A wide variety of operations in the transportation industry are weather-sensitive to local conditions in the short-term (3 to 36 hours). Typically, they are reactive due to unavailability of appropriate predicted data at the required temporal and spatial scale. Hence, optimization that is applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or the results of synoptic-scale weather models. While near-real-time assessment of observations of current weather conditions may have the appropriate geographic locality, by its very nature is only directly suitable for reactive response. Alternatively, mesoscale numerical weather models operating at higher resolution in space and time with more detailed physics may offer greater precision and accuracy within a limited geographic region for problems with short-term weather sensitivity. Such forecasts can be used for competitive advantage or to improve operational efficiency and safety. In particular, they appear to be well suited toward improving economic and safety factors of concern to state and local highway administrations. They are also relevant to other state and local agencies responsible for emergency management due to the effects of severe weather. Among others, such factors relate to routine and emergency planning for snow (e.g., removal, crew and equipment deployment, selection of deicing material), road repair, maintenance and construction, repair of downed power lines and trees along roads due to severe winds, evacuation from and other precautions for areas of potential flooding, etc.

To address these issues, we build upon our earlier work, the implementation of an operational testbed, dubbed "Deep Thunder". This protoype provides nested 24-hour forecasts for the New York City metropolitan area to 1 km resolution, which are updated twice daily. The work began with building a capability sufficient for operational use. In particular, the goal is to provide weather forecasts at a level of precision and fast enough to address specific business problems. Hence, the focus has been on high-performance computing, visualization, and automation while designing, evaluating and optimizing an integrated system that includes receiving and processing data, modelling, and post-processing analysis and dissemination. Part of the rationale for this focus is practicality. Given the time-critical nature of weather-sensitive btransportation operations, if the weather prediction can not be completed fast enough, then it has no value. Such predictive simulations need to be completed at least an order of magnitude faster than real-time. But rapid computation is insufficient if the results can not be easily and quickly utilized. Thus, a variety of fixed and highly interactive flexible visualizations have also been implemented, including ones focused on support of operational decision making in transportation.

The concept behind Deep Thunder in this context is clearly to be complementary to what the National Weather Service (NWS) does and to leverage their investment in making data, both observations and models, available. It is therefore also complementary to the deployment of Road Weather Information System (RWIS) stations by state highway administrations to monitor real-time weather conditions along roads. The idea, however, is to have highly focused modelling by geography with a greater level of precision and detail than what is ordinariliy available. Hence, we will review our particular architectural approach and implementation as well as the justification and implications for various design choices. Then we will outline how this approach enabled customization for problems associated with transportation applications as well as discuss the specific customizations. Finally, we will present some results concerning the effectiveness of such modelling capabilities for these applications.