1. 1 Earth System Science: Understanding & Protecting Our Home Planet Ghassem R. Asrar, Ph.D Associate Administrator for Earth Science January 5, 2004

2. 2 IT Tech infusion into which of 6 ESE science foci? Some sciences? Infrastructure for all? Enabling tech?

3. 3 DRY WARM VERY WET WET VERY DRY COLD Petabytes 10 15 Terabytes 10 12 Gigabytes 10 9 Megabytes 10 6 Calibration, Transformation To Characterized Geophysical Parameters Interaction Between Modeling/Forecasting and Observation Systems Interactive Dissemination Multi-platform, multi-parameter, high spatial and temporal resolution, remote & in-situ sensing Advanced SensorsData Processing & Analysis Information Synthesis Access to Knowledge Extending Science & Technology to Society Predictions

4. ERBS Integrating Global Measurements Terra Aqua Grace IceSat QuikScat Sage SeaWinds TRMM Toms-EP UARS Jason Landsat 7 SORCE SeaWiFS ACRIMSAT TOPEX/Poseidon EO-1

5. 5 Applications of National Priority Agricultural Efficiency Air QualityInvasive Species Water Management Disaster Management Coastal Management Homeland Security Carbon Management Aviation Safety Energy Forecasting Public Health Ecological Forecasting From Tech Infusion point of view, are applications equal? The Reason CAN projects only cover a subset of apps Obviously, Air Quality is by far the most important one

6. 6 Integrated System Solutions Value & benefits to citizens and society Data Policy Decisions Management Decisions Predictions Observations High Performance Computing, Communication, & Visualization Decision Support Tools -Assessments w/ dynamic scenario ability -Decision Support Systems Monitoring & Measurements -Satellite - Airborne - in situ Science Models -Oceans- Ice -Land- Coupled -Atmosphere InputsOutputs OutcomesImpacts Standards & Interoperability

7. 7 Inspire the Next Generation of Earth Explorers Where are the key infusion spots in the education??

8. 8 Technology Information Synthesis: Distributed, Reconfigurable, Autonomous Access to Knowledge: On-orbit Processing, Immersive Environments Challenges to Enable Future Science - Laser/Lidar technology to enable Earth system science measurements - Large Deployables to enable future weather/climate/ natural hazards measurements - Intelligent Distributed Systems using optical communication, on-board reprogrammable processors, autonomous network control, data compression, high density storage - Information Knowledge Capture through 3-D Visualization, holographic memory and seamlessly linked models. This is us? Is this the territory that SEEDS can make big mark?

9. 9 Flight Operations, Data Capture, Initial Processing, Backup Archive Data Transport to DAACs Science Data Processing, Info Mgmt, Data Archive, & Distribution Distribution, Access, Interoperability, Reuse Spacecraft NASA Integrated Services Network (NISN) Mission Services WWW Value-Added Providers Interagency Data Centers Int’l Partners & Data Centers Data Acquisition Ground Stations Tracking & Data Relay Satellite (TDRS) Research Users Education Users Science Teams Data Processing & Mission Control Polar Ground Stations Data System Architecture DAACs - EOSDIS ESIPs REASoNs with ESIPs & REASoNs And this is how we can actually do it? With arrows (and bow )?

10. 10 Data System Architecture DAACs - EOSDIS ESIPs REASoNs with ESIPs & REASoNs Slide 17 Slightly Simplified OK, DAACs are are the primary sources of raw ESE data So, is SEEDS the facilitator between the DAACs and the immediate user layer? Research Users Education Users Value-added Providers International Data Centers Interagency Data Centers Science Data Processing & Info Management, Archive, Distribution, Access, Reuse SEEDS? Data Acquisition Data Flow Through the Value Chain

11. 11 Turning Observations Into Knowledge Products Were are we (REASoN) along the data-to-knowledge dimension?? Seeds does not deal with advanced sensors, right? Is infusion most vital in processing/analysis or in synthesis? (I think synthesis is most in need of software and ‘intellectual’ technologies

12. 12 Evolving EOSDIS Elements Evolve data systems to achieve “stability with innovation”. Current Data System Context EOSDIS operation volumes include: – 2,178 unique data products – 4.5TB of daily ingest – 2TB of daily distribution – Over 2 million distinct users for 2003 Approach to system evolution Work with the ESE advisory committee (ESSAAC) to develop a plan for the way forward (plan expected within a year). Identify which current systems and functions need to evolve, e.g., bandwidth and storage capacity Work with the community (e.g. REASoN) to implement changes What is the EODIS and SEEDS (NEWDIS) Relationship? Tight co-evolution? REASoN-to-EODIS tech transfusion? Use EOSDIS as a data server and build the SEEDS technologies on top of EODIS?

