Weather Information for Surface Transportation (WIST)

1. Weather Information for Surface Transportation (WIST) Panel 3 Technical Risks and Challenges Bill Mahoney National Center for Atmospheric Research

2. WIST Technical Risks & Challenges Weather Data Acquisition Weather Diagnoses & Forecasts Decision Support System DOT Operations Data Acquisition

3. Weather Diagnoses & Forecasts • The weather information requirements of the • surface transportation community are highly • specialized. • The weather community has not traditionally • been focused to serve surface transportation • needs.

4. Weather Diagnoses & Forecasts • The transportation community requires: • - Very high resolution information • (misoscale = 40 m to 4 km) • - Rapid updates • (minutes to hours) • - Long lead time forecasts • (at least 48 hours)

5. Weather Diagnoses & Forecasts • The transportation community also requires: • - Surface information (0 to 2 m AGL) • - Probability metrics for meteorological • parameters • “What is the probability of receiving • 3 inches of snow between mile marker • x and y from 6:00 to 9:00 am tomorrow?”

6. Weather Diagnoses & Forecasts • Scientific challenges: • - Boundary layer meteorology (0 to 2 m AGL) • - Thermodynamics (heat flux, mixing, etc.) • - Probability & Statistics • - Numerical modeling (meso- to misoscale) • - Verification (with limited verification data) • - Quality control of non-standard data

7. Weather Diagnoses & Forecasts • If the weather information utilized by a DSS • Is not sufficiently accurate, then the stakeholders • will ignore DSS guidance. • There are no off-the-shelf plug and play weather • capabilities that can fully address the needs • of the surface transportation community; • however, there are several emerging technologies • that are likely to provide benefits.

8. Weather Data Acquisition • Access to surface observational data is • critical as it provides input to forecast systems • and is necessary for forecast verification. • Without these data, there is a significant • risk that the forecast output will be poor. • There is also a risk associated with using • non-traditional data. Quality control issues • must be addressed.

9. DOT Operations & Data Acquisition • Access to live DOT operations data is critical as it provides input to DSS systems. • Required DOT data includes: • - Traffic (volume, speed) - Staff availability • - Road surface condition - Work schedules • - Friction - Equipment • - Road subsurface conditions - Treatment type • - Chemical concentrations - Treatment location • - Level of service - Beat completed

10. DOT Operations & Data Acquisition • A DSS with even limited utility will require • live access to these kind of data. • The technology and resources required • to develop and maintain a dynamic DOT • data base cannot be underestimated. • There are several risks associated with • managing operational data, particularly when • data become old or invalid.

11. Decision Support System (DSS) • The STWDSR process clearly illustrates that • the user community is large and diverse. • There is a risk associated with the assumption • that a specific DSS solution will be broadly • applicable across the surface transportation • community.

12. Decision Support System (DSS) • Nearly every road maintenance district has • a unique operation. In addition, individuals within • districts have unique needs. • There is no “one-size-fits-all” DSS solution. There • are significant human factor issues associated • with DSS development that need to be addressed. • There is also a risk that users will reject a DSS • that makes too many decisions for them.

13. Decision Support System (DSS) • A “bottoms-up” rather than a “tops-down” • approach should be used for DSS system • development. • Local DOT organizations need to determine • the level of sophistication that is required • for their specific DSS application. • The FAA has and continues to experience many • of these challenges in their automation programs. • FAA experiences should be considered.

14. Summary There are numerous challenges associated with the WIST-DSS initiative; however, scientific and engineering solutions are coming to fruition that, given time and appropriate resources, are likely to produce significant benefits to the surface transportation community.

15. Summary (cont.) A long term, multifaceted WIST R & D program should be established in order to properly address user needs and to extract the scientific and technical capabilities that reside in organizations (government and private) across the country.