1. Applications of Weather Uncertainty Information – Transportation 2 nd OS&T Workshop on Communicating Uncertainty and Decision Support 4-6 August 2009 Sheldon Drobot NCAR/RAL drobot@ucar.edu

2. Outline  Weather & Transportation  The Maintenance Decision Support System (MDSS)  IntelliDrive SM  Ensembles

3. Annual Fatalities  74  740  7400  74000

4. Safety  Poor weather conditions contribute to 7,400 deaths and 673,000 injuries in an average year

5. Mobility  554 million vehicle-hours of delay per year result from snow, ice, and fog

6. Efficiency  Delays to trucking companies range from $2.2-$3.5 billion annually  Greenhouse gas emissions

7. Transportation Trends  Surface transportation weather continues to gain national attention  Several road weather initiatives are in progress and new weather information product concepts are being developed

8. Improved Weather Information Needs  Incident Management  Predicting problem areas  Placement of response assets  Faster cleanup  Construction  Paving operations  Striping  Project scheduling  Emergency Management  First responder support  Traffic control  Identifying impacted corridors

9. Things we know  Weather forecasts are inherently uncertain  Some people want or are willing to receive (some types of) uncertainty information

10. Things we don’t know  What kind of information do people want?  How best to deliver that information to people?  There is no one “people”  Confidence vs. uncertainty?

11. Transportation Examples  What is the likelihood of precipitation between 3 PM and 6 PM?  What is the likelihood that the precipitation will be snow or rain or ice?  What is the likelihood that the pavement temperature will be between 30 o F and 32 o F?  How much confidence do you have in your forecast?

12. What is MDSS?  Began in 1999 after a study discovered a disconnect between road weather forecasts and the road maintenance community  The weather and transportation communities were brought together to define and develop a system that translates current and predicted road & weather information into recommended maintenance actions  Goal - improve the productivity and cost efficiency of transportation agencies  A prototype was built based on open system principles, which fostered an MDSS market by making that prototype freely available

13. What is MDSS? Data Ingest Module Road Wx Forecast and Data Fusion Module Road Condition and Treatment Module Java-based Display Numerical model data Road Weather Information System (RWIS) data Miscellaneous observations (e.g., airport) Consensus forecast generation Road temperature and condition forecasts Rules of practice for anti-icing and deicing operations Treatment recommendations Delivery of information and data from upstream modules to end users via an interactive Graphical User Interface

14. Who is Using MDSS?

15. Prob/Uncertainty in MDSS Alerts

16. Conditional Probabilities Conditional Probability of Precipitation Type Product MDSS declared precipitation type Probability of Precipitation - Overall Conditional probability of precipitation type

17. Confidence  Cameras!

18. Forecast Use  The 2” snow forecast  Used by three entities (private road operator, public road operator, airport authority) in entirely different ways  Highlights the importance of understanding user needs…not everyone needs/wants the same thing

19. What is IntelliDrive SM ?  IntelliDrive SM is a suite of technologies and applications that use wireless communications  IntelliDrive SM applications provide connectivity:  with and among vehicles  between vehicles and the roadway infrastructure  among vehicles, infrastructure, and wireless devices (consumer electronics, such as cell phones and PDAs) that are carried by drivers, pedestrians, and bicyclists http://www.intellidriveusa.org

20. Vehicle Data in IntelliDrive SM Windshield Wiper Setting Head Lights Status Sun/Rain Sensor Antilock Braking System (ABS) Traction Control Adaptive Cruise Control (ACC) Ambient Air Temperature Barometric Pressure Brake Status Stability Control Speed and Heading Location and Elevation Hours of Operation http://www.intellidriveusa.org

21. VDT Data RSAS data Vehicle probe messages Surface weather observations Source: NWS Gridded radar data Road segment statistics Satellite cloud mask data

22. Precip Algorithm

23. The VDT

24. Is this right?

25. The AMS APT  AMS has convened an APT to look at mobile weather observations  Part of this will focus on user needs

26. One more thought

27. Take Home Points  We are losing lives, mobility, and money because of weather in transportation  Uncertainty/confidence information could have an enormous impact  The time is now Sheldon Drobot NCAR/RAL drobot@ucar.edu