The Use of METRo
(Model of the Environment and Temperature of the Roads)
in Roadway Operation Decision Support Systems
Seth K. Linden
Kevin R. Petty
National Center for Atmospheric Research
AMS
24th Conference on IIPS
January 24, 2008
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Outline
•Background about the MDSS
•An overview of the MDSS system and
where METRo fits in
•METRo Overview
•Limitation
•Benefits
•Performance
•Summary
Denver Blizzard, December 2006 (AP Photo/Peter M. Fredin)
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Maintenance Decision Support System
In the late 1990s, the Federal Highway Administration (FHWA) Road Weather
Management Program realized the need to address the challenges faced by
the winter maintenance community
•There was very little guidance about how to use road weather
information in the maintenance decision making process
•This disconnect between meteorology and surface transportation
became the genesis for the winter Maintenance Decision Support
System (MDSS)
The purpose of the MDSS functional prototype is to provide objective
guidance to winter road maintenance decision makers during adverse
weather events
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Maintenance Decision Support System
The MDSS:
• Real-time observations
•
•
•
Advanced weather forecasts
Road condition forecasts
Recommended treatments
Denver Blizzard, December 2006 (AP Photo/David Zalubowski)
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System Overview
NOAA/NWS
Numerical Weather
Prediction Models
oNAM
oGFS
Surface Observations
Model Statistics
NOAA/NWS
NOAA/GSD (Boulder, CO)
Supplemental
Numerical Weather
Prediction Models
oRUC
RWIS Data via MADIS
NCAR (Boulder, CO)
Road Weather Forecast
System (RWFS)
Road Condition and
Treatment Module
(RCTM)
Maintenance Garages
PC Java
Application
Data Server
Staff Locations
(access from home, etc.)
DOT Data
•RWIS
PC Java
Application
Road characteristics
Route characteristics
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System Overview
NOAA/NWS
Numerical Weather
Prediction Models
oNAM
oGFS
Surface Observations
Model Statistics
NOAA/NWS
NOAA/GSD (Boulder, CO)
Supplemental
Numerical Weather
Prediction Models
oRUC
RWIS Data via MADIS
NCAR (Boulder, CO)
Road Weather Forecast
System (RWFS)
Road Condition and
Treatment Module
(RCTM)
Maintenance Garages
PC Java
Application
Data Server
Staff Locations
(access from home, etc.)
DOT Data
RWIS
Road characteristics
Route characteristics
PC Java
Application
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Road Condition and Treatment Module (RCTM)
the pavement model
Weather
Forecasts
Road Temp and
Snow Depth
Module
Net Mobility
Rules of
Practice (RoP)
Roadway
Observations
Chemical
Concentration
Road Conditions
and Treatments
Roadway
Configuration
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The Need for a New Pavement Model
The MDSS utilizes a pavement model to predict road temperature and
road conditions
In the past, the MDSS has used a model called SNTHERM developed by
the U.S. Army Cold Regions Research and Environmental Laboratory
(CRREL)
SNTHERM is no longer actively developed and supported, thus there was
a need to implement a new pavement condition model
Through research and statistical evaluation of publicly available models,
a Canadian model called Model of the Environment and Temperature of
the Roads (METRo) was identified as the leading replacement
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METRo Overview
Developed and used by the
Meteorological Service of Canada
Uses roads surface observations
along with a weather forecast to
predict the evolution of pavement
temperatures and the accumulation
of precipitation on the road
Denver Blizzard, December 2006 (AP Photo/Ed Andrieski)
Composed of three parts:
• energy balance module for the road surface
• heat-conduction module for the road material
• module to deal with water, snow and ice accumulation on the road
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Challenges and Limitations
METRo was developed for Linux platforms only
Has modules written in three different software languages: C++, Python, and
FORTRAN-77
• Requires an external, publicly available library to interface between the
various modules
Requires and observational history of the road surface and, if available, the
road subsurface
• At least a 1 hour history is required and 12 hours is preferred
• Generating this history presents challenges at non-observing sites
Takes a relatively long time to run (~ 2 seconds for a 48 hour point forecast)
• Processing the XML input and output files takes a significant amount
of time
• Can become problematic when running over a large number of sites in
an operational system
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Benefits
Good performance under a number of disparate road weather conditions
Easy to acquire, install and run
• The code is stable
• The web site describes any model changes/updates
http://home.gna.org/METRo/)
Well documented
• A wiki facilitates information exchange by providing system
enhancement notifications and troubleshooting tips
http://documentation.wikia.com/wiki/METRo
Adequately supported by the developers (Environment Canada)
• The developers have been quick to analyze and resolve potential
issues, including modifying and re-releasing code
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Performance Assessment
The performance assessment is taken from a study that was conducted by
NCAR (2007) to find a suitable replacement model for SNTHERM
Two replacement models were examined:
METRo - Model of the Environment and Temperature of Roads
FASST- Fast All-season Soil Strength (Developed by CRREL)
SNTHERM is also included to serve as a baseline for performance
Road temperature predictions were compared to observed road temperatures
for an Environmental Sensor Station
Two types of analyses were completed:
1. Road temperature predictions were generated using forecast
atmospheric data (from the MDSS)
2. Predictions were generated using actual observations (perfect
prognosis [perfprog] approach)
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Model Performance
Road Temperature Predictions vs. Observations
Clear Case:
8 November 2006
Perfect Prognosis
Approach
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Model Performance
Road Temperature Predictions vs. Observations
Clear Case:
8 November 2006
Forecast Driven
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Model Performance
Road Temperature Predictions vs. Observations
Snow Case:
28 November 2006
Perfect Prognosis
Approach
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Model Performance
Road Temperature Predictions vs. Observations
Snow Case:
28 November 2006
Forecast Driven
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Summary
The MDSS is dependent upon a reliable and accurate pavement conditions
model such as METRo
Two notable weaknesses are the amount of time it takes to run and the
need for an observation history at each forecast site
The strengths of METRo clearly outweigh its weaknesses
• Performs well under a variety of weather conditions
• It is extremely easy to acquire, install and use, even for novice users
• Great support provided by the developers
• These attributes help facilitate the technology transfer process
METRo has been incorporated into the MDSS and it is recommended that
it be used in the development of other decision support systems targeting
roadway operations
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Questions ?
Denver Blizzard, December 2006 (AP Photo/Ed Andrieski)
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