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Earth System Science: Understanding

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Title: Understanding and Protecting Our Home Planet Author: SAIC ODIN Last modified by: rhusar Created Date: 3/13/2003 5:36:49 PM Document presentation format – PowerPoint PPT presentation

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Title: Earth System Science: Understanding


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

2
IT Tech infusion into which of 6 ESE science
foci? Some sciences? Infrastructure for
all? Enabling tech?
3
Extending Science Technology to Society
Interactive Dissemination
Calibration, Transformation To Characterized
Geophysical Parameters
Interaction Between Modeling/Forecasting and
Observation Systems
Petabytes 1015
Predictions
Terabytes 1012
Multi-platform, multi-parameter, high spatial and
temporal resolution, remote in-situ sensing
Gigabytes 109
Megabytes 106
Advanced Sensors
Data Processing Analysis
Access to Knowledge
Information Synthesis
4
Integrating Global Measurements
TOPEX/Poseidon
SORCE
SeaWiFS
ERBS
Jason
5
Applications of National Priority
Carbon Management
Aviation Safety
Energy Forecasting
Public Health
Water Management
Disaster Management
Coastal Management
Homeland Security
Agricultural Efficiency
Ecological Forecasting
Air Quality
Invasive Species
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
Integrated System Solutions
Science Models
- Oceans - Ice - Land - Coupled - Atmosphere
Value benefits to citizens and society
Predictions
High Performance Computing, Communication,
Visualization
Policy Decisions Management Decisions
Data
Standards Interoperability
Observations
Inputs Outputs Outcomes Impacts
7
Inspire the Next Generation of Earth Explorers
Where are the key infusion spots in the
education??
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
Data System Architecture
- EOSDIS
Flight Operations,
Science Data
Distribution,
Processing,
Data Capture,
Access,
Data
Info Mgmt, Data
Initial Processing,
Transport
Interoperability,
Backup Archive
Data Acquisition
to DAACs
Archive, Distribution
Reuse
Research
Users
Tracking
Spacecraft
Data
Relay Satellite
DAACs
(TDRS)
NASA
Integrated
Data Processing Mission Control
Services
Network (NISN) Mission Services
Education
Users
Ground Stations
WWW
Value-Added
Providers
Science Teams
Intl Partners
Interagency
Polar Ground Stations
Data
Centers
Data
And this is how we can actually do it? With
arrows (and bow ?)?
Centers
10
Data System Architecture
- EOSDIS
with ESIPs REASoNs
Data Flow Through the Value Chain
Science Data Processing Info Management,
Archive, Distribution, Access, Reuse
Research Users
ESIPs
Education Users
Data Acquisition
DAACs
Value-added Providers
REASoNs
International Data Centers
Interagency Data Centers
SEEDS?
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?
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
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
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.
  • ESEs 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
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
ESE Strategy 2003
16
ESE Technology Strategy
  • The ESE technology program adopts an end-to-end
    approach to facilitate technology infusion.

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
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_at_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
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
The Strategic Evolution of ESE Data Systems -
SEEDS
  • 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.

21
SEEDS Overview
  • 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 Mission Establish evolution strategy and
coordinating activities to assure continued
effectiveness of ESE data management systems
services.
  • Sustain Community Involvement
  • Conduct Community Workshops
  • Support Four Working Groups
  • Brief Science Committees Organizations

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 Coordinating Responsibilities
  • Foster Technology Evolution
  • Provide Cost Estimation Tool
  • Carry Out IT Prototyping Infusion
  • Carry Out Software Reuse Initiatives
  • Sustain Apply Unifying Framework of Core
    Standards Guidelines
  • Format Interface Standards Processes
  • Levels of Service Guidelines
  • Data Lifecycle Planning Procedures
  • Metrics Planning Reporting Guidelines
  • 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

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
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
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
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
Community Engagement
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
NASAs ESE products
  • John Townshend
  • University of Maryland

Benefits of CE
27
Levels of participation
Community Engagement
Community Involvement
Low
High
Deep Involvement
Participation
Awareness
Ownership
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
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-2 or Pathfinder PI
ESIP-1
ESIP-1, ESIP-2, SIPS or SCF
ESIP-1 or ESIP-2
ESIP-1 with LTA in-place
LTA
ESIP-2, ESIP-3, RESAC or RAC
Backbone Data Centers
Science Data Centers
Long Term Archive
Multi-Mission Data Centers
Application Centers
Mission Data Centers
31
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32
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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
Earth Science Enterprise, 2005
  • NASA-Centric -gt 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
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-g
  • 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
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 -gt transformation -gt synthesis -gt
    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
ESIP Federation Insights on Technology
  • Rob Raskin
  • NASA/Jet Propulsion Laboratory
  • California Institute of Technology

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
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
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
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
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
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
Technologies in Last Years 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
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