1. 1 Large-Scale Data Management Challenges Federating Climate, Water, and Weather Data Repository/Workspace Workshop 20-21 September 2010 Kenneth Galluppi Director, Disaster and Environmental Programs Renaissance Computing Institute University of North Carolina at Chapel Hill

2. Outline Environmental Problem Use Case – Climate and Weather – Hydrology Data Grid/Workspace Use Cases Answer Peter’s Questions

3. 3 Environmental Science Problems Enable cutting edge, Grand Challenge multidisciplinary science through the federation of data-grids of climate, water, 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 climate change – Communicate risk/crisis knowledge non-specialists

4. 4 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

5. 5 Collaboration and Datagrids National Climatic Data Center Emergency Management Research Program Federal Agencies Academic Research 140 universites

6. NOAA Mission: Weather & Water Serve Society’s Needs for Weather and Water Information Ecosystems Protect, Restore, and Manage the Use of Coastal and Ocean Resources through an Ecosystem Approach to Management Climate Understand Climate Variability and Change to Enhance Society’s Ability to Plan and Respond 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 National Climatic Data Center Commerce & Transportation Support the Nation’s Commerce with Information for Safe, Efficient, and Environmentally Sound Transportation Mission Support Provide Critical Support for NOAA’s Mission NOAA Goals: Data Supports NOAA/NCDC Mission

7. The National Environmental Data Archive Climate Analysis RADAR Satellite Other

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

9. NOAA CLASS Large Structured data Propriety Doesn’t interface with HPSS Climate Support of products and services Does well, what it does

10. NOAA’s Data Centers Will Function in a Wider Information Landscape NCDC NGDC NODC NSOF

11. NOAA’s Data Centers Will Function in a Wider Information Landscape

12. ORNL, ESG NSF DataNet DAPs Data Mgmt IPCC International Sources NEAAT

13. 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

14. NOAA/NCDC Climate Services

15. Data supports NOAA/NCDC Mission NCDC will need to function in a wider information landscape with a NOAA Federated Archive (6 data centers) – 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

16. 16 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

17. 17 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 Hydrology Community

18. CUAHSI HIS The CUAHSI Hydrologic Information System (HIS) is an internet based system to support the sharing of hydrologic data. It is comprised of hydrologic databases and servers connected through web services as well as software for data publication, discovery and access. Data Discovery and Integration platform Data Publication platform Data Synthesis and Research platform Data Services Metadata Services Metadata Search HIS Central HydroDesktopHydroServer Service registration Catalog harvesting Service and data theme metadata Data carts Water Data Services Spatial Data Services Like search portals Google, Yahoo, Bing Like browsersLike web servers Like HTML

19. 19 HIS Service Oriented Architecture

20. Publication of Point Observations Observations Data Model (ODM) – ODM Tools – ODM Data Loader – ODM Streaming Data Loader – ODM Controlled Vocabularies WaterOneFlow web services – Data are transmitted in WaterML format

21. Dynamic Controlled Vocabulary Moderation System Local ODM Database Master ODM Controlled Vocabulary HIS CV Website ODM Controlled Vocabulary Moderator ODM Data Manager ODM Controlled Vocabulary Web Services ODM Tools Local Server XML http://his.cuahsi.org/mastercvreg.html

22. Horsburgh, J. S., D. G. Tarboton, D. R. Maidment and I. Zaslavsky, (2008), A Relational Model for Environmental and Water Resources Data, Water Resour. Res., 44: W05406, doi:10.1029/2007WR006392. CUAHSI Observations Data Model http://his.cuahsi.org/odmdatabases.htmlhttp://his.cuahsi.org/odmdatabases.html

23. Maximize Data Access and Utility

24. 24 Data and Model Integration Needed to Support Hydrologic Science Observations Hydrologic Models Weather and Climate Models Physical Data Socioeconomic Data CUAHSI HIS DFC

25. ODM WaterOneFlow HydroServer Capabilities Database ODM Databases and Web Services ArcGIS Server Spatial Data Services Capabilities Database Configuration Tool Spatial Services WaterOneFlow Services

