Presentations

1
Presentations

• NEON Overview Bill Michener
• CI Overview Chaitan Baru
• Sensor Networking Overview Deborah Estrin
• Governance and Budget Issues Bill Michener

2
William MichenerAmerican Institute of
Biological Sciences U. New MexicoLTER
The United States National Ecological Observatory
Network (NEON) Science, Education and Enabling
Infrastructure

3
Agenda

• The Design Directive
• Science, Education, and NEON Infrastructure
• Partnership Opportunities
• Progress and Next Steps

4
Agenda

• The Design Directive
• Science, Education, and NEON Infrastructure
• Partnership Opportunities
• Progress and Next Steps

5
NEON A Bold Initiative for Big Science NSFs
Major Research Equipment and Facilities
Construction Process
scientific research that requires a massive
capital investment, that involves large teams of
scientists, and that is expected to yield very
significant resultsi.e., transform the science
6
The Design Directive

1. Objectively design a continental-scale
   observatory.
2. Prioritize the science and the infrastructure.
3. Accurately determine the costs.

7
Agenda

• The Design Directive
• Science, Education, and NEON Infrastructure
• Partnership Opportunities
• Progress and Next Steps

8
Observational Requirements Constraints
General Sub System Requirements
1) PSF Concentration (single exp.) a) FWHM (per
filter) lt0.8 b) Encircled Energy (d50, d80) TBD
Science Missions
Derived System Requirements
Physical and Operational Constraints
Camera
Telescope
Data Mgmt
Site
Chromatic Response 350-1100nm (310-350nm
usable) (6)
2) PSF Concentration (stacked) a) FWHM (per
filter) lt0.8 b) Encircled Energy (d80, d50) TBD

1. Optical System

II. Astro-climate a) Median seeing lt 0.7 b) 70
clear nights
Latitude
a) Field of View 3.5 Deg diameter b) Image
Quality lt 0.3 c) Collective aperture gt 6.5m2 d)
5 Filters
Science Missions
Observational Programs
3) PSF Shape a) single exp elt0.1 b) stacked
elt0.001
Weights
Collecting Area (Effective) gt6.5m2 (2a, 2b, 8a)
I) Strong Lensing
Analytic Methods (Observing Programs)

• III. Optical Efficiency

Weather
II) Weak Lensing
4) PSF Shape Stability a) s(ixx,iyy,ixy) lt 0.002
per visit b) e lt 0.0002 on gt5 scales in final
stack
a) Throughput gt 60 effective (600-800nm)
Derived System Requirements
Observational Programs
Omega (Effective) gt8.7deg.2 (5a, 7, 14)
III) SN Wide
Community Science Access
f) Extinction g) Sky brightness
h)
b) Fill factor gt90
d) Stray / Scattered Light

1. PSF Shape Analysis(I, II, V, XII)

Other
Dark Energy/ Dark Matter
c) High saturation level
e) Coating ( 310-1100nm)

IV) SN Deep
5) Celestial Regional Constraints Areal
Coverage a) High Galactic Lat. (bgt20) 15000
deg.2 b) Ecliptic plane 2000 deg.2 c) Low
Galactic Lat. (blt20) 3500 deg.2 d) Nearby
Galaxies TBD deg.2 e) Selected Deep Areas
500(SN) 500(KBO) deg.2

• Industrial Capacity
• Maximum Mirror Size 8.4 M diam.

c) Well Depth
Exposure Time 10-15 sec. (1a, 1b, 3a, 4a, 8a, 9a)
V) Linear Density Perturbations / Growth of
Structure
B) Photometric Redshifts(I, II, III, IV,V, VI,
XIV)
Image Quality
VI) Cluster Counting
Spatial Sampling 0.2/pixel (3, 4, 9, 10, 11, 12)
C) Image Differencing(I, III, IV, VII, VIII, IX,
X, XI, XII, XIII, XIV, XV, XVI, XVII)
Calibration
Methods
IV. Image Quality
6) Filter Complement (spectral bands) grizY (plus
usable performance in u)
Public Education Access
a) Pixel scale 0.2
c) Low wind perturbations d) Strong bedrock
b) Tracking 0.02 RMS
e) PSF
Subsystem Requirements
VII) Kuiper Belt Objects

