Real-time storm detection and weather forecast activation through data mining and events processing |
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| Authors: | Xiang Li Beth Plale Nithya Vijayakumar Rahul Ramachandran Sara Graves Helen Conover |
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| Institution: | (1) University of Alabama Huntsville, Huntsville, AL, USA;(2) Indiana University, Bloomington, IN, USA |
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| Abstract: | Each year across the USA, destructive weather events disrupt transportation and commerce, resulting in both loss of lives
and property. Mitigating the impacts of such severe events requires innovative new software tools and cyberinfrastructure
through which scientists can monitor data for specific severe weather events such as thunderstorms and launch focused modeling
computations for prediction and forecasts of these evolving weather events. Bringing about a paradigm shift in meteorology
research and education through advances in cyberinfrastructure is one of the key research objectives of the Linked Environments
for Atmospheric Discovery (LEAD) project, a large-scale, interdisciplinary NSF funded project spanning ten institutions. In
this paper we address the challenges of making cyberinfrastructure frameworks responsive to real-time conditions in the physical
environment driven by the use cases in mesoscale meteorology. The contribution of the research is two-fold: on the cyberinfrastructure
side, we propose a model for bridging between the physical environment and e-Science1 workflow systems, specifically through events processing systems, and provide a proof of concept implementation of that model
in the context of the LEAD cyberinfrastructure. On the algorithmic side, we propose efficient stream mining algorithms that
can be carried out on a continuous basis in real time over large volumes of observational data.
1 e-Science is used to describe computationally intensive science that is typically carried out in highly distributed network |
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| Keywords: | Cyberinfrastructure e-Science Weather forecast Data mining Workflow-driven analysis |
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