1. The FMI Road Weather Model, Applications and Projects
Marjo Hippi
Meteorological research / Meteorological applications
Finnish Meteorological Institute

2. Background and motivation
Weather warning service
road/sidewalk weather forecasting
issuing road/sidewalk weather warnings
Research
road maintenance needs
effect of climate change
Products
road maintenance scheduling
specialized warning system
route planning

3. End users of Road Weather Model (and its applications)
Meteorologists
Road maintenance people
Drivers
Walkers
Benefits of the model
Safety on roads
Less injuries
Less maintenance costs

4. Numerical model . . . . . . . . . 3-D 1-D 2-D
Atmosphere : weather model - weather parameters
3-D
10 km
Road model (1-D)
10 km
1-D
Road surface
Ground
4 meters
2-D
"Deep" ground
Climatological temperature

5. Model structure Atmosphere wind speed (Vz)
Upper boundary forcing
Model structure
Atmosphere
wind speed (Vz)
air temperature and humidity (Ta , Rh)
global (short wave) radiation (RS)
incoming long wave radiation (RL)
precipitation (P)
Traffic
mechanical wear, heating
Turbulence
natural
traffic induced
Surface heat exchange
sensible heat flux (H)
latent heat flux (LE)
long wave radiation (RL)
stability
Ground heat transfer
heat conductivity ()
specific heat (c)
density ()
porosity ()

6. Road surface temperature
Outputs:
Input data:
temperature, humidity
wind speed
precipitation intensity
lighting conditions
Traffic conditions
Traffic index
normal
bad
very bad
Road index
1. dry
2. damp
3. wet
4. wet snow
5. frost
6. partly icy
7. dry snow
8. icy
Simulation
Road condition
surface temperature
storages
- water, snow
- ice, frost
Temperature
Road surface temperature

7. Observation phase (3-48 h) Forecast initial state
Model run
Present time
Observation phase (3-48 h)
Forecast phase (24-48 h)
SYNOP, radar precipitation
Hirlam, ECMWF etc.
Input data :
Observations
Forecast (meteorologist's editor)
Simulation :
Road weather model
Road weather model
Forecast initial state
Model output
Input data
latest observations and forecasts
"data pool" updated automatically by a data fetch agent
Data
Base

8. Surface energy balance
Equation: G
= heat transfer into ground H
= sensible heat flux α
= ground reflectance LE
= latent heat flux RS
= global radiation
Wphch
= melting/freezing RL
= long wave radiation
Wtraff
= traffic heat generation

9. Effect of traffic on road surface
traffic wear
E.g. snow packed partly to ice, partly flown away
adjusted to main road network
day/night variation in values
can easily be adjusted if more detailed data becomes available
traffic friction and other warming effects
turbulence simulated by having minimum wind speed
friction heating included in the equations (not used so far)
0.5 1
1.5 2
2.5 3
3.5 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time / hrs
mm eq. water
snow storage
ice storage
snowing
Storages are one of the most important part of this model. There are storages for water, snow, ice and deposit.

10. Road condition : interaction between storages
SNOW
ICE
WATER
Wear
Freezing
Melting
ROAD :
COND :
dry snow
snow
+ ice
wet snow
ice
Traffic induced drifting
COND 2 :

11. Meteograms Temperature road temperature air temperature air dew point
snow
icy
partly icy
frost
wet snow
wet
damp
dry
Road index
Temperature
Precipitation
Radiation
Wind speed
Wind
Temperature (air)
Dew point (air)
Temperature (road)
water (mm/h) ; snow (cm/h)
long wave (W/m2)
water (mm) ; snow (cm)
short wave (W/m2)
INTENSITY :
Traffic idx :
Meteograms
Temperature
road temperature
air temperature
air dew point
Incoming radiation
long wave
short wave
Precipitation and storages
water prec. mm/h ( ), mm ( )
water storage (- -)
snow prec. mm/h ( ), mm ( )
snow storage (--)
ice storages (-o- ; --)
Road index
primary
secondary ooooooo
: SUM | STOR x 10

12. Meteogram vs. road weather camera

13. Road condition maps Feb 6 12:00 16:00 20:00 24:00 Dry snow Wet snow
Ice
Partly icy
Frost
Wet snow
Wet
Damp
Dry
Road condition maps
Feb 6
12:00
16:00
20:00
24:00

14. Traffic condition index
= Very difficult
= Difficult
= Normal

15. Road weather models, applications and projects
Four kind of road weather models
Normal road weather model
Road maintenance model
Pedestrian model
Coming: Road weather model using observations from road weather station
Applications
Frost model : Analysis model, based on observations from road weather stations
Icing model : Different kind of observations and/or forecasts give warnings
Varo service : Special localized warnings and advanced route planning based on predicted road weather
Projects
Helsinki Testbed : Urban measurement network
ColdSpots : More accurate forecasts for difficult road spots – verifications and model developing
CarLink : Wireless Traffic Platform for Linking Cars – Weather observations from cars, data transfer, …

16. Pedestrian sidewalk conditions
J. Ruotsalainen, R. Ruuhela, M. Kangas
FMI, Inst. of Occupational Health*
Warning of slippery walking conditions
Modified surface condition interpretation
Hospital preparedness
*) Työterveyslaitos
Foot gear friction measurements using a "stepping robot"

17. Pedestrian model – Background and Facts
During wintertime in Finland occur about pedestrian slipping accidents with serious consequences
1/100 in Finnish population
About patients (1/1000) are hospitalized
Annual costs 420 million euros