D) Precision Photometry Stellar(I, III,
IV,XII?, XVII, XVIII, XIX, XX XXI?)
7) Cadence (time between visit / duration) a)
Wide SN once per 3 nights (per filter x 3) over
3 months b) Deep SN once per 4 nights (per
filter x 4) over 3 months c) SS Map twice within
30min repeated twice per lunation d) KBO once
per 2 months e) Transients on selected classes
once per minute over several minutes f)
Variables log distribution between visits g)
Micro Lensing every 4 nights h) GRB once every
week
VIII) Near Earth Asteroids (PHA)
Alignment Good telescope focal plane IQ without
camera optics
Map Solar System Bodies
Obs Req. and Const.
IX) Main Belt Asteroids
E) Precision Photometry Extended(I, II, VI, X)
V. Reliability/Lifetime
Algorithm and Science Evolution
X) Comets
a) Low Maintenance, Safe easy handling
b) Low downtime 3 days/month, spares
Monetary
F) Image Position(VII, VIII, IX, X?,XI, XIX, XX)
c) Minimum lifetime 10 years
NEON Implementation
e) Easy access
d) Coating facility on site
f)
Efficiency
XI) Gamma Ray Bursters
8) Depth a) Single Exposure 24thAB _at_10s sky
noise limited b) Stacked 29thABarcsec-2 _at_10s,
points source V26th, KBOs R27th
Sky Access gt25000 deg.2 (5, 15)
XII) Micro-Lensing
G) Moving Object Linkage(VII, VIII, IX, X,)
VI. Observing Cadence
XIII) Eruptive non-Periodic Variables(Galactic
Extragalactic)
a) Readout 2sec
g) Data handling h) Data processing i) Latency
c) Blind pointing 3 RMS d) Slew settle 5sec
for 3.5deg slew
e) Data transport 1Gb/sec f) Latitude /- 35
H) Time Series Analysis(I, III, IV, XI, XII,
XIII, XIV, XV, XVI, XVII, XVIII, XIX, XX)
Signal
b) 5 filters readily available
9) Dynamic Range a) Single Exposure 7
magnitudes?? b) Stacked TBD
Transient Discovery and Time Domain
XIV) Active Galactic Nuclei
VII. Operating conditions
Time
I) Large Angular Scale Dependencies(II, III, V,
VI, XVIII, XIX, XXI)
XV) Exo-planet Transits and Occultations
10) Photometry (incl. distribution) a) Precision
?0.02, lt1 errors gt3 ? s b) Accuracy ?0.02, lt1
errors gt3 ? s
a) Blind Spot 3deg b) RH lt 85 c) Wind speed lt
43 knots
Computational
XVI) Unknown Events
11) Astrometry (single visit, inc.
distribution) a) Precision ?0.030 arcsec, lt1
errors gt3 ? s b) Accuracy ?0.060 arcsec, lt1
errors gt3 ? s
J) Non-LSST Supporting Observations / Data(I,
III, IV, X, XI, XII, XIII, XVI, IXX)
XVII) Low Amplitude Stellar Variability
VIII. Operational support
a) Logger b) Support Equipment
K) Image Stacking(I, II, III, V, VI, VII, XIV,
XVII, XIX, XX, XXI)
12) Proper motion accuracy (aggregate)
?0.002/year
d) Data storage 30 PBytes e) Data access
c) Seeing Sky monitors
XVIII) Galactic Halo Structure from Periodic
Variable Stars
13) Latency (transient) 1 minute ???
14) Number of Visits (per filter) a) WL 75 in
each of grizY filter b) Deep SN 230 in each of 4
filters per field c) SS Map 520 in any one of
gri filters d) KBO 4 in r filter e) Micro
Lensing 500 in one filter g) Galactic Map 70 in
each of griz
L) Static Object Association Aggregation (I,
III, IV, VI, XI, XII, XIV)
XIX) Galactic Streams from Proper Motions
Precision Colors
VIIII. Operational Control
Assembly of the Galaxy and Solar Neighborhood
c) Pipelines
a) OCS, scheduler
M) Large Sample Statistics(II, III, IV, V, VII,
IX, XVI, XIX, XXI)
XX) Solar Neighborhood Census from Stellar
Parallax
d) Data Quality Feedback
b) Data Quality Control
Science Requirements
Engineering Requirements
15) Airmass limits (per filter)
XXI) Stellar Population Density Structure in
the Galaxy
16) Stray scattered light rejection (TBD may
need to move on the right hand side)
Engineering Req. Docs
Science Req. Doc.
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The NEON Mission To provide the capacity to
forecast future states of ecological systems for
the advancement of science and the benefit of
society.

