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

L inked E nvironments for A tmospheric D iscovery. Transforming the sensing and numerical prediction of high impact local weather through dynamic adaptation. Vision.

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

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  1. Linked Environments forAtmospheric Discovery Transforming the sensing and numerical prediction of high impact local weather through dynamic adaptation Vision Revolutionize the ability of scientists, students, and operational practitioners to observe, analyze, predict, understand, and respond to hazardous or severe weather by interacting with it dynamically and adaptively Motivation • Weather technologies are hard to use in sophisticated ways because they’re complicated and linked together using cumbersome software - creating aHUGE DIVIDE BETWEEN THE HAVES AND THE HAVE NOTS • High impact local weather isVERY DYNAMICwhile our tools, cyber environments and learning modalities are highlySTATIC GOALS • #1: Lowering the barrier for using complex end-to-end weather technologies • Democratize the availability of advanced weather technologies for research and education • Empower application in a grid context • Facilitate rapid understanding, experiment design and execution • #2: Dynamic Adaptation to Weather • Models and hazardous weather detection systems responding to observations and their own output • Models and hazardous weather detection systems driving the collection of observations • IT infrastructures providing on-demand, fault tolerant services What can LEAD do for you? • Researchers • Access to: Powerful tools for Assimilation, Prediction, Mining and Visualization • Supercomputing resources - TeraGrid • Data – real-time and recent data resources • Educators • Access to: LEAD Learning Communities – tools and colleagues • Classroom demonstrations – hands on access to tools • Data –real-time and recent data resources • Students • Access to: Resources to learn about and visualize the weather • With approval – create experiments access tools • Data –real-time and recent data resources Learn more at: http://portal.leadproject.org LEAD is sponsored by the National Science Foundation

  2. Linked Environments forAtmospheric Discovery http://portal.leadproject.org LEAD Provides Dynamic Capabilities through a Web Portal and Workflows Traditional NWP Methodology is Static 2 1 STATIC OBSERVATIONS Analysis/ Assimilation Prediction/ Model Graphical Domain Selection The Process is Entirely Prescheduled and Serial; It Does NOT Respond to the Weather! • Product • Generation, • Display, • Dissemination Drop down menu for selection of runs • End Users • NWS • Private Companies • Students How? : Built on (Web) Services [But in a Grid Framework] 3 Service A (Forecast Model) Service B (Terrain Preprocessor) Service C (Interpolator Service) Many others… How? : Workflow Generation 4 Pre-configured workflows automatically generated, which can be modified by users via a composition tool – launch manually or via trigger (dynamic) LEAD is sponsored by the National Science Foundation

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