# Presentation: 313527

## A Decision Support Tool for Highway Maintenance:A First Principle Thermal Mapping Model 

- &
- Edward E. Adams
- WTI / MSU Bozeman, MT
- Allen R. Curran
- ThermoAnalytics Inc. Calumet, MI
- Sponsored by Montana Department of Transportation

- by Peter Gauer

**Notes:**
Thank you Mrs/Mr Chairman

Ladies and Gentleman,

I like to present  A Decision Support Tool for Highway Maintenance:
A First Principle Thermal Mapping Model.
Actually, I want to present a whole model chain. 
But before I go on, I like to acknowledge my co-workers Ed Adams also from the Western Transportation Institute / Montana State University, Bozeman and Al Curran from ThermoAnalytics. Calumet, MI. 
The work is sponsored  by the Montana Department of Transportation.

## Potential Benefits

- Information for Maintenance Decisions
- De-Icing
- Anti-Icing
- Information for Public
- Freeze  Warning
- Road Weather Conditions

**Notes:**
What would be the Potential Benefits of a thermal mapping tool?
Only to mention some:
Ice formation on roads is an obvious safety issue on highways at large in cold climates. 
Information about pavement temperature could support the decision-making by highway maintenance. The timing of the application of freezing depressants is crucial: 
De-icing
Timing is particularly crucial using anti-icing concepts.
Cost savings could be realized by DOTs in that resources can be applied to crucial areas in a more effective way. 

Based on real-time or forecast data the public could be alerted to potential problem areas.  

## Basic Physics

- Surface Heat Budget
- G (1 - )
-  L + L
- H
- LE
- QG
- 0

**Notes:**
Basic Physics

Winter road conditions are highly influence by the pavement temperature. 
The temperature is determined by the surface heat balance, which is sum of:  
Absorbed incoming global radiation (solar short wave radiation; direct and diffuse), 
Net long wave radiation (incoming minus outgoing). 
Here we can distinguish between the outgoing radiation and the in coming, partly from the sky partly from the surrounding.
Sensible heat flux
Latent heat flux.
 Both parts are depending on the turbulent exchange driven by the wind speed.  
Molecular heat flux into the bottom
 is the albedo which depends on changing surface properties.

## Model Chain

-  ETA
-  (40 km)

- Meteorological Input Data
-  Radiation (downward)
-  Wind Speed
-  Air Temperature
-  Relative Humidity
-  Precipitation Rate
-                  

## I-90 Bozeman Pass (Test Area)

**Notes:**
High way I-90 Bozeman Pass (Test Area)
Studies of the MDOT show high rate of weather related accidents (about 60%). About ten accidents every year result  in temporary full highway closures. 
I-90 Bozeman Pass between Livingston and Bozeman, with its serve winter weather condition, was identified as one critical section. 
The area around Livingston  is known for high wind speed forcing road closures.
In this area, two RWIS stations are available, Bozeman Hill (MBZH) and East Livingston (MELV).

## Meso-Scale Model I (Bozeman Area)

- Simulation were made using ARPS developed by the Center of Prediction and Analysis of Storms

- Wind field simulation for Bozeman Area
- 19/11/2000 19:00 UTC

**Notes:**
Meso-Scale Model I (Bozeman Area)

ARPS is running on 1 km grid resolution for an area of 60x60 km2, centered around Bozeman Pass. Give us about 4 grid points in the ETA grid  #212 .
The actual area for the Wintherm simulation is about 3x8 km2 through Rocky Canyon. In this winter we start with an additional section over Bozeman Pass.
Here, shown is an example of the surface wind field. The wind field by itself would provide useful information about forecasted wind speeds for areas like Livingston.  Noticeable are the high wind speed  in this area

## Meso-Scale Model II (MBZH)

- Comparison between Simulation and Measurements: 05/12/2000-05/14/2000

**Notes:**
Meso-Scale Model II (MBZH)

Shown is the comparison of the simulated global radiation, downward long wave radiation, air temperature, and wind speed with measurements for Bozeman Pass for a period of two day, 05/12/2000-05/14/2000.
The simulation shows reasonable good agreement with the measurements.
Noteworthy is the high fluctuation of the measured global radiation, probably caused by clouds. This is not reproduced by the simulation.       
Simulation like this are used as Input data to drive the WinTherm module.

## WinTherm I (I-90 Rocky Canyon)

- Thermal Map
- Period
- 05/06/2000 06:00 MDT
- 05/08/2000 14:00 MDT
- Surface Temperature Tsfc
- Color Coded in C
- Temperature at MM319

**Notes:**
WinTherm I (I-90 Rocky Canyon)

Now I want  show an animated thermal map for Rocky Canyon, I-90. 
Shown is the simulation of a 2.5 day period in May, 2000. 
The temperature  is color code in Celsius.
The number over the color bar reflects the pavement temperature at mile marker 319 about 2 miles from the RWIS station Bozeman Hill.  
We are looking eastwards, south is on the right. 

## WinTherm II (I-90 MM319)

- Comparison between 2 Simulations and Measurements

**Notes:**
WinTherm II (I-90 MM319)
Here, a comparison is shown between of two simulation and the measured pavement temperature at mile marker 319 for a four day period. One simulation is using input data from the RWIS station 2 miles away, the other is using ARPS data.  
Both simulation show good results 
The simulation using the RWIS data follows the form more closely, what isn’t surprising as it uses real meteorological input data from the nearby station.  
The measurements also indicates the importance of the radiation and the interaction of the cloud cover. 
The peak on May 9 is an artifact of the program restart at that time. 

## WinTherm III (I-90 MM319)

- Comparison between Simulation and Measurements

**Notes:**
WinTherm III (I-90 MM319)

Here, a similar comparison is shown between of simulation using RWIS data and the measured pavement temperature at mile marker 319 for a period of 18 day.
Overall the simulation reflects the pavement temperature quit well.

## Concluding Remarks

- A successful implementation of the thermal model chain can be a useful tactical decision support tool for winter highway management.
- Coupled with a snowpack model, it can also be a tool to predicted snowpack stability and to forecast avalanche hazard threatening highway sections.

**Notes:**
Concluding Remarks

A successful implementation of the thermal model can be a useful tactical decision support tool for winter highway management. 
Coupled with a snowpack model, it can also be a tool to predicted snowpack stability and to forecast avalanche hazard threatening highway sections.

## What need to be done further?

- Meteo-Model (Improvement of the Meteorological Input Data for Wintherm):
- Cloud Cover Modeling  Radiation
- Nesting:
-  Grid Resolution
-  Number Nesting Steps
- Thermal Mapping
- Grid Resolution
- Adaptive Meshing for Critical Areas
- Influence of Surface Properties
- Albedo for Snow/Ice Covered Pavements
- Melting, Icing, Sublimation, Condensation
-  etc.

**Notes:**
What need to be done further?
Meteo-Model: Improvement of the Meteorological Input Data for Wintherm
Cloud Cover Modeling and the interaction with the radiation is important.
Nesting:
 Grid Resolution 
 Number Nesting Steps
Thermal Mapping
Grid Resolution
Adaptive Meshing for Critical Areas
Influence of Surface Properties
Albedo changing for Snow/Ice Covered Pavements
Melting, Icing, Sublimation, Condensation, etc.