• Two Overarching Questions
• How are ecosystems affected by variations in
  climate and changes in land use?
• How will the patterns and movements of organisms
  be affected by variations in climate and changes
  in land use?

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Balances of Mass and Energy in Ecosystems Impacted by Land Use, Land Cover, and Vegetation Balances of Mass and Energy in Ecosystems Impacted by Land Use, Land Cover, and Vegetation Balances of Mass and Energy in Ecosystems Impacted by Land Use, Land Cover, and Vegetation Balances of Mass and Energy in Ecosystems Impacted by Land Use, Land Cover, and Vegetation

NEON Measured or Calculated Variable Variable Sensitive to Land Use and Land Cover Change Variable Sensitive to Vegetation Attributes
Surface Energy Balance
Direct solar radiation ?
Diffuse solar radiation ?
Surface terrestrial radiation (upward) ? ? ?
Atmospheric terrestrial radiation (downward) ?
Surface albedo ? ? ?
Sensible heat loss to atmosphere ? ? ?
Latent heat loss to atmosphere ? ? ?
Heat storage ? ? ?
Surface Water Balance
Precipitation ?
Runoff and discharge ? ? ?
Infiltration ? ? ?
Storage ? ?
Evapotranspiration ? ? ?
Aerodynamic Balance
Displacement length ? ? ?
Surface roughness ? ? ?
Winds (u, v and W) ? ?
Shear stresses ? ? ?
Vertical wind profile ? ? ?
Friction velocity ? ? ?
Atmospheric density ?
Temperature profile ? ? ?
Vapor pressure profile ? ?

NEON measures important feedbacks between the biosphere and the atmosphere that are associated with alterations in land use, land cover, and vegetation. Surface energy, surface water, and aerodynamic balances are important components of these feedbacks and vary in their sensitivities to changes that occur across the diversity of landscapes. NEON measures important feedbacks between the biosphere and the atmosphere that are associated with alterations in land use, land cover, and vegetation. Surface energy, surface water, and aerodynamic balances are important components of these feedbacks and vary in their sensitivities to changes that occur across the diversity of landscapes. NEON measures important feedbacks between the biosphere and the atmosphere that are associated with alterations in land use, land cover, and vegetation. Surface energy, surface water, and aerodynamic balances are important components of these feedbacks and vary in their sensitivities to changes that occur across the diversity of landscapes. NEON measures important feedbacks between the biosphere and the atmosphere that are associated with alterations in land use, land cover, and vegetation. Surface energy, surface water, and aerodynamic balances are important components of these feedbacks and vary in their sensitivities to changes that occur across the diversity of landscapes.

11
NEON Science

• How are ecosystems affected by variations in
  climate and changes in land use?
• How will ecosystems respond to changes in land
  use and climate across a range of spatial and
  temporal scales? And, are the responses gradual
  or abrupt?
• How do changes in land use and climate influence
  the movement of water and materials from
  terrestrial to aquatic ecosystems? And, how does
  this affect nutrient dynamics and ecosystem
  metabolism?
• How will the patterns and movements of organisms
  be affected by variations in climate and changes
  in land use?
• How will plant and animal biodiversity respond to
  land use change and climate variations? And, do
  changes in biodiversity have a reciprocal effect
  on land use and climate?
• How do changes in land use and climate affect the
  spread of infectious diseases and invasive
  species? And, what are the ecological
  implications?