13. 13 Drivers of Evolving Data & Info Systems Missions to Measurements ESE is moving from mission-based data systems to those that focus on Earth science measurements. ESE’s DIS will be a resource for science-focused communities enabling research, and will be flexible, scalable and suited for the particular community needs. Continue on the pathways for acquiring observations to understand processes and develop Earth system models. The Advance of Information Technologies NASA will remain at the forefront of IT development and will partner with other agencies to ensure the strategic use of IT resources to avoid obsolescence and enable enhanced performance. The lowering cost of IT infrastructure enables ESE data systems to take advantage of improving computation, storage and network capabilities. Facilitate the Transition from Research to Operations Work with Federal partners to transition operational elements of data systems to other agencies while maintaining core data system functions necessary for conducting NASA ESE mission and goals. Refocus from Missions to Measurements ??? Why not from missions to science questions?

14. 14 Federation Contributions to the Evolution of EOSDIS The Federation has contributed to existing DIS capabilities through prototyping, partnering and implementation activities. Access and Interoperability -OpenDAP (A data protocol that has allowed the science community to be active participants in a distributed data infrastructure - interconnecting DAACs, ESIPs and others) -ESML (The Earth Science Markup Language provides a means for describing disparate data types to enhance search and service capabilities.) Data Analysis and Processing Tools -GIS-friendly formats (ESIPs offering data converted into GIS formats enabling rapid use of ESE data.) -Search, Discover and Order (Several new data portals where user communities can easily obtain the particular data needed - this has been very successful in the land research communities.) -Prototypes for Exploring Emerging Capabilities (Subsetting, reprojection, and aggregation; data mining and discovery tools). Federation Technologies to be infused through SEEDS? OpenDAP, ESML, GIS-Friendly, DataPortals, Prototypes

15. 15 ESE Strategy 2003

16. 16 ESE Technology Strategy The ESE technology program adopts an end-to-end approach to facilitate technology infusion.

17. 17 On the Context for IT Infusion Processes Driving Forces for IT Transfer Environmental Settings (Landscape Dimensions, Views Perspectives) Nodes and Directionality (IT Providers, Transformers, Users) Connectivity ( Contents of Transfer (Tools, Methods, Infrastructure) Notation:

18. 18 Strategic Evolution of ESE Data Systems - SEEDS and the ESIP Federation Briefing to the ESIP Federation July 29, 2003 Karen L. Moe Karen.L.Moe@nasa.gov SEEDS Study Team Catherine Corlan, Kathy Fontaine, Vanessa Griffin, Gail McConaughy, Ken McDonald, Karen Moe, H. Ramapriyan, Richard Ullman, Stephen Wharton SEEDS Web Page http://eos.nasa.gov/seeds

19. 19 Information System Challenges Development & exploitation of heterogeneous information systems Enable flexibility within data systems to adapt to new data stream(s) or to changes in current processing streams Create measurement oriented data systems within the SEEDS interoperable framework that will help guide the flow of information and services and improve performance and access. SEEDS as fabric: a mesh bridging Earth science data sets to the information web What & how does EOSDIS evolve into next generation distributed architecture Identify and create interfaces that facilitate the flow of data to modeling efforts (e.g. carbon assimilation) - “one size does not fit all”. Enable seamless ‘hooks’ into data mining and high performance computing environments. Leverage internet, plug & play

20. 20 SEEDS Mission: To establish an evolution strategy and coordinating activities to assure the continued effectiveness of ESE data management systems and services. SEEDS Objectives: Ensure timely delivery of Earth Science information at an affordable cost. Maximize availability and utility of ESE products. Engage the community on data management issues, objectives, and solutions. Enable the development of flexible systems to readily accommodate evolving products and services. The recent Research, Application, and Education Solutions Network (REASoN) cooperative agreement is the first implementation of the SEEDS framework. The Strategic Evolution of ESE Data Systems - SEEDS

