In this study, profiles of temperature and humidity (<250 cm above the road and 5 m into the surroundings) have been used to examine the development of temperature differences in the air layer close to the road. Temperature, humidity and wind profiles were measured, together with net radiation and observations of road surface state, at a test site at Road 45, Surte, Sweden. Measured temperature differences were compared with present weather, preceding weather, surface status, wind direction and other parameters thought to be important for the development of temperature differences. The results showed that large temperature differences (1-3 °C between 250 cm and 10 cm above the road) occurred when there was a high risk of slipperiness caused by hoarfrost, snow or ice on the road. The temperature differences between different levels were associated with the exchange of humidity and temperature between the air layer and the road surface. The 10 cm level reflected the surface processes well. Higher levels were influenced by the surroundings because of turbulence and advection. This study emphasises the need for measurements to be taken at a height and place that reflects the processes at the road surface.

The object of this study is to examine the possibility of developing a model that can determine road surface adjustment times to new conditions after weather changes. Situations where the weather changes from clear to overcast conditions are studied in order to produce a worst-case scenario. One hypothesis tested is that the decrease in road surface temperature (RST) differences between shaded and sun-exposed stations on clear days can be used to determine the reduction in RST difference on days with weather changes. A relationship that describes the variation in RST difference as a function of time of day and season is developed. For clear days a fourth-order polynomial can describe the relationship between RST differences and time of day. The coefficient in the polynomial depends on maximal solar elevation. Validation of the model for clear days showed that the deviation between modelled and measured values varied between 0.3 and 0.5 °C. It was also possible to use the clear day relationship to predict the decrease in RST differences for situations with weather changes, but during these situations the deviations between modelled and measured values were higher and also tended to show a positive bias.

The variation of road surface temperature across a road network is influenced regionally by meteorological parameters and locally by geographical parameters. A fast and reliable technique is described which allows the continuous collection of high resolution, geographical data including the sky-view factor which is suitable for use in road climate modelling studies. Then, by use of regression analysis, the relative importance of five geographical parameters (altitude, topography, sky-view factor, landuse and road construction) is assessed with respect to road surface temperature and atmospheric stability. Results show that sky-view factors dominate surface temperatures at high atmospheric stability whereas altitude becomes increasingly important as stability decreases. Finally, a statistical road surface temperature model is discussed with the ability to explain up to 75% of the variation of residual road surface temperatures in the study area entirely by the interaction of geographical parameters.

A new ice prediction strategy is presented based on the numerical modelling of surveyed geographical parameters. This approach enables the thermal projection of road surface temperatures across the road network entirely by model predictions and without the need for thermal maps. The influence of eight geographical parameters (latitude, altitude, sky-view factor, screening, roughness length, road construction, traffic density and topography) is investigated by means of sensitivity analyses. The sky-view factor is highlighted as the dominant control on road surface temperature, particularly at high levels of atmospheric stability. A numerical road weather model incorporating all eight parameters was run over 20 nights using forecast and retrospective meteorological data. The model has the ability to explain up to 72% of the variation in road surface temperature purely by thermally projecting surface temperature using geographical variables. Retrospective results produce an average r.m.s. error of 1 °C which is comparable to existing UK road weather models.

Many northern countries use a road weather information system (RWIS) with a network of stations to monitor winter road conditions. Present station locations were selected after field investigations of micro- and local-climate conditions (e.g. using thermal mapping). This paper describes an approach to optimally locate and equip the stations in order to best identify conditions hazardous to road transport. This is achieved using multiple regression analysis of observed data and correlation with location meta-data. A geographical information system (GIS) is used to develop quantitative and objective descriptions of station locations by using knowledge of local and regional climate variations. Road climate is described using a slipperiness classification, in which weather situations are classified into ten types of slipperiness from the meteorological variables collected at RWIS stations. The relationships between quantified locations and data on road slipperiness in southern Sweden during one winter are analysed. The results show that the spatial patterns for different types of slipperiness are significantly related to local parameters. The three most prevalent types are analysed in detail: snowfall on a frozen road surface, hoarfrost and low visibility, and strong formation of hoarfrost.

There is a severe risk of road icing and slipperiness when rain or sleet falls on a frozen road surface. Traffic can be severely affected, with a higher frequency of road accidents. These conditions usually occur during warm frontal passages when cold weather and low road surface temperatures are replaced by warmer air and precipitation. Two case studies from southern Sweden show the spatial and temporal distribution of rain or sleet on a frozen road surface. The influence of regional-scale characteristics is demonstrated. Air temperature governs the spatial distribution when temperatures around -10 °C precede the event. Weather stations in an open environment experience slippery conditions first. If temperatures are just below zero before the front arrives, road surface temperature becomes the crucial factor. Weather stations in locations screened by forest or road cuts are the first to register this event. The methodology presented in this paper can be used to increase knowledge of the spatial and temporal patterns of road slipperiness in an area. As a result, forecasts and maintenance activities can be improved and there is a better understanding of the relationship between synoptic events and road slipperiness.