12
NEON Climate Domains
Northeast
Mid Atlantic
Southeast
Atlantic Neotropical
Great Lakes
Prairie Peninsula
Appalachians / Cumberland Plateau
Ozarks Complex
Northern Plains
Central Plains
Southern Plains
Northern Rockies
Southern Rockies / Colorado Plateau
Desert Southwest
Great Basin
Pacific Northwest
Pacific Southwest
Tundra
Taiga
Pacific Tropical
13
NEON Deployment
14
NEON Infrastructure Overview
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BioMesoNet Measurements

• Basic 10 Meter Tower- Air temperature (at 10 m,
  1.5 m, 10 cm, 0 cm)
• - Barometric pressure (at 1.5 m)
• Relative humidity (at 10 m, 1.5 m 2 other
  canopy-dependent heights)
• - Precipitation (rain snow liquid equivalent)
• Wind speed direction (at 10 m, 1.5 m 2 other
  canopy-dependent
• heights)
• - Soil moisture (at -2, -30, -100 cm)
• - Soil temperature (at -5, -15, -30cm)

• Advanced 10 Meter TowerBasic components plus
• - Incoming, reflected, total diffuse solar
  radiation (at 1.5 m)
• - Sensible and latent heat CO2 fluxes
• - CO2 concentration (at 8-10 vertical levels from
  ground to above canopy)
• - H2O vapor (at 8-10 vertical levels from ground
  to above canopy)
• - Stable isotopes of C O in H2O CO2
• - CH4 concentration
• - CO concentration (at 3-5 m)
• - NO, NO2, NOx concentrations
• - O3 concentration (at 3-5 m)
• - Trace biogenic gas concentrations (e.g.,
  carbonyls, alcohols, aldehydes,
  BTX-compounds)
• - Airborne particulates (e.g., pollen, bacteria)
• - Dry deposition of SO42-, NO3-, NH4, SO2,
  HNO3
• - Wet deposition of NH4, NO3-, o-PO43-, SO42-,
  Cl-, Ca2, Mg2, K, pH
• - Leaf/canopy condition (moisture, incidence of
  disease, remote sensing calibration)
• Leaf wetness (at 10 m, 1.5 m 2 other
  canopy-dependent heights)

16
Terrestrial Sensor Measurements
Acoustic Array - Biological audio recordings

• Canopy Climate Sensor Nets
• - Total, diffuse, incident photosynthetically
  active radiation (PAR)
• - Sunshine duration
• Biological temperature (i.e. soil/leaf/canopy
  surface temperature)
• - Air temperature (at 10 m, 1.5 m, 10 cm, 0 cm,
  Climate only)
• - Relative humidity (at 0 m 1.5 m, Climate
  only)
• - Precipitation (rain snow liquid equivalent,
  Climate only)

Soil Sensor Net - Root mycorrhizae phenology -
Soil respiration (CO2 emission) - Soil NO3-
concentration - Soil O2 concentration - Soil pH -
Soil water potential - Soil water volume - Soil
moisture (at -2, -30, -100 cm) - Soil
temperature (at -5, -15, -30cm) - Biological
temperature (i.e. soil/leaf/canopy surface
temperature)
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Aquatic Sensor Measurements
Groundwater Platform - Groundwater level - Soil
moisture

