applied climatology

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    Road Weather Information System Project work 3, Applied Climatology

    Group 3

    Tomas Barzdenas

    Dimitri Castarde

    Dalia Grendaite

    Sara Lidn

    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 measurementsVaisala Remote Surface State Sensor DSC111

    Spectroscopic measuring principle, individuallyidentifying 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:WaterIceSlushSnow and Frost on the road

    Ts + fog detection (Visibility) Freezing fogVisibility measurements Fog/bad visibilityWind 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 measurementsVaisala Surface Patrol HD Pavement Temperatureand 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, aquaplaningPrecipitation + temperatures (~+1 - -2 ) Icy roadsTa, RH and Ts Hoarfrost/rime on roadTs + fog detection (Dew point and Ta) Freezing fogTemperature +1 - -2 Slippery due to IceTa, RH (Dew point) Fog, bad visibilityAmount of precipitation +Ta Snow amount on the roadIf 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 humidityWind 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; KlimatorAnders Johansson, Pr Ekstrm & Magnus Andersson; Semcon ABGothenburg 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?

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