Road Weather Forecasting

How Full Coverage Road Weather Data Transforms Weather Forecasting

Each year in the United States, inclement weather accounts for approximately 7,400 deaths and 700,000 injuries. This human toll is compounded by an estimated $42 billion dollars in economic loss. As our current technologies have developed, efforts to mitigate the impact of inclement weather on our nation’s roadways are being implemented. The largest thrust of these efforts is toward harnessing full coverage road weather data. Leveraging this data will enable traffic managers to foresee hazardous road conditions as they arise, and give them the information they need to proactively address changing road conditions.

Ground-Based Sensors

Efforts by state and national Department of Transportation (DOT) agencies to use ground-based sensors to provide road weather data have been relatively successful. However, there are some drawbacks to this system. Agencies use Environmental Sensor Stations (ESS) as part of a larger Road Weather Information System (RWIS). ESS installations collect local weather data, atmospheric conditions, road conditions, traffic patterns, and sometimes water levels of adjacent streams and rivers. This information is then uploaded to a local, regional, or national RWIS network and made available to transportation managers.

The first drawback to ESS and RWIS systems is their lack of coverage. There are currently only about 2,400 ESS nodes in place across the United States. This means that ESS locations monitor road conditions for only a small fraction of the nation’s road network. The second drawback of the ESS based system is the speed that data is collected and how that data is presented. RWIS networks attempt to fill in the gaps left by ESS networks by combining traditional weather forecasts and spatial predictions based off of information gathered from ESS locations. Because these systems rely on inference and prediction, they are inherently inaccurate when applied to hyper-local weather data. Generating predictions through combined spatial algorithms and weather forecasting takes time, and creates a lag between when information is collected and presented to transportation managers.

One solution to these deficiencies is the use of smartweather professional weather stations.  Smartweather stations collect much of the same data as ESS locations. However, they are significantly cheaper to deploy. Reduced cost means that more smartweather stations can be placed along roadways, filling the gaps left by traditional ESS. Additionally, smart weather stations can collect and transmit data from vehicle-based weather stations, and upload that data to cloud-based analytics software in real-time. Smart weather stations are one part of a broader network used to generate full coverage weather data in real-time, closing the gaps left by traditional ESS and RWIS networks.

Vehicle-Based Sensors

The Federal Highway Administration, combined with state and local agencies, are also beginning to utilize vehicle-based weather stations to provide data for road conditions. These vehicle-based weather stations are often being contracted through the private sector, where advances in technology and manufacturing have brought costs down and allowed a broader implementation.

Vehicle-based sensors are most commonly attached to the bumper or cabin of DOT or EMS vehicles. These sensors then collect data about current weather and road conditions, such as precipitation, ambient temperature, barometric pressure, and road friction. This information is then combined with data gathered at other sensor points throughout an RWIS network and provided to traffic managers to give the complete picture of on-the-ground weather conditions.

Currently, the drawbacks to vehicle-based sensor systems are a lack of widespread implementation, a high cost, and lack of integration with roadside weather stations and sensors. Private-sector companies, such as Fathym, have addressed these shortcomings by developing systems that are significantly cheaper to deploy and work seamlessly with roadside smart weather professional weather stations. Fathym’s weather systems highlight the advantages of using a private sector developed road weather monitoring system. Fathym’s weather system utilizes both vehicle and roadside based sensors to collect data. This data is then transmitted in real-time to cloud-based analytics software that filters the data down and transmits it to data dashboards that are accessible from any mobile device. This approach closes the gaps left by traditional ESS and RWIS systems while providing seamless full coverage road weather data. Furthermore, this type of system is fully expandable and customizable. Additional vehicle-based sensors can be added as fleet size grows, without the large overhead costs of other systems. Additional data sources can be fused together to create a broad view of current conditions and more accurate forecasts and routing.

Effective full coverage road weather data requires a mesh system of integrated vehicle-based and stationary weather sensors. This type of system is specifically designed to close the gaps left by traditional radar and sensor-based weather forecasting, by providing hyper-local real-time weather data. Implementing full coverage weather data systems gives transportation managers the information they need to proactively address changing road weather conditions as they arise.

Introducing the Fathym Forecaster

The Fathym Forecaster is a robust, feature-rich API that offers a powerful suite of weather forecasting and open-source data visualization tools. The Forecaster combines the world’s best weather forecasts with statistics-based, machine-learning techniques to tackle the largest datasets, including road weather.
The Fathym Forecaster offers developers comprehensive weather forecasting capabilities over freely chosen locations and routes across the globe, with any combination of variables. The API delivers a unique suite of highly specialized forecast variables derived through statistically based machine learning models.
Get started exploring the forecaster’s capabilities today for free.

Scroll to Top
Thinky Octopus Logo

This website uses cookies to ensure you get the best experience. By continuing to use our site, you are consenting to our use of cookies.

Share via
Copy link
Powered by Social Snap