• Small Stream, Wetland, Limnological Platforms
• Automated water sample collection for chemical,
  biological,
• isotopic measurements (Stream Wetland
  only)
• - Dissolved organic carbon concentration (Stream
  Wetland only)
• - Dissolved gas concentrations CO2, N2, N2O,
  CH4, O2
• - Nutrient concentrations NO3-, NH4, PO43-, Si
• - pH
• - Oxidation/reduction potential
• - Conductivity
• - Water temperature (temperature profiles for
  Limnological)
• - Turbidity
• Chlorophyll
• Water Depth
• - Air temperature (at 10m, 1.5 m, 10 cm, 0 cm,
  Limnological only)
• - Wind speed (Limnological only)
• - Latent sensible heat fluxes (Limnological
  only)
• - Barometric pressure (Limnological only)
• - Relative humidity (Limnological only)
• - Precipitation (rain snow liquid equivalent,
  Limnological only)

18
Fundamental Sentinel Unit Measurements

• Remote Sensing
• MODIS Satellite
• Land Use, Land Cover
• Primary Production
• S-Band Polarimetric Radar (N-POL)
• Bird Migration
• High Resolution Precipitation

• Airborne Instrument Pods
• Hyperspectral
• LiDAR
• Side Aperture Radar (SAR)
• Interferometry
• Thermal Imaging

• Field Observation Programs
• Biodiversity
• - Soil Microbes
• Nematodes- Ants
• - Ground Beetles
• - Plants
• Algae
• - Aquatic Invertebrates
• Fish
• Breeding Bird Survey
• Aquatic Biogeochemistry
• - Ground Water Flow
• - Aquatic Sediments

• Vectors Pathogens - Mosquito West Nile,
  Encephalitis, etc.
• Deer Mice Hanta Virus, Plague, etc.
• Phenology
• Standardized Lilacs
• Dominant Plant Species- First Robin Nesting

• Organism Tracking System
• House Finch
• Deer Mice

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Citizen Science Gateway
Cyberinfrastructure
Signage

• Training Scientists and Students

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2. Store data
Internet2, National Lambda Rail, Lambda Grids

• Provide supporting IT for
• 3. Curation
• 4. Data analysis, integration, modeling
• and visualization
• 5. Access via customized user
• Interfaces
• 6. Collaboration
• via standards, software engineering facilities

Model-based integration
Web services interfaces
1. Collect data
Online data
NEON
Non-NEON
Non-NEON (LTER, Museum, DIGIR Etc.))
NEON
Paper documents
Sensornet data
21
Agenda

• The Design Directive
• Science, Education, and NEON Infrastructure
• Partnership Opportunities
• Progress and Next Steps

22
Examples of Partnership Opportunities
Research and development of wireless,
self-organizing environmental sensor networks.
Experimental forests as potential sites for NEON
infrastructure.
EROS Data Center--Data gateway and visualization
technologies for remotely sensed imagery.
Integration with NBII data sources.
Development of data analysis and synthesis
approaches for knowledge creation.
Reporting on the condition of the Nations
ecological systems.
Integration of atmospheric flux data resources
development of common measurement protocols.
Potential sites for NEON infrastructure source
of long-term ecological data development of
experimental approaches that complement NEON
observations.
Developing capacity to make ecological forecasts,
one of nine societal benefit areas.
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Partnership Opportunities

• Infrastructure siting
• USDA Forest Service
• USGS EROS Data Center
• Private, State, Federal lands
• Facility/instrument sharing
• NCAR/UCAR
• TIGR
• Biocollections facilities (e.g. Smithsonian
  Inst.)
• Scientific and education programs
• State
• Federal
• International
• Cyberinfrastructure

24
Agenda

• The Design Directive
• Science, Education, and NEON Infrastructure
• Partnership Opportunities
• Progress and Next Steps

25
The Design Directive

1. Objectively design a continental-scale
   observatory.
2. Prioritize the science and the infrastructure.
3. Accurately determine the costs.

26
The Design Directive

1. Objectively design a continental-scale
   observatory.
2. Prioritize the science and the infrastructure.
3. Accurately determine the costs.

27
The Design Directive

1. Objectively design a continental-scale
   observatory.
2. Prioritize the science and the infrastructure.
3. Accurately determine the costs.

