Considerable savings in winter road maintenance and accident costs can be achieved with accurate road weather forecasts. Forecasting road surface freezing time accurately enables the timely start of salting and thus ensures safety on roads. The optimal use of road weather observations is essential for the accuracy of short-range road condition forecasts. Road weather models incorporate radiation and other atmospheric variables from numerical weather prediction models. In this study, observations were used to correct the forecast radiation and thus improve road weather forecasts for a set of specific sites. Eighteen different configurations of this methodology were tested for 20 road weather stations in Finland during the autumn-winter period 3 October 2013 to 13 January 2014. This study shows that the coupling method has potential to significantly improve road surface temperature forecasts. Two model configurations in particular turned out to be better than the others giving almost equally good road surface temperature forecasts. It was found that the length of the adjustment period using the corrected radiation had only a slight effect on the results. The outcome of this study can be used to improve road weather models and thus achieve more accurate forecasts.
The advances in communication technologies have made it possible to gather road condition information from moving vehicles in real time. However, data quality must be assessed and its effects on the road weather forecasts analyzed before using the new data as input in forecasting systems. Road surface temperature forecasts assimilating mobile observations in the initialization were verified in this study. In addition to using measured values directly, different statistical corrections were applied to the mobile observations before using them in the road weather model. The verification results are compared to a control run without surface temperature measurements and to a control run that utilized interpolated values from surrounding road weather stations. Simulations were done for the period 12 October 2017–30 April 2018 for stationary road weather station points in southern Finland. Road surface temperature observations from the stations were used in the forecast verification. According to the results, the mobile observations improved the accuracy of road surface temperature forecasts when compared to the first control run. The statistical correction methods had a positive effect on forecast accuracy during the winter, but the effect varied during spring when the daily temperature variation was strong. In the winter season, the forecasts based on the interpolated road surface temperature values and the forecasts utilizing mobile observations with statistical correction had comparable accuracy. However, the tested area has high road weather station density and not much elevation variation, so results might have been different in more varying terrain.
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