Weather Applications > Weather Detection

• Introduction
  • What are Weather Detection Systems?
  • The Rationale for Weather Detection Systems
• System Description
  • Weather Sensors
  • How are Weather Sensors Integrated with other Weather-Related ITS Technologies?
• Assessment
  • Key Results
  • Benefits
  • Costs
  • Implementation and Operational Challenges
• Where are Weather Detection Systems Implemented?
• Case Studies
• References

INTRODUCTION

What are Weather Detection Systems?

Weather Detection Systems use different types of sensors to detect present-moment weather. The purpose of these technologies is to inform drivers and roadway personnel of real-time road weather conditions.

Weather sensors measure the following weather conditions:

• Atmospheric water vapor
• Pavement temperatures
• Fog density
• Ice levels
• Snow levels

These sensors are often integrated with other weather-related ITS technologies. Many weather detection systems can forecast weather and disseminate traveler information. Still others can forecast weather, disseminate traveler information, and maintain roads affected by weather. And still others can forecast weather, disseminate traveler information, and control traffic in adverse road-weather conditions. See our Telecommunications Diagrams on Weather Detection for more information.

The Rationale for Weather Detection Systems

There are nearly 6,600 fatal accidents annually in the United States due to poor visibility, icy surfaces, and other road weather conditions. Moreover, there are about 402,000 injury crashes nationwide because of inclement weather, one third of them occurring in rural areas. Weather detection systems can help decrease this number of accidents and increase road safety by preparing drivers and highway agencies for poor weather. Road maintenance crews can respond with greater efficiency to road weather conditions when they receive information from weather sensors. Similarly, traffic management centers can use information from weather sensors to control traffic speeds and routes, thereby enforcing safe driver behavior.

SYSTEM DESCRIPTION

Weather Sensors

Snow and Ice Sensors
Road Weather Information Systems (RWIS) are the most useful type of technologies for measuring ice and snow levels. RWIS use an environmental sensor system (ESS) to collect weather and road surface data. This sensor system consists of remote processing units (RPUs) and pavement sensors that are placed along roadways, either separate from or next to the RPUs. The pavement sensors relay information about pavement and air temperature, precipitation accumulation, and wind speed to RPUs. RPUs collect and send environmental data to a central processing unit (CPU) that is typically located in a highway maintenance facility. CPUs communicate, collect, archive, and distribute the data. The raw data are used directly or in coordination with a service provider to prepare nowcasts or forecasts.

The majority of other kinds of ice sensors measures liquid water content (LWC) or use Forward Scattering Spectrometer Probes. LWC is measured by a microprocessor that runs an algorithm that in turn inputs icing rate, velocity, and temperature. It can then output an estimate of LWC. This type of measurement can be accurate to within 30%. Forward Scattering Spectrometer Probes measure the diameter of water droplets by the forward scattering of laser light through water drops.

Fog Sensors
Nephelometers are the most widely used and cost-effective fog sensors. They measure the light scattered in a forward direction by fog particles. The sensor unit provides (1) accurate fog density measurement (2) low maintenance requirements and (3) selectable output format (visibility in distance units or voltage proportional to fog density). These sensors are designed with roadside highway use in mind; they are small and lightweight and have their light emitters and detectors contained within their housing, which eliminates the need for external lenses and windows.

A nephelometer sensor unit consists of an optical system, electronics, and software for communication to a host computer. The data obtained by the detector are digitized and transferred to the host computer. Once the host computer requests fog density data from each sensor, it determines the level of warning based on a pre-established conversion equation.

Storm Sensors
Storm sensor systems can both measure visibility and determine the type of storm (i.e. hurricane, snow etc). Visibility is measured with forward scatter detection technology. Other storm detection instruments measure wind speed and direction, precipitation amount, air temperature, relative humidity, roadway surface conditions, and the type and rate of precipitation.

Weather detection systems can also include information dissemination systems. They transmit information about road-weather conditions to agents like traffic management centers (TMC), which can in turn disseminate that information to drivers via highway advisory radio (HAR), variable messages signs (VMS), or wireless phone services.

How are Weather Sensors Integrated with other Weather-Related ITS Technologies?