This study considers night-time urban air and road surface temperature variations under different synoptic weather conditions. Five years of winter weather data from a single weather station were classified into groups with similar air-mass characteristics. Using a two-stage process of principal components analysis followed by cluster analysis, the resulting clusters were used to sort paired air and road-surface temperature observations recorded at 2100hr from the West Midlands road weather information network into days with similar meteorological controls. The six resulting clusters have good between-group variation and feature a dominant wind direction. The results show that the difference between air and road surface temperatures is significant except during strong westerly conditions. Days featuring clear and anticyclonic weather showed the greatest between-site variation and evidence of the urban heat island effect. Under these conditions, road surface temperatures were warmer than the air - the opposite of what one would normally expect in rural areas. Those days with wet and windy weather were found to have the least between-site variation, excepting that due to altitude. The presence of a high vapour flux in the urban environment literally dampens the temperature response.

The need for innovative snow and ice control techniques and processes has continued to grow as national and European road networks have developed substantially over recent decades. The demand for improvement, including the sophistication of the techniques and technology used, continues to be driven by the increasing need for safe and efficient national and international road freight and passenger transport and by the environmental and other policies affecting highways (roads and bridges).

The European Commission project ‘COST Action 344: Improvements to snow and ice control on European roads and bridges’ started in April 1999 as a three-year project, with participation from 18 European countries - Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Hungary, Iceland, Ireland, Norway, Poland, Romania, Slovenia, Spain, Sweden, Switzerland and United Kingdom.

The project's aims are to:

1) review existing international practices,

2) define snow and ice control requirements in different European climatic regions,

3) specify ‘Best Practice’ in different European climatic regions,

4) develop guidelines for the integration of specified snow and ice control methods into network level road management and maintenance systems,

5) recommend improvements to driver information and traffic management systems, and

6) recommend future research projects.

This COST Action will promote the exploitation of technological advances in the application and distribution of snow and ice control measures, with a view to providing significant environmental and safety benefits and lower operational costs. Millions of ECUs could be saved through lower operational costs and a reduction in adverse effects on the highway infrastructure and the environment. For road users, more effective management of winter operations could lead to a reduction in traffic delays and accidents. For practitioners, implementation of ‘Best Practice’ should enhance standards and lead to ‘best value’ being achieved. The implementation of best value could provide the means to measure the performance of the winter maintenance service within various road administrations. For widespread dissemination of knowledge, interim results of the COST Action are being made available to European and national policy-makers, regional planners, engineers, road and vehicle operators, industry and academia. The internet, a CD-ROM, email, handbooks and events such as workshops, conferences and seminars are also being used to target a wider audience of practitioners and researchers.

The difference between air and road surface temperature in urban and rural areas is an important consideration when modelling the road climate. In this study the effect of the urban heat island in the Stockholm area on road climate is examined. Factors such as distance from the city centre, traffic and topography are analysed in order to assess their impact on the spatial variation of road and air temperature.

Of the full set of observations carried out at weather stations, the ‘present weather’ observation is one of the most difficult to automate. Initially, elements such as rain and fog were automated. However, during the past ten years a new group of sensors, aimed at identifying the type and intensity of precipitation, has emerged. These sensors have come to be called Present Weather Sensors (PWSs). Most of the early tests and intercomparisons have been limited to liquid precipitation. Not much attention has been given to investigating the reasons for disagreement between the primary reference, the professional meteorological observer, and the automated observations. This paper presents an analysis of an intercomparison between observer and one type of PWS (Vaisala FD12P), in conditions of both liquid and frozen precipitation. An interaction model is introduced as a new and improved tool for the quality control of PWSs.

Fog is the most important physical process that reduces surface visibility, having a major negative impact on air traffic. In this study, a one-dimensional boundary layer model was developed in order to predict the occurrence and intensity of fog. The model uses a selection of parameterisations, suggested by different authors, and uses the finite element method for the vertical discretisation of the equations. The 1977 Cabauw fog event is used to validate the model and eight fog events at Lisbon Airport are simulated. The validation shows that the model is able to predict realistically the fog's onset, growth and dissipation. The fog forecasts at Lisbon Airport are, in general, quite reasonable, and a simple simulation of a fog event, where advection played a fundamental role, also gives realistic results.