1 The Use of METRo (Model of the Environment and Temperature of the Roads) in Roadway Operation Decision Support Systems 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 Seth K. Linden Kevin R. Petty National Center for Atmospheric Research AMS 24th Conference on IIPS January 24, 2008

2 Outline Background about the MDSS An overview of the MDSS system and where METRo fits in METRo Overview Limitation Benefits Performance Summary 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)

3 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

4 The MDSS: Real-time observations Advanced weather forecasts Road condition forecasts Recommended treatments Maintenance Decision Support System Denver Blizzard, December 2006 (AP Photo/David Zalubowski)

5 System Overview NOAA/NWS NOAA/GSD (Boulder, CO)  Supplemental Numerical Weather Prediction Models o RUC  RWIS Data via MADIS NOAA/NWS NOAA/GSD (Boulder, CO)  Supplemental Numerical Weather Prediction Models o RUC  RWIS Data via MADIS NOAA/NWS  Numerical Weather Prediction Models o NAM o GFS  Surface Observations  Model Statistics NOAA/NWS  Numerical Weather Prediction Models o NAM o GFS  Surface Observations  Model Statistics NCAR (Boulder, CO) Road Weather Forecast System (RWFS) Road Condition and Treatment Module (RCTM) Road Weather Forecast System (RWFS) Road Condition and Treatment Module (RCTM) Data Server DOT Data RWIS  Road characteristics  Route characteristics DOT Data RWIS  Road characteristics  Route characteristics Maintenance Garages Staff Locations (access from home, etc.) Staff Locations (access from home, etc.) PC Java Application

6 System Overview NOAA/NWS NOAA/GSD (Boulder, CO)  Supplemental Numerical Weather Prediction Models o RUC  RWIS Data via MADIS NOAA/NWS NOAA/GSD (Boulder, CO)  Supplemental Numerical Weather Prediction Models o RUC  RWIS Data via MADIS NOAA/NWS  Numerical Weather Prediction Models o NAM o GFS  Surface Observations  Model Statistics NOAA/NWS  Numerical Weather Prediction Models o NAM o GFS  Surface Observations  Model Statistics NCAR (Boulder, CO) Road Weather Forecast System (RWFS) Road Condition and Treatment Module (RCTM) Road Weather Forecast System (RWFS) Road Condition and Treatment Module (RCTM) Data Server DOT Data  RWIS  Road characteristics  Route characteristics DOT Data  RWIS  Road characteristics  Route characteristics Maintenance Garages Staff Locations (access from home, etc.) Staff Locations (access from home, etc.) PC Java Application

7 Road Condition and Treatment Module (RCTM) Weather Forecasts Road Temp and Snow Depth Module Net MobilityRules of Practice (RoP) Roadway Configuration Chemical Concentration Road Conditions and Treatments Roadway Observations the pavement model

8 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

9 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 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 Denver Blizzard, December 2006 (AP Photo/Ed Andrieski)

10 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

11 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 Well documented A wiki facilitates information exchange by providing system enhancement notifications and troubleshooting tips Adequately supported by the developers (Environment Canada) The developers have been quick to analyze and resolve potential issues, including modifying and re-releasing code

12 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)

13 Model Performance Road Temperature Predictions vs. Observations Clear Case: 8 November 2006 Perfect Prognosis Approach

14 Model Performance Road Temperature Predictions vs. Observations Clear Case: 8 November 2006 Forecast Driven

15 Model Performance Road Temperature Predictions vs. Observations Rain Case: 7 July 2006 Perfect Prognosis Approach

16 Model Performance Road Temperature Predictions vs. Observations Rain Case: 7 July 2006 Forecast Driven

17 Model Performance Road Temperature Predictions vs. Observations Snow Case: 28 November 2006 Perfect Prognosis Approach

18 Model Performance Road Temperature Predictions vs. Observations Snow Case: 28 November 2006 Forecast Driven

19 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

20 Questions ? Denver Blizzard, December 2006 (AP Photo/Ed Andrieski)