Forecasting of road surface and traffic conditions is an important aspect of traffic safety and winter road maintenance, especially in the harsh northern climate. The weather conditions can change quickly, for example, with the onset of snowfall or during rapid temperature variations. A prior knowledge of road weather is important from a public road safety standpoint. Proper consideration of upcoming weather events also helps the road maintenance authorities to attend the roads in an effective and economical manner. In Finland, the Finnish Meteorological Institute (FMI) is duty bound to issue warnings of hazardous traffic conditions to the general public. To strengthen these services towards more efficient estimation of rapidly varying conditions of the road surface at a national scale, a simulation model, RoadSurf, has been developed. As input, the model employs numerical weather forecasts, either directly or after modifications made by meteorologists, as well as observations from synoptic or road weather stations and radar precipitation measurement network. As output, the model produces not only road surface temperature, but also road surface condition classification and a traffic index describing the driving conditions in more general terms, as well as road surface friction. The model has been in operational use since 2000. In addition to the original goal of providing road weather forecasts for the national road network, the model has been used in several other applications, for example, in predicting pedestrian sidewalk conditions and in numerous intelligent traffic applications. The present study describes the road weather model RoadSurf and its main applications.

Icy and snowy sidewalks are typical wintertime phenomena in Finland. Wintertime slipping injuries are common and lead to substantial economic costs to health care as well as losses to society due to long sick leaves. In Finland, almost every second person slips and falls outdoors annually, and around 70,000 persons are injured needing medical attention. Typically, the most slippery conditions are encountered when the daily average temperature is slightly below 0°C or temperature crosses 0°C and there is precipitation in some form. The Finnish Meteorological Institute (FMI) has developed a numerical weather model that simulates the level of slipperiness on the sidewalks. The model classifies the sidewalk slipperiness into three classes; normal, slippery and very slippery. The FMI issues warnings of hazardous sidewalk conditions to the general public. Pedestrians' road safety can be increased with sidewalk condition forecasts and warnings. When warned, people can choose proper footwear or use anti‐slip devices, change the route or mode of transport, postpone the journey or cancel it altogether. Precise and reliable weather and sidewalk condition forecasts enable targeted and more effective sidewalk maintenance activities that can improve the grip of sidewalks and thus reduce the risk of accidents and injuries. This study presents the sidewalk condition model RoadSurf‐Pedestrian, its physical principles and examples of model runs. There are some challenges in the modelling of the slipperiness but the model gives valuable information on the slipperiness for duty forecasters. Slipping injury statistics are also presented and used as verification data.

Sodankylä, in the heart of Arctic Research Centre of the Finnish Meteorological Institute (FMI ARC) in northern Finland, is an ideal site for atmospheric and environmental research in the boreal and sub-Arctic zone. With temperatures ranging from −50 to +30 °C, it provides a challenging testing ground for numerical weather forecasting (NWP) models as well as weather forecasting in general. An extensive set of measurements has been carried out in Sodankylä for more than 100 years. In 2000, a 48 m-high micrometeorological mast was erected in the area. In this article, the use of Sodankylä mast measurements in NWP model verification is described. Starting in 2000, with the NWP model HIRLAM and Sodankylä measurements, the verification system has now been expanded to include comparisons between 12 NWP models and seven measurement masts, distributed across Europe. A case study, comparing forecasted and observed radiation fluxes, is also presented. It was found that three different radiation schemes, applicable in NWP model HARMONIE-AROME, produced somewhat different downwelling longwave radiation fluxes during cloudy days, which however did not change the overall cold bias of the predicted screen-level temperature.

Road weather is a major concern for the public safety and health, industries and transport sectors. Half of the yearly 27,000 road and 50,000 pedestrian injuries in Finland, Norway and Sweden can be traced back to slippery road and walkway conditions. We simulated the climate change impacts on future roads and walkways for mid- and end-century in Finland, Norway and Sweden with the road weather model RoadSurf, driven by the regional climate model HCLIM38 with boundary data from two global climate models following the RCP8.5 scenario.Our simulations for mid-century suggest strong road surface temperature increases, especially in southern Finland (+ 5.1 °C) and Sweden (+ 7.1 °C). Snowy and icy road surface conditions decreased by 23 percentage points, causing 18.5 percentage points less difficult driving conditions during the cold season. Zero-degree-crossing days mostly decreased in autumn and spring by up to 7 days and increased in winter by up to 5 days. Sidewalks mostly showed a decrease in slipperiness, but a five percentage point increase of water above ice layers on the sidewalks in winter, suggesting the slip-season might become shorter, but more slippery.Our results are upper extreme estimates but can serve as a reference to help local decision-makers plan mitigation and adaptation measures ahead of time.

Pedestrians’ slipping injuries are a very typical problem in the Nordic countries, causing varying degrees of injuries and in the worst case, long sick leaves. There is a clear seasonal variation in the number of slips. Sidewalk slipperiness and the risk of slips is a complex combination of weather, winter maintenance activities, number of walkers, and the grip between shoes and surface, as well as human behavioral and physical factors. In this study, the effect of weather on pedestrians’ slipping injuries is studied. Daily weather observations are compared to the slip statistics that have been collected from commuting accident statistics in cases where the way of commuting has been walking. A total of 16 cities from Finland for 14 winters are included in this study. The results reveal that snow on the ground increases the slip risk more than three times compared to no-snow situations. Near zero temperatures and precipitation are very typical on days when slip injuries occur more than usual. However, there are also days when high amounts of slips cannot be explained with the weather. The study also shows that there are significant differences as to the number and timing of slips between different parts of the country.

In this paper, we evaluate the skill of the road weather model RoadSurf to reproduce present-day road weather conditions in Finland. RoadSurf was driven by meteorological input data from cycle 38 of the high-resolution regional climate model (RCM) HARMONIE-Climate (HCLIM38) with ALARO physics (HCLIM38-ALARO) and ERA-Interim forcing in the lateral boundaries. Simulated road surface temperatures and road surface conditions were compared to observations between 2002 and 2014 at 25 road weather stations located in different parts of Finland. The main characteristics of road weather conditions were accurately captured by RoadSurf in the study area. For example, the model simulated road surface temperatures with a mean monthly bias of −0.3 ∘C and mean absolute error of 0.9 ∘C. The RoadSurf's output bias most probably stemmed from the absence of road maintenance operations in the model, such as snow plowing and salting, and the biases in the input meteorological data. The biases in the input data were most evident in northern parts of Finland, where the regional climate model HCLIM38-ALARO overestimated precipitation and had a warm bias in near-surface air temperatures during the winter season. Moreover, the variability in the biases of air temperature was found to explain on average 57 % of the variability in the biases of road surface temperature. On the other hand, the absence of road maintenance operations in the model might have affected RoadSurf's ability to simulate road surface conditions: the model tended to overestimate icy and snowy road surfaces and underestimate the occurrence of water on the road. However, the overall good performance of RoadSurf implies that this approach can be used to study the impacts of climate change on road weather conditions in Finland by forcing RoadSurf with future climate projections from RCMs, such as HCLIM.