21. 21 SEEDS Overview SEEDS Objectives 1. Ensure timely delivery of Earth Science information at an affordable cost. 2. Maximize availability and utility of ESE products. 3. Engage community on data management issues, objectives, and solutions. 4. Enable the development of flexible systems to readily accommodate evolving products & services. SEEDS Mission Establish evolution strategy and coordinating activities to assure continued effectiveness of ESE data management systems & services. Sustain & Apply Unifying Framework of Core Standards & Guidelines Format & Interface Standards & Processes Levels of Service Guidelines Data Lifecycle Planning Procedures Metrics Planning & Reporting Guidelines Foster Technology Evolution Provide Cost Estimation Tool * Carry Out IT Prototyping & Infusion Carry Out Software Reuse Initiatives Sustain Community Involvement Conduct Community Workshops Support Four Working Groups * Brief Science Committees & Organizations Support HQ Initiatives to Fund Distributed Providers of Products & Services Utilize SEEDS Paradigm Address Thematic Science Questions for Research, Education and Applications Support REASoN CAN Management * Support ESE Data Systems Evolution Planning Address key ESE data systems goals Coordinate data system evolution with implementing projects * Lead coordination/planning with national & international partners Support Transition to Measurement-Focused Paradigm Sustain and Apply Cost Estimation Tool * SEEDS Coordinating Responsibilities

22. 22 SEEDS Status Completed Study Recommendations: - Discussed draft recommendations at March 2003 workshop in Annapolis. - We have community buy-in for the recommendations. - Presented overview of recommendations to AA in March. Received action from AA to develop plan for evolution of ESE data systems. Working with ESDIS Project Manager to address this action (see slide next page). - Incorporated feedback and published final recommendations July 3, 2003. Supported REASoN CAN: - Contributed guidelines and selection criteria. - Supported evaluation, selection, awards process and milestone negotiation. SEEDS planning activities to continue in FY2004: - Determination of the appropriate scope, responsibilities, and resources for SEEDS will be made as part of the ESE data systems planning action. - Support working group participation by REASoN CAN awardees - standards, metrics, technology, reuse. - Develop options and recommend approach and budget for SEEDS coordinating functions.

23. 23 ESE Data Systems: Planning for the Future Work in partnership with ESDIS to apply SEEDS principles and guidelines to plan the evolution of ESE data systems: - SEEDS - focused on setting context for evolution of current data systems - ESDIS - focused on development, management and operation of Enterprise science data systems Address ESE key data systems goals: - Increase resources for higher level product generation - more science value - Increase community participation - Move to measurement based systems and away from mission-based systems - Increase utilization of smaller, distributed systems and reduce reliance on large, centralized systems - Enable the development of Climate Data Records - Closer collaboration between scientists and those planning and developing data systems Define evolutionary path and project plan that: - Meets ESE science and budgetary goals - Addresses data systems goals - Takes advantage of advances in technology - Utilizes existing assets to maximum ability - Is flexible enough to allow continued evolution

24. 24 SEEDS Working Groups SEEDS working groups specified in the REASoN CAN are not solely populated by REASoN winners, but rather will be augmented by the REASoN winners. - ESIP Federation members who have participated on the study teams are more than welcome to continue. The SEEDS management team is working with the REASoN study managers to refine the work plan of working groups. - REASoN negotiations are still in progress. - SEEDS is trying to figure out how best to accommodate the REASoN winners’ first or second choice of working group(s). It is necessary to balance the work load. - We anticipate starting this Fall, 2003. SEEDS working groups and Federation standing committees have similarities and differences in their mission, scope, approach. - SEEDS and the Federation will spend more time determining where the greatest leverage lies and work in collaboration.

