Road weather forecasts for winter operations
A SaaS platform purpose-built for winter maintenance agencies and airports. Route-specific forecasts, treatment recommendations, work order dispatch, and compliance logging in one operational view.
Road weather forecasting, from atmosphere to asphalt
Road weather forecasting tells you what is happening on the surface of the road. It combines atmospheric forecasting with physical road modeling to predict wet, dry, snowy, and icy conditions before they form.
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- 40 +
years of road weather expertise
- 5 +
millions of kilometer of roads modeled across North America, Europe, Japan, and beyond
- 500 +
winter maintenance agencies and automotive partners relying on Xweather.
Road weather forecasts from the leading provider
Road-specific forecasts
Wet pavement, snowfall, sleet, poor visibility, and high wind all change how a vehicle handles the road. It's estimated that every fifth road accident in the US is weather-related. Poor road conditions don't just slow people down, they raise the risk of accidents, injuries, and fatalities.
Most organizations trying to manage that risk are still working from a general weather forecast, one built to describe the atmosphere, not the road surface underneath it.
Decades of road weather expertise
Vaisala Xweather road weather modeling is built on 85+ years of Vaisala sensor development and more than 30 years of road weather forecasting
General forecasts miss what happens on the road
Regional forecasts don't reach the pavement
A general forecast covers a wide area and can't account for the elevation changes, bridges, shaded curves, and other microclimates that make road conditions vary block by block. It can tell you it's going to rain. It can't tell you whether that rain will freeze on a bridge deck two kilometers down the road.
Conditions change faster than typical forecasts update
General forecasts refresh every one to twelve hours. Road surface conditions can shift from wet to icy in far less time than that, especially overnight or in microclimates prone to sudden temperature drops.
Limited parameters, limited decisions
Knowing it will snow isn't the same as knowing how much will accumulate on the road, how much grip a vehicle will have, or how likely it is to aquaplane. Winter maintenance teams and vehicle systems both need more detail than a standard forecast provides to act with confidence.
How road weather forecasts are generated
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Road weather forecasts combine data from multiple sources.
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Process
Atmospheric weather forecasts and road configuration information feed into a road weather model that outputs road-specific snow depth, water film thickness, and ice thickness on the road surface.
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Forecast
These outputs are combined into a final forecast covering road surface conditions, fog, blowing snow, and risks like aquaplaning or rollover.
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Millions of kilometers of road covered
Coverage spans roads across North America, Europe, Japan, South Korea, Australia, and New Zealand.
Atmospheric weather forecasting
Every road weather forecast starts with atmospheric weather forecasting, a four-step process refined over decades of meteorological science.
Global measurement
Weather measurements are collected by every country in the world, and the basic measurements are shared freely between nations.
Global modeling
Governmental organizations run global numerical weather prediction (NWP) models on supercomputers, taking those measurements as input. These models typically produce a new forecast every six hours.
Local refinement
National weather institutes, such as the National Weather Service in the US or the Deutscher Wetterdienst in Germany, enhance the global models at the local scale by increasing the spatial and temporal resolution.
Nowcasting
The latest satellite, radar, and weather station data are folded in through nowcasting, forecasts that refresh at least once an hour and extend only a couple of hours ahead, capturing conditions as they change in real time.
Road weather modeling
Atmospheric data alone can't tell you what's happening on the pavement. That takes a physical road weather model built from two components: an energy-balance model and a mass-balance model.
- The energy-balance model
Calculates road surface temperature. Anyone who has walked on a black road in the sun knows it heats up faster than the air around it. The model accounts for solar heating and every other physical factor that affects pavement temperature, including how heat flows into and out of the ground, to produce an accurate estimate of road surface temperature.
- The mass-balance model
Tracks how much water, snow, ice, and de-icing material, like salt, sits on the road at any given time. When the atmospheric model predicts rain, water is added to the water reservoir. When it predicts snow, snow is added to the snow reservoir. When the energy-balance model shows the road surface dropping below freezing, material shifts from the water reservoir to the ice reservoir. The model also accounts for how material leaves the road for example, through traffic spray, evaporation, run-off, or plowing, and adjusts the reservoirs accordingly. The contents of these reservoirs are what determine whether a road is forecast as wet, dry, snowy, or icy.
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Continuous improvement against ground-truth
The road weather model accounts for all processes that add and remove water, snow, and ice from the road surface. Forecasts are continuously refined by validating model output against ground-truth measurements from road weather stations and mobile sensors.
The modeling draws on 85+ years of Vaisala sensor development and more than 30 years of road weather forecasting.
Where road weather forecasting matters most
Automotive and connected vehicles
Road awareness built into the vehicle
Automotive manufacturers and mobility technology companies integrate road weather data directly into autonomous driving, navigation, and driver-warning systems. Probability-based surface condition data gives developers the confidence levels they need to build safety and comfort features that respond to real road conditions, already in production in vehicles on the road today.
Winter maintenance
Confident treatment decisions across the network
Winter maintenance agencies, cities, and airports use route-specific road weather forecasts to know when and where to treat, not just when it's going to snow. Forecasts are combined with ground-truth sensor data to reduce short-term error and support treatment plans, work orders, and compliance logging.
Road weather intelligence, however you need it
Road weather data via Weather API
Real-time and forecast road surface conditions delivered through a RESTful API, built for automotive navigation stacks, ADAS logic, and mapping and planning engines.
Visualize road weather conditions with MapsGL
Interactive road weather visualization for dashboards, navigation overlays, and development environments, with dedicated layers for surface condition, grip, and temperature.
Bring road weather intelligence to your operation
Whether you're managing a road network or building the next generation of connected vehicles, Xweather road weather forecasting gives you the surface-level detail a general forecast can't.