18. How to reduce the number of slipping accidents?
Winter maintenance of pavements
Awareness of pedestrians
Foot wear with good grip
Warnings of slippery pavement conditions would help both pedestrians and winter maintenance work
Peak days of traffic accidents are not usually the same as peak days of pedestrian slipping accidents
Friction between a tyre and road is different from the friction between foot wear and the underfoot surface

19. Some statistic

20. Age distribution of patients with slipperiness injuries
Number of slipperiness injuries
Number of slipperiness injuries outside during winter time from Töölö Hospital Emergency

21. Age distribution of patients with hip fracture due to slipperiness accidents
Number /
Age /

22. Development of the Road Weather and Pavement Condition Model
Changes in storage terms were adjusted for pavements
e.g. snow -> ice, maintenance
Three-valued index for slipperiness was developed
Normal, slippery, very slippery
Most slippery conditions for pedestrians are
Light, dry snow on the smooth layer of ice
Water on the smooth layer of ice
Melting
Raining
Humid, heavy snowfall made slippery by
- pedestrians
- or wrong maintenance equipments
The pedestrian model was taken in operational use winter
The service was extended to cover whole Finland

23. Road maintenance scheduling
Co-operation with FMI and Finnish Road Enterprise*
Enhanced snow manipulation
Advance warning of snow accumulation for maintenance scheduling
Snow removal (ploughing) included in the model
Coming later: scheduling for salting
New style of thinking: Model does not predict the weather, it predict what should be done
Time to next snow removal
*) Tieliikelaitos

24. VARO service - Driver Alert for drivers
FMI (M.Hippi, M.Kangas), FMI/Cust.Serv., Finnish Road Enterprise, Road Authorities, VTT, Telia-Sonera, transport companies
Special localized warnings
Road model
rapidly changing weather, freezing rain, heavy snowfall, etc.
mobile phone based localization of the vehicles
warnings come to users via mobile phone, to the car navigators in the near future
warning to those who are in the warning area
Also advanced route planning based on predicted road weather *

25. VARO service - Route planning

26. Project : ColdSpots
Co-operation with FMI, Foreca and Finnish Road Enterprise
Funding from MINTC (Ministry of Transport and Communications), partners and Finnish Road Authority
Initiated after a serious wintertime road accident
Objective to further improve winter weather and road condition forecasts
Concentrating in the problem points of the Finnish road network

27. ColdSpots : Benefits and risks
Less traffic accidents, saving money and lives
Winter road maintenance becomes more efficient
Scheduling and planning maintenance actions becomes easier
We take a risk on the quality of new forecasts. As this is a pilot project, we do not know how much improvement (if any) can be made
What we want to do:
During this project we do also friction measurements and thermal mapping along the roads (Vaisala’s optical measurements)
more accurate forecasts on road conditions
more effectively warn to drivers, especially about the problem spots
want to learn how much the road conditions differ locally along the road network and why
want to learn more about the influence of weather to road accidents

28. What is a ColdSpot? A spot with accidents due to slipperiness
Or a spot which is difficult for road maintenance people
Can be an open area -> large sky-view factor, radiation cooling
A valley with cool air pooling at night
Coastal area near the sea or lake -> lots of moisture advection
Elevated spot, a hill top -> lower temperature, forced uplift of moving air (not a common problem in Finland)
A bridge, curve, ramp, …
Many spots have passing lanes (a cause or a result?)

29. How much temperature can differ in near situated road weather stations
Distance between road weather stations is about km.

30. Part II

31. How does a ColdSpot look like?
Aneriojärvi: an open area, a lake on the right
Ikela hill: an open area ending to a hill
Bridge of Halikko may be slippery, strong wind can cause extra risk
Curve of Koikkala: Road curving on a hill – poor visibility

32. ColdSpots do not look like much but they may kill you...
Drivers cannot sense the danger while driving
One good way to warn: variable signs

33. Conclusions about FMI:s road weather modeling
The basic road weather model operative since 2000
a total of 66 model runs/day
Worked well, stable and reliable
Spin-offs and model developments
special traffic and pedestrian warnings
Cust.Services : commercial products
road maintenance, VARO etc.
Interest from abroad
Interest from Lithuania, Czech, Luxemburg, Barbados (considering mud slides)...

34. ... and future Road weather observations
improved localization
Ideas : friction output, EPS, road salting, feedback from maintenance vehicles, …
Problem : radiation observation availability
FMI : decrease of cloud/radiation observations since summer 2006 => analysis to grid impossible
=> no more radiation observations for the model
radiation observations replaced by forecasts
forecast quality ?

35. More about road weather modeling in the future
We want to better and do more accurate forecasts to different kind of places (bridges, hills, valleys, ramps, …)
need observations of different kind of places
need information of terrain
need information about the structure of road and ground
need to but into the model those things
need also observations from city area and pavements
In project ColdSpots we research is it possible to do more accurate forecasts already
Friction model
Now we know if the surface is icy. We would like to know also how slippery it is.
Now there are friction measurements available

36. What happens to road maintenance costs in the future?
Because of global warming winter time road maintenance costs will increase in Finland
Summer time season will be longer. Less costs on Nov, Dec, Mar.
In the middle of the winter (Jan, Feb) the costs will increase
The number of near zero temperatures will increase in the middle of the winter  Need more salting because of icy roads
The total sum of maintenance costs in the winter season will increase

37. Thank You for Your Interest!