applied climatology

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www.gu.se 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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Page 1: applied climatology

www.gu.se

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/

Page 2: applied climatology

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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

Page 3: applied climatology

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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

Page 4: applied climatology

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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

Page 5: applied climatology

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Air temperature

Relative humidity

Wind speed

Precipitation - quantity and type

Surface temperature

Surface conditions

Weather parameters of interest

Page 6: applied climatology

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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

Page 7: applied climatology

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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

Page 8: applied climatology

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Sensitive areas:

● Main roads● Bridges● Valleys● Places near bigger lakes● Outskirts

Location of stationary stations

Page 9: applied climatology

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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

Page 10: applied climatology

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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

Page 11: applied climatology

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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

Page 12: applied climatology

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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

Page 13: applied climatology

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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, Pär Ekström & Magnus Andersson; Semcon ABGothenburg University, Sweden

Page 14: applied climatology

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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

Page 15: applied climatology

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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?