Weather-sensitive business operations are primarily reactive to short-term (3 to 36 hours), local conditions (city, county, state) due to unavailability of appropriate predicted data at this temporal and spatial scale. This situation is commonplace in a number of applications including, but not limited to transportation, agriculture, energy, insurance, entertainment, construction, communications and emergency planning. Typically, what 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. Alternatively, mesoscale (cloud-scale) numerical weather models operating at higher resolution in space and time with more detailed physics has shown "promise" for many years as a potential enabler of proactive decision making for both economic and societal value. They may offer greater precision and accuracy within a limited geographic region for problems with short-term weather sensitivity. In principle, such forecasts can be used for competitive advantage or to improve operational efficiency and safety. However, a number of open questions exist. For example, can both business and meteorological value be demonstrated beyond physical realism that such models clearly provide? Can a practical and usable system be implemented at reasonable cost?

To begin to address these issues, a prototype system, dubbed "Deep Thunder", has been implemented for the New York City metropolitan area. This effort 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 business decisions, 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. They range from techniques to enable more effective analysis to strategies focused on the applications of the forecasts.

We will discuss our particular architectural approach and implementation as well as the justification and implications for various design choices. Deep Thunder is now being used by local agencies to assist in operational decision making with various weather-sensitive problems in highway maintenance and operation, surface transportation and emergency planning. Therefore, we will also present some results concerning the effectiveness of such modelling capabilities for these applications.