Improving road weather model forecasts by adjusting the radiation input

Abstract

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 road weather model RoadSurf driven by the HARMONIE-Climate regional climate model: evaluation over Finland

“…As it was the case for the biases, the surrounding characteristics did not have a significant effect on the RMSE values. This partly contradicts the results obtained by Karsisto et al (2016) who found some differences in the RMSE values in October 2013 between the different stations with different surrounding characteristics. In that study, the largest RMSE values were obtained at stations where the Sun was the most obscured.…”

“…On the other hand, the errors in the precipitation input might have caused the higher RMSE values and lower correlations in April compared to the other months: The biases in the HCLIM-ALARO simulated precipitation were the highest in April. In addition, Kangas et al (2015) December at all stations in 2013 and also in almost every simulated year (not shown) as opposed to the findings by Karsisto et al (2016). In their study, RMSE values of the simulated Troad were larger in October 2013 compared to December 2013.…”

“…However, the surrounding characteristics of the stations did not affect the biases. Also Karsisto et al (2016) concluded that there were no considerable differences in the biases in simulated Troad between the stations having different surrounding characteristics (open, slightly obscured, and obscured).…”

“…and RMSE values were slightly larger compared to the previous studies of RoadSurf. For example, Karsisto et al (2016) found that the biases in the simulated Troad varied between -1 and 1 ºC and RMSE values between 0.3 and 1.9 ºC at 20 stations in Finland during October and December 2013. In their study, the input forecast was produced by a high-resolution NWP version of HARMONIE (cy36h1.4) with a grid resolution of 2.5 km.…”

“…These RCM simulations are evaluated against a standard meteorological dataset, E-OBS, over Fenno-Scandia. In the previous studies, mainly NWP model outputs have been used to force RoadSurf. The simulated road weather parameters, such as Troad, have been verified against observations over Finland (Karsisto et al, 2016) and the Netherlands (Karsisto et al, 2017). In addition, Kangas et al (2015) have studied RoadSurf's ability to simulate the amount of water, snow, frost, and ice on the road (called storage terms in RoadSurf) as well as road surface conditions and friction values, although only for two road weather stations in Finland.…”