Weather detection systems that can forecast weather and disseminate traveler information:

Road Weather Information Systems (RWIS) are an example of an ITS technology that monitor and detect pavement temperatures in order to predict whether precipitation will freeze on the pavement. These predictions are then used to inform drivers and traffic agencies of road weather conditions so that they can take the necessary safety precautions.

Weather detection systems that forecast weather, disseminate traveler information, and maintain roads affected by weather:

Most anti-ice technologies are integrated with RWIS. Combined, they create a system that can maintain roads in adverse weather conditions while forecasting weather and informing travelers of road conditions.

Weather detection systems that forecast weather, disseminate traveler information, and control traffic in adverse road-weather conditions:

The Tennessee Fog Detection and Warning System integrates fog sensors, speed detectors, HAR, static signs, VSL, and VMS. This system can predict heavy fog conditions and then instruct drivers to drive at a designated speed.

ASSESSMENT

Key Results

• Thus far, those weather detection systems that are integrated with other ITS weather technologies have most successfully increased safety and efficiency on roads in adverse weather.
• Travelers who receive real-time weather information from weather sensors feel better prepared to respond to adverse road conditions.
• Snow and ice sensors: These sensors accurately monitor and detect ice and snow levels.
• Fog sensors: The nephelometers successfully detect and monitor fog, and the software used to facilitate communication between the sensors and host computer is effective. Because of its highly accurate measurements, this type of fog sensor is frequently deployed at airports.
• Storm detection sensors: These sensors provide accurate data about storm types and potential storm duration.

Benefits

• Safety: Weather detection systems allow for improved emergency road response as well as increased driver awareness.

• Efficiency: Highway agencies can make more informed decisions about how to respond to adverse road-weather conditions; Travelers can adjust their travel time, delay trips, or change their mode of transportation.

Costs

• RWIS: Capital Cost: 25k, Operation and Maintenance: 0.4-2.5k per year
• ESS: Capital Cost: 10-50k, Operation and Maintenance: 1.9-4.1k per year
• VMS: Capital Cost: 10-50k, Operation and Maintenance: 1.9-4.1k per year
• HAR: Capital Cost: 16-32k, Operation and Maintenance: 0.6-1k per year

Implementation and Operational Challenges

• There is a need for improved coordination between weather detection and information dissemination systems.
• Maintaining reliable communication lines between weather sensors and TMC is especially difficult in remote or rural areas.

WHERE ARE WEATHER DETECTION SYSTEMS IMPLEMENTED?

• Weather detection projects are implemented across the United States and the world.
• RWIS are found in most US states.
• The Aurora project, which develops test standards for weather detection and forecast technologies, is an international collaboration of researchers.
• Fog detection and warning systems are found in Tennessee, Alabama, South Carolina, and California.

CASE STUDIES

Aurora

System Description: Aurora is a long-term program of collaborative research, development, and deployment of advanced technologies for detailed road and weather monitoring and forecasting. Aurora programs integrate road and weather technologies with weather monitoring infrastructures in order to forecast weather and provide real-time information to travelers. There are currently 5 completed Aurora projects and 16 on-going projects. One of Aurora’s completed projects is the Standardized Testing Methodologies for Pavement Sensors-Phase I. The purpose of this project was to establish and evaluate standard procedures for testing RWIS sensors, related software, and models. Phase I was aimed at identifying worldwide efforts to test and calibrate road weather sensors. This project determined that Aurora’s size and resources are not sufficient to fund an independent effort to develop test and calibration standards. A Phase 2 for this project has been funded and is aimed at finding ways to promote the development of national and international RWIS standards and procedures.

Source: Aurora Program

RWIS Programs

System Description: A Road Weather Information System (RWIS) uses historic and current climatological data to develop real-time road and weather information (i.e. forecasts) to roadway users. RWIS use specialized equipment and computer programs to monitor air and pavement temperatures in order to predict whether precipitation will freeze on the pavement. Sensors collect real-time data on air and pavement temperatures, precipitation, and the amount of deicing chemicals on the pavement. These are combined with information from value-added meteorological services to predict pavement temperatures for a specific area, such as a mountain pass, over a 24-hour period. These predictions are then transmitted to a computer at the highway agency's winter maintenance center. This information is critical to an effective anti-icing strategy, since deicing chemicals must be applied about an hour before the pavement reaches freezing temperatures. This prevents ice from forming on the pavement, in contrast to traditional methods in which the ice is cleared after it has already bonded to the pavement. Using portable computers linked by modem to the central computer, maintenance managers can monitor conditions and advise motorists and dispatch crews as necessary.

