Transcript The FMI Road Weather Model, Applications and Projects Marjo Hippi Meteorological research / Meteorological applications Finnish Meteorological Institute 6.11.2015

The FMI Road Weather
Model,
Applications
and
Projects
Marjo Hippi
Meteorological research /
Meteorological applications
Finnish Meteorological Institute
6.11.2015
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
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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
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Numerical model
Atmosphere : weather model - weather parameters
3-D
. . .
. . .
. . .
10 km
Road
model
(1-D)
10 km
1-D
4 meters
Road surface
2-D
Ground
Climatological temperature
"Deep" ground
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Model structure
Upper boundary forcing
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
Ground heat transfer
• heat conductivity ()
• specific heat (c)
• density ()
• porosity ()
Surface heat exchange
• sensible heat flux (H)
• latent heat flux (LE)
• long wave radiation (RL)
• stability
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Input data:
• temperature, humidity
• wind speed
• precipitation intensity
• lighting conditions
Outputs:
Traffic index
• normal
• bad
• very bad
Road index
• surface temperature
• storages
- water, snow
- ice, frost
1. dry
2. damp
3. wet
4. wet snow
5. frost
6. partly icy
7. dry snow
8. icy
Temperature
Road surface
temperature
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Model run
Present time
Forecast phase (24-48 h)
Observation phase (3-48 h)
SYNOP, radar precipitation
Hirlam, ECMWF etc.
Input data :
Observations
Forecast (meteorologist's editor)
Simulation :
Road weather model
Road weather model
Forecast
initial state
Input data
• latest observations and
forecasts
• "data pool" updated
automatically by a data
fetch agent
Model output
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Surface energy balance
Equation:
G  1   RS  RL  RL  H  LE  Wphch  Wtraff
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
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Effect of traffic on road surface
• traffic wear
 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)
3
mm eq. water
 E.g. snow packed partly to ice,
partly flown away
3.5
snow storage
2.5
2
ice storage
1.5
1
0.5
snowing
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time / hrs
Storages are one of the most
important part of this model. There
are storages for water, snow, ice and
deposit.
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Road condition : interaction between storages
Traffic induced drifting
Wear
Melting
Freezing
SNOW
ICE
WATER
ROAD :
COND :
COND 2 :
dry snow
snow
+ ice
wet snow
+ ice
ice
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Temperature
Temperature
• road temperature
• air temperature
• air dew point
Temperature (air)
Temperature (road)
Wind
snow
icy
partly icy
frost
wet snow
wet
damp
dry
Traffic idx :
)
water (mm) ; snow
(cm)| STOR x 10
: SUM
Precipitation
INTENSITY : water (mm/h) ; snow (cm/h)
)
long wave (W/m2)
short wave (W/m2)
Radiation
Incoming radiation
• long wave
• short wave
Dew point (air)
Road index
Road index
• primary
• secondary ooooooo
Precipitation and storages
• water prec. mm/h ( ), mm (
• water storage (- -)
• snow prec. mm/h ( ), mm (
• snow storage (--)
• ice storages (-o- ; --)
Wind speed
Meteograms
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Meteogram vs. road weather camera
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Road condition maps
Dry snow
Wet snow
Ice
Wet
Partly icy
Damp
Frost
Dry
Feb 6
12:00
16:00
20:00
24:00
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Traffic condition index
= Very difficult
= Difficult
= Normal
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Road weather models, applications and projects
•
•
•
Four kind of road weather models
1. Normal road weather model
2. Road maintenance model
3. Pedestrian model
4. 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, …
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Pedestrian sidewalk conditions
• J. Ruotsalainen, R. Ruuhela, M. Kangas
• FMI, Inst. of Occupational Health*
*) Työterveyslaitos
• Warning of slippery walking conditions
• Modified surface condition interpretation
• Hospital preparedness
Foot gear friction
measurements
using a "stepping
robot"
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Pedestrian model – Background and Facts
• During wintertime in Finland occur about 50 000
pedestrian slipping accidents with serious
consequences
• 1/100 in Finnish population
• About 5 000 patients (1/1000) are hospitalized
• Annual costs 420 million euros
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How to reduce the number of slipping accidents?
1.
Winter maintenance of pavements
2.
Awareness of pedestrians
3.
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
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Some statistic
The number of slipping accident patients in Töölö
Hospital Emergency in February 2004
30
Patients
25
20
15
10
5
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
Date
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Age distribution of patients with slipperiness injuries
Ulkona liukastuneiden (koodi W00) lukumäärä
Töölön tapaturma-asemalla talvikaudella 2003-2004
214
200
Liukastuneiden lukumäärä
Number of slipperiness injuries
225
175
163
150
Number of
slipperiness injuries
outside during
winter time 20032004 from Töölö
Hospital Emergency
138
118
125
107
96
100
75
50
25
20
0
< 20
20-29
30-39
40-49
50-59
60-69
> 70
Age / Ikä
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Age distribution of patients with hip fracture
due to slipperiness accidents
Lonkkamurtumien ikäjakauma Töölössä 2003-04,
liukastumistapaturmat
16
14
12
Number / lkm
10
8
6
4
2
0
<20
20-39
30-39
40-49
Age /
50-59
60-69
70-79
>80
ikäluokka
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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 2004-2005
The service was extended to cover whole Finland
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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
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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
*
* **
**
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VARO service - Route planning
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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
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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:
 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
• During this project we do also friction measurements and thermal
mapping along the roads (Vaisala’s optical measurements)
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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?)
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How much temperature can differ in near situated
road weather stations
2m temperature 21.1.2004 Vt1
Time
0
-10:00
3:00
6:00
9:00
12:00
15:00
18:00
21:00
0:00
-2
2005
-3
2062
-4
2072
-5
2063
-6
2064
2066
-7
-8
Temperature
Road surface temperature 21.1.2004 Vt1
0
-10:00
3:00
6:00
9:00
12:00
Time
-2
15:00
18:00
21:00
0:00
Distance
between
road
weather
stations is
about 2530 km.
2005
-3
2062
-4
2072
-5
2063
-6
2064
2066
-7
-8
Temperature
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Part II
2m temperature 21.10.2003 Vt1
Temperature
2
0
0:00
-2
2064
3:00
6:00
9:00
12:00
15:00
18:00
21:00
0:00
2065
2066
2063
-4
2072
-6
2005
2062
-8
-10
Time
Temperature
Road surface temperature 21.10.2003 Vt1
10
8
6
4
2
0
-20:00
-4
-6
-8
-10
2064
2065
2066
2063
3:00
6:00
9:00
12:00
15:00
18:00
21:00
0:00
2072
2005
2062
Time
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How does a ColdSpot look like?
Aneriojärvi: an open area,
a lake on the right
Bridge of Halikko may be slippery,
strong wind can cause extra risk
Ikela hill: an open area
ending to a hill
Curve of Koikkala: Road curving
on a hill – poor visibility
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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
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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 : 10-20 commercial products
 road maintenance, VARO etc.
• Interest from abroad
 Interest from Lithuania, Czech, Luxemburg,
Barbados (considering mud slides)...
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... 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 ?
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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
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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
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Thank You
for
Your Interest!
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