are more aptly described as having intermittent, patchy or isolated low ceilings/visibilities. Therefore, the chances of mischaracterizing an event are greater when there are only a few observations. Thus, higher density of predictors (i.e., the observations at the surrounding stations) can more clearly distinguish among the various types of events and provide more accurate probabilistic categorical forecasts.