The document describes a Road Weather Information System (RWIS) that consists of: 1) Stationary weather stations located in high-risk areas to measure conditions like temperature, humidity and road conditions. 2) Mobile sensors on vehicles that also record temperature, humidity and detect conditions like ice and fog. 3) A forecast system that predicts conditions using models and data from the stationary and mobile sensors.

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Road Weather Information System
Project work 3, Applied Climatology
Group 3
Tomas Barzdenas
Dimitri Castarède
Dalia Grendaite
Sara Lidén
https://www.flickr.com/photos/timopfahl/6056441507/

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Our Road Weather Information System (RWIS) in short
Weather situations of interest for our RWIS
Weather parameters of interest
Stationary Measurements
Mobile Measurements
Forecast system
Maintenance
Outline

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• Consists of stationary stations on locations of great risk of
slipperiness
• Mobile measurements taken by cars and other vehicles
traveling the roads
• Together with weather forecasts it is possible to get forecasts
of the upcoming road climate.
Our RWIS

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Dew formation during freezing conditions
Frost
Snowfall and drifting snow
Wet snow
Rainfall during or followed by colder temperatures
Heavy rainfall
Fog
Strong winds
Weather situations of interest

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Air temperature
Relative humidity
Wind speed
Precipitation - quantity and type
Surface temperature
Surface conditions
Weather parameters of interest

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Type of sensors for stationary measurements
Vaisala Remote Surface State Sensor DSC111
• Spectroscopic measuring principle, individually
identifying the presence of:
Water / Ice / Slush / Snow or Frost
sensing technology
• Infrared surface temperature sensor
Measures following parameters:
• surface and air temperature
• surface depth temperature
• relative humidity
• visibility
• wind speed and direction
• atmospheric pressure

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Spectroscopic measuring → Presence of:
Water
Ice
Slush
Snow and Frost on the road
Ts
+ fog detection (Visibility) → Freezing fog
Visibility measurements → Fog/bad visibility
Wind speed → Strong winds
Road conditions from stationary stations

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Sensitive areas:
● Main roads
● Bridges
● Valleys
● Places near bigger lakes
● Outskirts
Location of stationary stations

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Type of sensors for mobile measurements
Vaisala Surface Patrol HD Pavement Temperature
and Humidity Sensor with Display DSP200 Series
• Infrared pavement temperature sensor
• Capacitive polymer relative humidity sensing technology
Measures following parameters:
• surface and air temperature
• relative humidity
• dew point temperature
Along with frequency of
windshield wipers from the cars

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Precipitation amount → Slippery, aquaplaning
Precipitation + temperatures (~+1 - -2℃ ) → Icy roads
Ta
, RH and Ts
→ Hoarfrost/rime on road
Ts
+ fog detection (Dew point and Ta
) → Freezing fog
Temperature +1 - -2℃ → Slippery due to Ice
Ta
, RH (Dew point) → Fog, bad visibility
Amount of precipitation +Ta
→ Snow amount on the road
If snowfall in temperatures >0℃ → Wet snow
Road conditions from mobile measurements

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Companies with large vehicle fleet are of interest like:
- Taxi companies in the cities
- Postal vehicles
- County/municipality owned vehicles
- Delivery trucks
- Rental cars
Location of Mobile Sensors

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Forecasted by for example MET Norway :
Air temperature
Relative humidity
Wind speed
Precipitation - quantity and type
However the surface temperature of the road also needs to be forecasted.
Therefore, another forecast system is needed for this parameter.
Today, the most efficient model to predict surface road temperature is a statistical
model. This kind of model is pretty accurate but can not predict the extremes.
A better way to predict this parameter would be an Energy Balance Model EBM
From these parameters and using the same calculations as seen before, a
prediction of the road conditions can be done. The forecast system has to be
updated several time a day
Forecast system

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Forecast system
Figure : Slipperiness probability associated with different types of
weather
Knowing the type of weather, we can know the probability of slipperiness using the
coefficient below :
The same kind of coefficient can be done for the visibility
By knowing the weather situation and possible road conditions in upcoming days, roads
can be closed or salted in advance
Reference : SIRWEC-BiFi-Bearing information through vehicle intelligence T. Gustavsson & J. Bogren Department of Earth Sciences; Klimator
Anders Johansson, Pär Ekström & Magnus Andersson; Semcon AB
Gothenburg University, Sweden

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• GPS and SIM card
• Data sent to a cloud database
• Automatic data checking
• Smartphone app with warning system of present conditions and forecasts
Gathering and providing information

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Our RWIS
• Consists of stationary stations on locations of great risk of
slipperiness
• Mobile measurements taken by cars and other vehicles
traveling the roads
• Together with weather forecasts it is possible to get
forecasts of the upcoming road climate.
Questions?