25. 25 ESE Data Systems: Planning for the Future Work in partnership with ESDIS to apply SEEDS principles and guidelines to plan the evolution of ESE data systems: - SEEDS - focused on setting context for evolution of current data systems - ESDIS - focused on development, management and operation of Enterprise science data systems Address ESE key data systems goals: - Increase resources for higher level product generation - more science value - Increase community participation - Move to measurement based systems and away from mission-based systems - Increase utilization of smaller, distributed systems and reduce reliance on large, centralized systems - Enable the development of Climate Data Records - Closer collaboration between scientists and those planning and developing data systems Define evolutionary path and project plan that: - Meets ESE science and budgetary goals - Addresses data systems goals - Takes advantage of advances in technology - Utilizes existing assets to maximum ability - Is flexible enough to allow continued evolution

26. 26 Community Engagement John Townshend University of Maryland SEEDS Workshop Recommendation: It should be the highest priority for the current Formulation Team of the SEEDS project to develop and implement organizational structures facilitating much deeper engagement of key stakeholders. This action itself must involve some of these stakeholders and should start immediately. The success of SEEDS will strongly depend on the degree to which we engage all the communities supplying, analyzing, adding value and using NASA’s ESE products Benefits of CE

27. 27 Levels of participation Low High Deep Involvement Community Engagement ParticipationAwarenessOwnership Community Involvement

28. 28 Community Engagement Community engagement is a process, not a program. It is the participation of members of a community in assessing, planning, implementing, and evaluating solutions to problems that affect them. As such, community engagement involves interpersonal trust, communication, and collaboration. Such engagement, or participation, should focus on, and result from, the needs, expectations, and desires of a community's members.

29. 29 Principles of Community Engagement (derived from with some additions from www.cdc.gov/phppo/) 1.Be clear about the purposes or goals of the engagement effort, and the populations and/or communities you want to engage. The implementers of the engagement process need to be able to communicate to the community why participation is worthwhile. 2.Become knowledgeable about the community in terms of its economic conditions, political structures, norms and values, demographic trends, history, and experience with engagement efforts. Learn about the community's perceptions of those initiating the engagement activities. It is important to learn as much about the community as possible, through both qualitative and quantitative methods from as many sources as feasible. 3.Go into the community, establish relationships, build trust, work with the formal and informal leadership, and seek commitment from community organizations and leaders to create processes for mobilizing the community. Engagement is based on community support for whatever the process is trying to achieve. 4. Remember and accept that community self-determination is the responsibility and right of all people who comprise a community. No external entity should assume it can bestow on a community the power to act in its own self-interest. 5. Partnering with the community is necessary to create change and improve information systems.. 6. All aspects of community engagement must recognize and respect community diversity. Awareness of the various cultures of a community and other factors of diversity must be paramount in designing and implementing community engagement approaches. 7. Community engagement can only be sustained by identifying and mobilizing community assets, and by developing capacities and resources for community decisions and action. 8. An engaging organization or individual change agent must be prepared to release control of actions or interventions to the community, and be flexible enough to meet the changing needs of the community. 9. Community collaboration requires long-term commitment by the engaging organization and its partners.

30. NewDISS “Petri Dish” with Generic Federation Mapping ESIP-1 with LTA in-place LTA ESIP-1, ESIP-2, SIPS or SCF Backbone Data Centers Science Data Centers Long Term Archive Multi-Mission Data Centers Application Centers ESIP-2, ESIP-3, RESAC or RAC ESIP-1 or ESIP-2 ESIP-1 Mission Data Centers ESIP-2 or Pathfinder PI

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33. 33 Mapping Federation Components Into NewDISS Goodman showed the now familiar "petri dish" diagram, which is a theoretical mapping of how NewDISS components might fit together. Martha Maiden likes this idea. Certainly, the Science Plan is now the primary driving force behind all that goes on in the Earth Science Enterprise. The secondary objective is to make data available for all. She suggests a thematic look at the science questions addressed by Federation activities would certainly be useful. Victor Zlotnicki returned discussion to the "petri dish" that attempts to show how Federation partners would actually fit into a NewDISS mapping. His point was that to an outside observer, this would in fact look like total chaos. Might we not be better off tracing a single activity through the value chain? That is, it is probably more useful to focus on a single application and see how it expands outward and brings in various partners. The idea of a single chart showing all the interrelations might be unrealistic and also, in the end, not all that helpful. Dave Jones points out that this is the approach taken by most weather forecasts. Start out by showing local conditions and expand out to the national view to explain what factors have contributed to make the current conditions. A similar analogy would seem to apply for an Earth Science data product or service. Show the end product and trace back to show all the partners who contributed to the design. It could be a good way to introduce new people to what it is that the Federation does.