Source: RWIS

Tennessee Fog Detection and Warning System

In December 1990, a chain-reaction collision involving 99 vehicles prompted the design and implementation of a fog detection and warning system on Interstate 75 in southeastern Tennessee. The system covers 19 miles including a three-mile, fog-prone section above the Hiwassee River and eight-mile sections on each side.

System Description: Center managers with the Tennessee DOT and Tennessee Highway Patrol access a central computer system that collects data from eight fog detectors, and 44 vehicle speed detectors. By continually monitoring fog and speed sensor data, the computer system predicts and detects conditions conducive to fog formation, and alerts managers when established threshold criteria are met. Highway Patrol personnel visually verify onsite conditions.

The computer system provides decision support by correlating field sensor data with pre-determined response scenarios. Operational techniques include advising motorists of prevailing conditions via flashing beacons atop six static signs, two Highway Advisory Radio (HAR) transmitters, and ten Dynamic Message Signs, reducing speed limits using ten VSL signs (i.e., 50 mph or 35 mph), and restricting access to the affected highway section with ramp gates under the worst-case scenario (i.e., visibility less than 240 feet).

Results: There have been over 200 crashes, 130 injuries and 18 fatalities due to fog on this highway section since 1973. Since the installation of the fog detection and warning system in 1994, no fog-related accidents have occurred.

Source: Tennessee Fog Detection and Warning System

Maintenance Decision Support System (MDSS) Project

Project Description: The Maintenance Decision Support System (MDSS) project is a multi-year effort to prototype and field test advanced decision support components for winter road maintenance. The project’s prototype development phase is being conducted from September 2000 to September 2001, and an operational test phase will begin in October, 2001. The MDSS Prototype Development Project Plan describes the tasks, milestones, deliverables, and design elements for the development of the prototype.

System Description: This prototype will include mesoscale and ensemble forecasts, video cameras, and road weather sensors. These data will be sent to the National Center for Atmospheric Research’s dynamic, intelligent, forecast system (DICAST), which will generate point and time specific forecasts valid along road segments. The output will include probability information for each parameter. The diagnosed and forecasted weather data will be integrated with DOT operational data and passed to a road conditions module, which will generate information related to snow drifting, road temperature, and friction coefficient. These data will then be processed by a decision support system using rules of practice logic and presented to decision-makers on a geographic information system.

Sources: MDSS, April 2001 & Mahoney, William. An Advanced Winter Road Maintenance Decision Support System. Eleventh Annual ITS American 2001 Meeting.

Mobile Road Condition Sensor Project

Project Description: The objective of the Mobile Road Condition Sensor project is to develop and field test a sensor system to be mounted on winter road maintenance vehicles that (1) Detects and monitors thin films of ice, snow, water, and ice-water mixture on road surfaces and (2) Provides real-time road condition information to the vehicle operator and to road maintenance and traffic management systems. Potential applications include (1) Optimal application of surface treatments to reduce costs of winter maintenance operations and damage to the environment. (2) Provision of real time hazardous road condition information to traffic management systems, emergency response units and traveler information systems.

Sources: Mobile Road Condition Sensor Project & Maintenance Management: Mobile Road Condition Sensor Project

Fog Detection Project in Alabama

System Description: The AL DOT completed an Incident Management/Fog Detection Study on I-10 in Mobile to address the inordinate number of accidents in the area. The fog detection and tunnel management system will ultimately be expanded into a full incident management system on the seven-mile Bay Bridge and I-10 through Mobile.

Source: US DOT ITS Fog Detection Project

Fog Mitigation System in South Carolina

System Description: A fog mitigation system that monitors visibility conditions is in operation on I-526 near the Cooper River in Charleston, South Carolina. When weather conditions warrant, motorists are warned of adverse driving conditions.

Source: US DOT ITS Fog Mitigation System

Automated Local Evaluation in Real Time (ALERT)

System Description: ALERT uses remote sensors in the field to transmit environmental data about flooding to a central computer in real time. There are many types and manufacturers of ALERT hardware and software, but they are all designed to meet a common set of communications criteria, making most equipment and programs interchangeable.

