The role of meso-γ-scale numerical weather prediction and visualization for weather-sensitive decision making

Abstract

Weather-sensitive business operations are often reactive to short-range, local conditions due to unavailability of appropriate predicted data at this temporal and spatial scale. This situation is commonplace in a number of applications, some of which address either planning for or response to hazards or disasters. These include, but are not limited to, transportation, agriculture, energy, insurance, entertainment, construction, communications and emergency management. 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- to meso-β-scale weather models. This time range is typically beyond what is feasible with modern nowcasting techniques. Hence, near-real-time assessment of observations of current weather conditions may have the appropriate geographic locality, but by its very nature is only directly suitable for a reactive response. Alternatively, meso-γ-scale (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 by enhancing both the quality and lead time of such information. In particular, they appear to be well suited toward improving economic and safety factors of concern for transportation applications of interest to state and local highway administrations and airport terminal operators. 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, and short-term environmental impact. To begin to address these issues, a prototype system, dubbed "Deep Thunder", was first 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. This protoype provides nested 24-hour forecasts, which are typically updated twice daily, for the New York City metropolitan area to one km resolution utilizing explicit, bulk cloud microphysics. It was further customized for transportation and other applications. It was extended to also provide forecasts for the Baltimore/Washington, Chicago, Kansas City and San Diego metropolitan areas at one to two km resolution, at least once per day. All of the processing, modelling and visualization are completed in 30 to 60 minutes on relatively modest hardware to enable sufficiently timely dissemination of forecast products at reasonable cost. We will discuss the potential role that this class of capabilities has in the weather-sensitive decision making process, including those with significant societal and economic impact, and which relate to specific choices in the design of the system. Deep Thunder is being used by local agencies to assist with various weather-sensitive problems in transportation, logistics and emergency planning. Therefore, we will also present some results concerning the effectiveness of such modelling capabilities for these applications.