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Large-Scale Data Management Challenges Climate, Water, and Weather Data

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  1. Large-Scale Data Management ChallengesClimate, Water, and Weather Data Kenneth Galluppi Director, Disaster and Environmental Programs Renaissance Computing Institute University of North Carolina at Chapel Hill NOAA - National Climatic Data Center Ed Kearns

  2. Goal of Collaborations • Enable cutting edge, Grand Challenge multidisciplinary science through the federation of data-grids of climate, hydrological, and weather data, with other geospatially and socially relevant datasets. • Understanding of regional impacts of climate change on water availability and society trends • Understanding and prediction of catastrophic weather-driven events under climatatic change • Communicate risk/crisis knowledge non-specialists

  3. Challenges of Data • Integration of Large, Multidisciplinary Datasets • NCDC and NOAA Centers, SDSC, and others • Discover, access, integration, utility [not store/retrieve] • Linkage of Datasets to Computational Models • Input/outputs for real-time model forecasting • Model-to-observation comparison • Climatic models for reanalysis and prediction • Access to Large Reference Data • Climate Reanalysis Datasets, 1 PetaByte • NWS DataCube for aviation and emergencies

  4. Collaboration and Datagrids Academic Research Federal Agencies National Climatic Data Center Emergency Management Research Program

  5. Data Supports NOAA/NCDC Mission NCDC’s Place in NOAA’s Mission NOAA Mission: To understand and predict changes in Earth’s environment and conserve and manage coastal and marine resources to meet our nation’s economic, social, and environmental needs NOAA Goals: Climate Understand Climate Variability and Change to Enhance Society’s Ability to Plan and Respond Weather & Water Serve Society’s Needs for Weather and Water Information Commerce & Transportation Support the Nation’s Commerce with Information for Safe, Efficient, and Environmentally Sound Transportation Ecosystems Protect, Restore, and Manage the Use of Coastal and Ocean Resources through an Ecosystem Approach to Management Mission Support Provide Critical Support for NOAA’s Mission

  6. Data supports NOAA/NCDC Mission • NCDC will need to function in a wider information landscape with a NOAA Federated Archive • Support distributed data management and services • Interoperable with DataNet, Earth System Grid, GEO-IDE, EOSDIS, etc. • netCDF, LDM, CF conventions, ISO 19115-2 • Move out of the Box and into the Cloud (networked) • Utilize highly distributed storage and computing (RENCI, Oak Ridge National Lab • Implement supporting technologies to enable interoperability with Designated Communities (OGC, WMS/WFS) • Institute rules-based data management to enable true federation of NOAA Centers of Data – iRODS

  7. The National Environmental Data Archive Comprehensive Large Array-data Stewardship System (CLASS) Storage (reanalysis)

  8. NOAA’s Data Centers Will Function in a Wider Information Landscape NSF DataNet ORNL, ESG DAPs Data Mgmt NEAAT IPCC International Sources

  9. Climate Services using Federated DB’s • NOAA’s Data Centers will need to provide access to petabytes of data that are distributed across multiple NOAA facilities • Be able to integrate these data with data from other disciplines (environmental, biological, social, etc..) that are distributed on other databases both in the public and private sector domain • Export data to common data formats - Shapefile, Well-Known Text, Arc/Info ASCII GRID, Gridded and Raw NetCDF, GeoTIFF and KMZ (Google Earth) • Support : • Disaster reduction • Human Health • Climate • Water Resources • Weather • Ocean Resources • Agriculture & Land-Use • Ecosystems

  10. NOAA/NCDC Climate Services

  11. NCDC-RENCI Potential Use Cases • Catastrophic Event Modeling and Observations • Climate Reanalysis Datasets • Climate records everywhere, for 30 years • 1-PetaByte • Regional and local sub-setting • Ten’s of thousands of users • Multi-sensed Gridded Precipitation Climatology • Extreme Event Climatology • Green Energy, physical-social science Integration

  12. Hydrology Community High Level View of HIS Service Oriented Architecture As of October 2009, 1,867,108 sites and 4,336,790,286 data values where available through the HIS from federal, state, and academic data providers. There have been 543,144 “GetValues” data requests from Feb 2008 to Oct 2009 . http://his.cuahsi.org

  13. HIS Service Oriented Architecture

  14. Maximize Data Access and Utility

  15. Data and Model Integration Neededto Support Hydrologic Science DFC Observations Hydrologic Models Physical Data Weather and Climate Models Socioeconomic Data CUAHSI HIS

  16. Meteorology, Hydrology, Ecological Models TOPS TOPS CHPS RHESSYS ADAS WRF HEC-RAS RHESSys ADCIRC ADCIRC StateClimateOffice Sensor Data Bus • Sensor Cloud • National Weather Service • Department of Transportation / FAA • USGS NWIS, USFS • Buoys, Stream Gauges, Soil Moisture • People with mobile devices • etc … Enablement

  17. Use Case: National Water Model Flooding in the Mississippi River Basin, August 1993 observed from satellite imagery Hydrologic scientist have expressed a “grand research challenge” of building a National Water Model for flood and drought applications. Terrain in the Neuse River Basin, NC constructed from 390 million LiDAR measurements Source: terrain.cs.duke.edu Achieving this goal will require a system like DFC to handle the massive data requirements. Source: nasa.gov

  18. CUAHSI Case Study Hydrology Grand Challenge Problem: National Water Model How much water is available in the Nation’s water resources? Currently, hydrologic models are implemented at the watershed-scale (county) Hydrologists plan to scale physically-based models to national level Provide CI, Policies & Sustainability for Water Model Data Gathering, analysis, dissemination and preservation Policies for quality control, metadata harvesting, versioning and usage Enables the data required for real-time analysis for flood and drought modeling Enables integrating data from “new sources” Enables new science, outreach, decision making and disaster recovery Integration of Predictive Models, Real-time Data and Historic Data

  19. Technical Solutions • Too many systems/solutions, home grown to programs (CUAHSI) • Standards (ODM, OGC, Virtual USA, etc. • Federal enterprises • NOAA, CLASS general, heavy system • Oracle front end to large tape system • Unique • Handling large sets with limited skills • Multidisciplinary, formats are not enough, but knowledge • Federal • Has to work, has to preserve • Observation systems are getting more complex • Users are more sophisticated and demanding more