Our results suggest that the epidemiology of distal radius fractures in elderly women in Finland has changed compared with a previous study. Weather analysis showed that the slipperiness of the pavement could partly explain the wintertime excess of distal radius fractures.

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.

Prevailing road surface conditions, the grip between tyres and the road surface, strongly correlate with traffic accident rate. Surface friction is reduced especially during snowfall or icing. Friction observations derived from Vaisala's optical DSC111 sensors, operated during two winter seasons (2007/2008, 2008/2009) at several Finnish roadside stations have been used. The devices measure the depth of water, snow and ice on the road surface and also produce an estimate of prevailing friction. The observations have been used to develop statistical equations to model road surface friction. The model has been evaluated against an independent dataset from winter 2009/2010. The goal thereafter was to integrate the scheme into the road weather service of the Finnish Meteorological Institute by combining background information from a road weather model with the derived statistical equations.

During the morning rush hours of 17 March 2005, a band of intense snowfall affected the Helsinki metropolitan area in southern Finland. The event caused severe pile-ups on the highways, with almost 300 crashed cars, the deaths of three people and more than 60 people injured. The snowfall was soon followed by freezing drizzle. Some of the media later blamed this as being responsible for the unprecedented number and severity of the accidents that had occurred. However, the official inquiry came to the conclusion that the pile-ups had been caused by the very poor visibility due to intense snowfall and excessive driving speeds combined with reduced road surface friction. In this study, these two viewpoints are investigated, by using high time-resolution dual-polarization radar observations to analyse the changes in intensity and form of the precipitation leading to the event. The radar data were particularly useful in supplementing weather observations.

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.

(1) Background: In Finland, slippery snowy or icy ground surface conditions can be quite hazardous to human health during wintertime. We focused on the impacts of the variability in weather conditions on tourists’ health via documented accidents during the winter season in the Sotkamo area. We attempted to estimate the slipping hazard in a specific context of space and time focusing on the weather and other possible parameters, responsible for fluctuations in the numbers of injuries/accidents; (2) Methods: We used statistical distributions with graphical illustrations to examine the distribution of visits to Kainuu Hospital by non-local patients and their characteristics/causes; graphs to illustrate the distribution of the different characteristics of weather conditions; questionnaires and interviews conducted among health care and safety personnel in Sotkamo and Kuusamo; (3) Results: There was a clear seasonal distribution in the numbers and types of extremity injuries of non-local patients. While the risk of slipping is emphasized, other factors leading to injuries are evaluated; and (4) Conclusions: The study highlighted the clear role of wintery weather conditions as a cause of extremity injuries even though other aspects must also be considered. Future scenarios, challenges and adaptive strategies are also discussed from the viewpoint of climate change.

Abstract. 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.

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