26. ODM WOF National Dataset Water Data Services NWIS WOF ODM WOF ODM WOF ODM WOF HydroServer Distributed Water Data Services STORET WOF DAYMET WOF Metadata Catalog Ontology HydroDesktopHIS Central Ontology Services Metadata Services HydroDesktop Plug-ins Desktop Data Repository Search, Download, and Manage Data Subscriptions Visualize and Summarize (TSA) Convert Units Convert Formats and Export Import Files Data Discover y Calls Web Service Calls WaterML SNOTEL WOF … Metadata Harvesting Variable Mapping ODM WOF R MATLAB Excel Desktop Analysis Software Workflow Database API

27. 11 WATERS Network test bed projects 16 ODM instances (some test beds have more than one ODM instance) Data from 1246 sites, of these, 167 sites are operated by WATERS investigators National Hydrologic Information Server San Diego Supercomputer Center HydroServer Implementation in WATERS Network Information System

28. RHESSys TOPS ADAS Meteorology, Hydrology, Ecological Models WRF RHESSYS HEC-RAS ADCIRC Scientific Research Historical Re-Analysis Disaster Planning Disaster Response Agricultural Forecasts Ag Decision Support Public Dissemination Economic Planning etc … Sensor Data Bus TOPS State Climate Office Sensor Cloud National Weather Service Department of Transportation / FAA USGS NWIS, USFS Buoys, Stream Gauges, Soil Moisture People with mobile devices etc … CHPS Enablement

29. 29 Use Case: National Water Model Terrain in the Neuse River Basin, NC constructed from 390 million LiDAR measurements 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. Achieving this goal will require a system like DFC to handle the massive data requirements. Source: nasa.gov Source: terrain.cs.duke.edu

30. 30 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

31. 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

32. Data Management Large Storage Systems Compute and Servers Firewall Security HPCC Compute iRODS Workflow Data Manage DataNet Data Management, Data Grid Testbed

33. Diversity in the Landscape Data grids to include generic data management infrastructure – Data sharing – Digital libraries, publish and discovery – Persistent archives for preservation – Data processing pipelines – Virtualize data collections File systems Tape archives Cloud storage Institutional repositories Digital repositories

34. Diversity in the Landscape Policy-based Data Management – Each center has same management needs but implement different policies and procedures – Implement their own policies but leverage standard data management – Interoperate with other repositories through specific drivers that implement protocol Integrated Rule Oriented Data System (iRODS)

35. How to Federate? Users, services and local storage Clients – present information in context – User level file systems – Web browsers – Web services Workflow – manage processing steps Data grid – access to the repositories – Uniform name space – Properties (meta) and access (time stamp, version) – Policies – retention, disposition, authenticity, QA Storage Systems – tapes, file system, cloud

36. Safe Replication Repositories must be replicated Data grids are good at this – Making copies – Keeping track of copies – Integrity of copies – Disposition of copies (rules for retention and checking)

37. Policy Rules for Control Actions that simplify use of data – Data sharing: access control, distribution, organizing – Publishing: Descriptive metadata, integrity, replication – Data preservation: retention, disposition, trust, ownership Data ingestion, storage, and access control

38. User Workspaces Needed for interim data products Track operations performed on the data – Same needs as repositories, only shorter timeframe – Individual, organization, operation processing

39. Processing and workspaces Process of petabytes collections and distributed processing Process at local storage if simple processing Move file is processing is complex or demanding. Data management views processing transparently and facilitates: – Move files – Manage processing and workspace

40. Frameworks for distributed processing iRODS – integrated Rule Oriented Data System – Internal workflows (rules of microservices) – External workflows (Taverna, Kepler, Pegasus) – Data management decoupled from workflows and both can be distributed Data interchange with workflow – Parameter passing (microservice) – In-memory structures (workflow and microservice) – In-memory, but distrubuted – Shared metadata, retrieved out of catalog – Shared files