34. 34 Earth Science Enterprise, 2005++ NASA-Centric -> National Applications Expand the economical and societal benefits of ES information and technology: REASoN –Research, Education and Applications Solutions Network A distributed network of data and information providers for ES, applications and education projects 42 awards to government (21), university (16), commercial (3), and non-profit organizations (2) These projects unite previously disparate NASA Earth Science activities and programs Federation of Earth Science Information Partners –ESIPFed Improve science-based end-to-end processes, rhe quality and value of ES products and services Composed of 50+ agencies, universities, companies, non-profit orgs, REASoN projects Brings together scientists and organizations that have not worked together for the common good ESE Budget Summary: Preserving a robust Earth Science program Completing EOS first series; mission development budget ramps down accordingly EOSDIS becoming more efficient with EOSDIS Maintenance & Development contract Research program growing commensurate with availability new data from new missions Applications program level funded beyond FY05 Continuing commitments to Climate Change Science Program, international cooperative programs

35. 35 Case for Loose Coupling: A Network Science rationale The distribution of web connections are is ‘scale-free’ with power-law distribution of connections (Barabasi, 2000) The number of the number of links k originating from a given node exhibits a power law distribution. P(k) ~ k  The scale-free pattern of the Web is maintained by the continuous, (mostly) autonomous addition of new ‘nodes’ and links among the nodes. Any fixed linking structure (i. e. strong coupling) among the nodes fail to incorporate the new arrivals retain broken or obsolete links and in general will not be an agile, adoptive system in other words, it can not satisfy the original goals of SEEDS

36. 36 Value Chain The mission of ESE is better science Science is turning obs into actionable knowledge by the transforming data into knowledge by processing synthesis) Two parts of the data life cycle: Data Acquisition, Data usage focus on usage Data access -> transformation -> synthesis -> explanatory or actionable knowledge This is a Value chain! An increasing fraction of the Earth Science data are and are web accessible through a variety of ‘web services’

37. 37 ESIP Federation Insights on Technology Rob Raskin NASA/Jet Propulsion Laboratory California Institute of Technology

38. 38 DODS Server-side read/subset for most data formats Client-side integration with most visualization/ analysis tools (IDL, MATLAB, VisAD, GrADS) About 300 datasets available Data Access Protocol (DAP) to be separately developed and distributed One of the few ESIPs with a specific mission to work with other ESIPs

39. 39 DODS (cont.) Advantages Integration with science visualization software Disadvantages: Catalog system remains weak Data must be converted to intermediate format for transfer User interacts with array row/column parameters rather than geographic parameters

40. 40 WMS/WCS Open standards developed by Open GIS Consortium (OGC) Web Mapping Server (WMS) for maps; Web Coverage Server (WCS) for data NASA plays major role in standards development processes for WMS/WCS Eight WMS or WCS servers in place Advanced by Digital Earth Cluster (now GIS Services Cluster)

41. 41 WMS/WCS (cont.) Advantages Part of larger suite of standards, e.g. Web Feature Server (WFS) for vector data Enables overlay of disparate datasets Standards developed in conjunction with broader communities Disadvantages WCS still in development Complex data types generally not supported

42. 42 Peer-to-Peer (MODster) NAPSTER-like functionality for MODIS data Essentially a redirection service enabling users to find MODIS granules of interest Appropriate model for cases where multiple sites have similar data product

43. 43 WSDL/UDDI WSDL and UDDI provide Web service interoperability Standard way to access Web services Explored by IBM ESIP UDDIs currently for business services

44. 44 Technologies in Last Year’s Winning Proposals Universal Interchange Technology for Earth Science Data (UNITE) (UAH, JPL, ORNL) Plug & play based on ESML descriptors ESML, WCS integration into FIND Standards Framework in Support of Dynamic Assembly of NewDISS Components (BASIC, IBM, JPL, ORNL, JHU) WSDL/UDDI, WMS/WCS, FIND integration MODster (UCSB, DODS) Peer-to-Peer