Transforming the sensing and numerical prediction of high-impact local weather through dynamic adaptation

Abstract

Mesoscale weather, such as convective systems, intense local rainfall resulting in flash floods and lake effect snows, frequently is characterized by unpredictable rapid onset and evolution, heterogeneity and spatial and temporal intermittency. Ironically, most of the technologies used to observe the atmosphere, predict its evolution and compute, transmit or store information about it, operate in a static pre-scheduled framework that is fundamentally inconsistent with, and does not accommodate, the dynamic behaviour of mesoscale weather. As a result, today's weather technology is highly constrained and far from optimal when applied to any particular situation. This paper describes a new cyberinfrastructure framework, in which remote and in situ atmospheric sensors, data acquisition and storage systems, assimilation and prediction codes, data mining and visualization engines, and the information technology frameworks within which they operate, can change configuration automatically, in response to evolving weather. Such dynamic adaptation is designed to allow system components to achieve greater overall effectiveness, relative to their static counterparts, for any given situation.The associated service-oriented architecture, known as Linked Environments for Atmospheric Discovery (LEAD), makes advanced meteorological and cyber tools as easy to use as ordering a book on the web. LEAD has been applied in a variety of settings, including experimental forecasting by the US National Weather Service, and allows users to focus much more attention on the problem at hand and less on the nuances of data formats, communication protocols and job execution environments.

The environmental eScience revolution

“…Weather forecasting and seasonal climate prediction are initial-value problems, while long-term climate prediction is a boundary-value problem, and the computing challenges are somewhat different. This difference is well illustrated by Droegemeier (2009), who shows a set of examples that bring together observations and advanced models in real time to improve predictions. Along with many other papers in this issue, he uses computing clusters to speed up modelling.…”

“…This difference is well illustrated by Droegemeier (2009), who shows a set of examples that bring together observations and advanced models in real time to improve predictions. Along with many other papers in this issue, he uses computing clusters to speed up modelling.…”

The evolution of the Web and implications for eResearch

“…The successes of eScience and grid computing have tended to focus on large projects with coordinated infrastructure, such as in the work of Droegemeier (2009). The Wikipedia definition of eScience notes: 'Due to the complexity of the software and the backend infrastructural requirements, eScience projects usually involve large teams managed and developed by research laboratories, large universities or governments'.…”