Results: ALERT systems have become a standard in real-time environmental data collection because of their accuracy, reliability, and low cost. The benefits of using ALERT are a low cost/high benefits ratio, real time data acquisition and most of all, automated warnings.

Source: http://www.alertsystems.org/

Hurricane Eye

System Description: Eagle Vision II is a mobile commercial imagery satellite ground receiving and processing system, designed to process space images of hurricane damage to roads, bridges and coastlines while the storms are still under way. The unit gets images of storms from satellites, processes them within 45 minutes and conveys them to officials electronically. It uses radar to obtain ground detail even while a hurricane is in progress.

The goal of this system is to gain a fast assessment of infrastructure conditions so that emergency management staff can locate critical facilities (such as inoperable roads or bridges) and dispatch personnel. The U.S. Army Corps of Engineers is running a test of the system in the month of August, 2001 in Savannah, Ga. Operations are planned for the Southeast coast for the hurricane season of 2001.

Source: Hurricane Eye

Highway Fog Warning System

The Highway Fog Warning System is the result of FHWA's study to develop a low-cost, reliable, fog sensor. The study was aimed at developing a cost-effective highway visibility sensor that measures the density of roadway fog, is linked to traveler information systems, and could substantially reduce fog accidents.

System Description: Sensor units, containing nephelometers (devices that measure the light scattered in a forward direction by fog particles) were placed in several locations for this study. Spacing between sensor units was between 61 and 213 m (200 and 700 ft), covering the study area. With this configuration, patchy fog, as well as dense fog, could be monitored along the highway over a large area. The host computer requests fog density data from each sensor and determines the level of warning based on a pre-established conversion equation. The warning signal is then transferred to motorists through roadside displays or audio communication.

Results: Subsequent field tests have demonstrated the ability of the fog sensor to accurately determine fog density in a highway environment. Both laboratory and field tests have shown that this device can withstand the extreme temperature ranges, heavy rainfall, and blizzard conditions. Refinements based on field-testing experiences have been incorporated into the latest design of this device. Specific improvements in the production and testing of future sensors have been proposed.

Source: Highway Fog Warning System,Techbrief, April 1999

Idaho Storm Warning System

The Storm Warning Project was initiated in 1993 as a result of a large number of serious traffic crashes that occurred during periods of low visibility on I-84 in southeastern Idaho between 1988 and 1993. The purpose of the operational test was to investigate various sensor systems that could provide accurate and reliable visibility and weather data.This data would then be used to provide general warnings, speed advisories, and possible road closure information to travelers on a section of I-84 in southeast Idaho that is highly prone to reduced visibility from blowing snow and dust. The primary goal of such a system is a major reduction in visibility-related multi-vehicle accidents in rural areas.

System Description: Sensors measuring traffic, visibility, roadway, and weather data were installed at the test site, and automatic traffic counters recorded the lane number, time, speed, and length of each vehicle passing the sensor site. To confirm visibility readings provided by the sensors, a video camera was installed at the test site and aimed at a series of target signs placed along the interstate at various known distances. During the course of the project, four variable message signs (VMS) were installed along the test section of roadway, to provide information to travelers regarding low visibility and other road condition information in the test area. Data generated by the sensor systems was transmitted to a master computer, which recorded readings every five minutes. This information provided a baseline of driver behavior, to help determine if the signs were causing drivers to change their behavior. Information was transmitted to the motorist via changeable message signs. Cost: Estimated Total ITS Funds: $804,500 Estimated Total Project Cost: $1,231,900.

Source: Idaho Storm Warning System Operational Test, Final Report, Idaho Transportation Department, December 2000.

REFERENCES

Idaho Storm Warning Project Operational Test, Final Report, University of Idaho/Boise State University, December 2000. Link to Report

Mahoney, William. An Advanced Winter Road Maintenance Decision Support System. Eleventh Annual ITS American 2001 Meeting.

Prospects from the Acquisition of Icing data from Operational Aircraft, Final Report, US DOT Office of Aviation Research, September 1999. Link to Report

Surface Transportation Weather Decision Report Requirements, Preliminary Interface Requirements/Draft version 2.0, Mitretek Systems, Inc., October 2000. Link to Report

Links

California fog warning system
http://www.dot.ca.gov/dist10/pr01.htm

Author: Lauren Smith, last update: 11/01/01