Cyberinfrastructure for solving grand challenge problems in atmosphere meteorology climate
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Cyberinfrastructure for Solving Grand Challenge Problems in Atmosphere/Meteorology/Climate . Guy Almes, Kathy Carusone, Rudolph Dichtl, Mark Eakin, Jack Fellows, Mike Folk, Catherine Gautier, John Helly, Anke Kamratn,

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Cyberinfrastructure for Solving Grand Challenge Problems in Atmosphere/Meteorology/Climate

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Cyberinfrastructure for solving grand challenge problems in atmosphere meteorology climate

Cyberinfrastructure for Solving Grand Challenge Problems in Atmosphere/Meteorology/Climate

Guy Almes, Kathy Carusone, Rudolph Dichtl,

Mark Eakin, Jack Fellows, Mike Folk,

CatherineGautier, John Helly, Anke Kamratn,

Ron Murdock, Kurt Paterson, Mohan Ramamurthy, Russ Rew, Sue Stendebach, Steve Tanner,

Pat Waukau, May Yuan


Atmosphere meteorology climate grand challenges

Atmosphere/Meteorology/ClimateGrand Challenges

  • Aerosol: data assimilation/modeling/

  • Severe Wx (e.g. Flooding, Hurricanes): real-time data assimilation/modeling

  • Chemical Wx Forecasting: air quality forecast, near-term

  • Climate Prediction on annual and longer time scales

  • Cyber-sensor National/Global Observing Systems


Meeting grand challenges

Meeting Grand Challenges

  • Data assimilation: real-time and historical

    • Standard toolkit (API) for interfacing to sensors

    • Design sensors with cyberinfrastructure needs in mind

  • High compute cycle and data storage needs

    • Research needed to define resource requirements

  • Digital Library Integration

    • Data discovery

    • Support for comprehensive ontology development

  • Integration of data from multiple sources and disciplines

    • Global mapping/reference issues

    • Including integration of uncertainty

  • Integrate with GIS (Geovisualization, advanced true 4D GIS, new representational models)

  • Advanced Data mining

  • Grid computing (distributed – computational, data, software, …)

  • “Portability” of model results and observations

  • Need for scientific data models for scientific relational databases (major challenge for NSF)


Meeting grand challenges cont d

Meeting Grand Challenges (cont’d)

  • Leveling of the “playing field”

    • Increased accessibility to advanced Visualization tools to scientific and education community

    • Poor man’s access grid/Collaboratory

    • Accessibility to information on available resources (CI Portal)

  • Education, Outreach, Training, Visibility, Accessibility

  • Policy Issues: global geopolitical dimensions

    • Global cyberinfrastructure needs

  • Leverage Commercial Developments, but some areas not addressed:

    • Tool integration, multi-source data access and discovery, real-time collaboration

  • Data reduction and deduction tools

  • Long term support (interagency effort?)

    • Data persistence, Software (consider supporting open source model), IT Infrastructure


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