A work in progress.
28
The Formal Design Process
Science Requirements/Goals
Financial Goals/Limitations
Current Focus
Engineering/Design Specifications
Traceability Matrices The Proven Approach
Design
Preliminary Project Execution Plan
Prospectuses
Project Execution Plan
Construction
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Site and Facility Prospectuses
Biocollections
Stable Isotope
Cyberinfrastructure
Genomics
30
NEON Instrument RFI Sessions

• Series of one / two day workshops at CENS and
  SDSC
• Sensor and Instrumentation (CENS, January)
• BioPDA (CENS, January)
• Wireless Sensor Platforms (CENS, February)
• Cyberinfrastructure (SDSC, February)

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RFI Sensor and Instrumentation (CENS, January)

• Academic
• Martin Wikelski (Princeton, Org. tracking)
• John Melack (UCSB, Aquatic)
• Tim Kratz (U Wisconsin, Aquatic)
• Ken Bible (U Washington, Biomesonet)
• Michael Goulden (UC Irvine, Biomesonet)
• Bill Munger (Harvard, Biomesonet)
• Michael Hamilton (Micromet)
• Philip Rundel (UCLA, Micromet)
• John Heidemann (ISI)
• Tom Harmon (UC Merced, soil)
• Joshua Schimel (UCSB, soil)

• Industry
• Campbell Scientific (System Integ.)
• Davis Instrument (Meteorology)
• Dynamax (Aquatic)
• LI-COR (Biomesonet)
• Sparrow System (Org. Tracking)
• Vaisala (Meteorology)
• YSI Environmental (Aquatic)

32
RFI BioPDA (CENS, January)

• Industry
• Hewlett Packard
• Flickr Software
• Trimble Navigation
• D.R. Systems
• Garyhill Embedded Systems
• Handheld Systems
• Fusion Solutions
• Good Technology
• Bear River Associates

• Academic
• Robert Morris (U Mass Boston)
• Matt Jones (NCEAS)
• Stinger Guala (USDA)
• Greq Quinn (SDSC)
• Eric Seabloom (NCEAS)
• Chris Jones (UCSB)

33
RFI Wireless Sensor Platforms (CENS, February)

• Industry
• Sensoria
• Crossbow
• Dust Inc
• Arched Rock
• Campbell Scientific

• Academic
• Tony Fountain (SDSC)
• John Heidemann (ISI)
• Bill Kaiser (UCLA)

34
RFI Hardware and Large Systems SDSC Victor
Hazelwood (security), Jay Dombrowski
(networking), Bryan Banister (storage systems),
Tony Fountain, Neil Cotofana, LJ Ding. And, SDSC
Viz and Synthesis Center folks NCSA Randy
Butler (LTER grid experience), Michael
Welge TeraGrid someone from distributed systems
and networking side, and perhaps project
management side (e.g. ANL folks) BIRN Mark
James (project manager), Jeff Grethe (project
architect) NEES Lelli van Dam (might be on
maternity leaveor designee) and another
technical person ISI/NMI Ann Chervanak? (data
grid, Globus, and experience with Earth System
Grid), or designee
35

NEON A continental research platform designed to
provide the capacity to forecast future states of
ecological systems for the advancement of
science and the benefit of society

• Novel infrastructure that
• allows scientists to observe the previously
  unobservable
• enables a new forecasting and predictive
  capacity for ecology
• takes advantage of new and evolving in situ
  sensing technologies
• couples human and natural systems

36
Why multi-scale distributed sensor-networking
will transform ecology
Radioastronomy
Computing
Field ecology
Supercomputers
Single Telescopes
Individual observations
because it has done so over and over again
Very Large Array
Internet
NEON
37
NIBD Contributors

• Chaitan Baru
• Deborah Estrin
• William Michener
• William Kaiser
• Tony Fountain
• Rick Munro
• Rand Knight
• Meeko Oishi
• Brian Wee

• NEON National Network Design Committee
• UCLA, Center for Embedded Network Sensing
• San Diego Supercomputing Ctr
• Triad Project Mgmt. Services
• NEON Postdoctoral Associates

