Specific Priorities
Maintenance Decision Support System (MDSS)
RAL will continue to work with the winter maintenance community to improve the MDSS, which was RAL’s highly successful inaugural road weather project. The MDSS will continue to be developed and validated through local and national test beds. Research will focus on improving pavement temperature modeling, precipitation prediction, data fusion techniques, and snow and ice control rules of practice. Technology transfer activities will involve working closely with local and state Departments of Transportation (DOTs) in the development of procurement specifications for MDSS technologies, and supporting the commercial weather community as it develops and enhances commercial versions of the MDSS.
Sample image from the prototype MDSS. Winter maintenance managers can view current and predicted road and weather conditions along specific plow routes and received anti- and deicing snow and ice control recommendations based on predicted conditions.
Vehicle Infrastructure Integration (VII)
Working with the FHWA and the automotive industry, RAL will develop concepts and explore the feasibility of instrumenting vehicles with meteorological sensors and utilizing vehicle data to detect and report weather and road condition hazards. This work is part of a broader FHWA Vehicle Infrastructure Integration Program. We will work with automobile manufacturers to understand vehicle data elements, processing requirements, quality assessment, post processing, and accuracy requirements. RAL developed the VII Feasibility and Concept Development Report, which will be delivered to the FHWA by the end of FY2006, and will provide the leadership for additional research and development activities leading to a field demonstration of weather related VII capabilities. The use of vehicle data to supplement fixed observations, particularly in data poor regions, is expected to hold great value. In addition to assessing the utility of vehicles as probe data, RAL will work with the surface transportation community on the development of in-vehicle information systems.
Nationwide surface transportation weather detection and forecast system
RAL will continue to provide input and feedback to the FHWA on the development of the Nationwide Surface Transportation Weather Detection and Forecast System Initiative known as the Clarus Initiative. In 2005, an operational concept document was developed, and detailed design specifications were prepared for the Clarus System by a national team of researchers. RAL will continue to participate on various Clarus Task Forces charged with overall system development and demonstrations. Of particular interest to RAL is the development of surface transportation hazard products designed to identify and predict hazardous surface conditions. A significant amount of research is required to develop products with sufficient fidelity to be used by the traveling public while mobile.
WRF-Fire testing, validation and implementation
RAL, with previous funding from the NCAR Wildfire Initiative and collaborators in MMM and ACD, has developed a physics package for WRF that is specifically designed to predict fire behavior at high resolution. The model incorporates high-resolution fuels data and runs in nested mode with grid resolution of less than 500 m over and around the fire perimeter. RAL will work with the primary land management agencies to arrange for testing and validating the physics package in operational environments. The USDA Forest Service currently uses MM-5 as its atmospheric model but has no coupled-model capability to integrate a mesoscale atmospheric model with a fire behavior model. RAL will time the testing and validation phase with the Forest Service decision to migrate from MM-5 to WRF to support its operations.
U.S. economic sector sensitivity assessments
RAL will continue our work jointly with SERE to quantitatively characterize how much U.S. economic output varies as a result of weather variation. Results will be presented and published at technical conferences and meetings, and in leading publications such as the Bulletin of the American Meteorological Society).RAL will also (1) work on projects to estimate the national value of improved weather forecasts; (2) develop and pre-test draft survey instruments to elicit societies’ values for improved forecasts (e.g., hurricane predictions); and (3) undertake case studies of how society perceives, understands, and uses probabilistic forecast information.
Weather and society
Integrated studies workshops: RAL will continue to develop, organize, and conduct workshops similar to the Weather and Society Integrated Studies (WAS/IS) workshop series devoted to teaching social science and economic methods, skills, and tools with the goal of building a community of researchers with the skill sets necessary to conduct social science and economic studies. Specifically, the workshops will be designed to develop a common knowledge set on social science and weather; identify and address typical roadblocks and challenges of cross-disciplinary efforts, (especially those relevant to incorporating societal impacts into weather forecasting and product development); identify the most appropriate and straightforward methodologies to use for improving understanding, communication, and use of weather information; develop strategies to broaden the training of physical and social scientists to better understand and appreciate the fundamental interconnections between new product development and application; and create plans to improve and further facilitate the ongoing relationships between professionals in meteorology and the social sciences.
Digital library on societal impacts: An important part of SIP is to create a clearinghouse of social and economic research results. RAL will grow the SIP Digital Library throughout the program and add published results from within and outside UCAR.
International collaboration on societal impacts: RAL will continue to play a critical lead role in U.S. and international efforts on societal impacts assessment in the THORPEX program including holding workshops on developing a research agenda and chairing the THORPEX SEA Working Group.
Intelligent Weather Systems (IWS)
RAL will continue its work on the development of advanced weather forecasting systems such as the Dynamic Integrated Forecast System (DICast) by incorporating the new methods and techniques designed to optimize automated forecasts. We will investigate new model data sources such as the Weather Research and Forecast (WRF) model, and explore fuzzy-logic statistical processing, and ensemble techniques. Methods and techniques to generate probabilistic weather prediction products (e.g., probability density functions) will also be investigated and developed. Development of prototypical products incorporating probabilistic output will also be conducted and various methods for communicating uncertainty will be analyzed.
The short-term forecast period (0-6 h) needs special attention, as has been mentioned earlier in this plan. RAL will explore ways to incorporate observational data from radars, satellites, and surface observations to optimize these forecasts. In addition, new data fusion techniques will be developed to ensure that rapidly-changing weather conditions will be accurately predicted.
Diagram of the Intelligent Weather System (IWS) concept. This concept has been used successfully in several RAL products to optimize results.
Verification
As discussed in earlier sections, verification is essential to all part of the RAL program. In particular, it is increasingly important to extend our efforts toward verification approaches that are user-focused and that incorporate information regarding the needs of specific users. Initially, this effort will involve development of generic approaches which are likely to benefit many users, but the goal will be to work with specific users regarding their particular needs. In contrast to traditional verification approaches, this approach will provide specific information that can be used for decision making. A more complete discussion of the verification effort in RAL is presented in the later section “Forecast Verification and Quality Assessment.”
Public health initiative
RAL and its collaborators will engage the public health sector to determine where decision support systems can be applied to those areas of public health that are very sensitive to weather events (asthma, extreme cold, extreme heat, dehydration, stroke, etc.) after carefully reviewing and evaluating pioneering work done in the United Kingdom. Designs for scientific collaborative studies with researchers in the public health community and for prototype decision support systems will be completed and proposed for implementation or testing.