38
Sensor Sensor Networking

• Debra Estrin
• Michael Hamilton
• Jose Fuentes
• Michael Goulden
• Steven Hansen
• Miki Hondzo
• J. William Munger
• Josh Schimel
• David Stahl
• Carol Wessman
• Martin Wikelski

• Cntr. Embedded Network Sensing
• James San Jacinto Mtns Reserve
• University of Virginia
• University of California Irvine
• Utah State University
• University of Minnesota
• Harvard University
• University of California Santa Barbara
• University of Washington
• University of Colorado
• Princeton University

39
IT Communications

• Chaitan Baru
• Michael Welge
• James Beach
• Tony Fountain
• Matthew Jones
• Rebecca Koskela
• Bonnie Nardi
• Laura Pearlman
• Sylvia Spengler
• Ann Zimmerman

• San Diego Supercomputing Center
• University of Illinois
• University of Kansas
• San Diego Supercomputing Center
• University of California Santa Barbara
• Arctic Region Supercomputing Cntr.
• University of California Irvine
• USC Information Sciences Institute
• Lawrence Berkeley Natl Laboratory
• University of Michigan

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41
Presentations

• NEON Overview Bill Michener
• CI Overview Chaitan Baru
• Sensor Networking Overview Deborah Estrin
• Governance and Budget Issues Bill Michener

42
NEON CI High Level Goals

• Provide secure, reliable transfer of data from
  NEON Nodes and NEON Facilities, to NEON Data
  Archives
• Provide immediate access to NEON data for NEON
  science community
• Simple search, simple browsing, advanced search
• Data access via publish/subscribe interfaces, via
  portals
• Establish standards for data, metadata, derived
  products, forecast models, etc.
• Provide efficient access to important 3rd-party
  data sources
• E.g. USGS, EPA, USFS, NASA, etc. data

43
NEON CI Approach

• NEON is a systems integration effort
• Versus new technology development
• Focus on running a production system
• Develop end-to-end functioning infrastructure
• 24x7 operation
• A Help Desk to help Node operations as well as to
  support user community
• Test and QA prior to production deployment
• KISS Keep It Simple
• Begin with what already works today (whether
  free, GOTS, or COTS), and ramp up from there
• Plan for technology refresh over the years

44
NEON Visualization and Forecasting Facility (NEON
front office)
NEON Observatory Facilities
NEON Node 1
Visualization displays
Visualization displays
NEON PoP
NEON PoP
NEON PoP
. . .
NEON PoP
Internet aggregate data rate 10GB-1TB/day,
depending on which sensors are active And data
rates from facilities (e.g. remote sensing)
NEON Data Processing Archive Facility (back
office)
Compute Cluster w/ disk
Observatory N
Observatory 1
Observatory 2
Observatory 3
. . .
Processing cluster
. . .
. . .
Geographically Remote archival copy
Storage Area Network
Raw data, Derived products
Archival Storage
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Subscribe
Access control-based Publish/Subscribe for data
and events
Publish
Integrated instrument and/or sensor array data
(Level 2)
Derived analysis and forecasting products
(Level 3)
Filtered data (Level 1)
Event detection
Event detection
Raw data (Level 0)
Event detection
Analysis, Forecasting
Data Integration
QA/QC Filtering
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47
Presentations

• NEON Overview Bill Michener
• CI Overview Chaitan Baru
• Sensor Networking Overview Deborah Estrin
• Governance and Budget Issues Bill Michener

48
NEON Fielded Instruments and Embedded CI
49
NEON Fielded Instruments Structure

• Fundamental Instrumented Unit (FIU)
• automatically gather relevant biotic and abiotic
  data
• fixed FIU will include two components BioMesoNet
  Tower, SensorNetworks
• three fixed and one roving FIU deployed per NEON
  Domain
• user community will augment with targeted higher
  spatial resolution deployments

50
Fielded Instrument Measurements