Marine & Aquatic Sensor Arrays
Ken Johnson
Monterey Bay Aquarium
Research Institute
• Sustained, Integrated Ocean
Observing Networks
• Observatories Network Issues
• Biogeochemical
Sensor Systems
General Outline:
Ocean Observatories InitiativeOcean Observatories Initiative Approved by the National Science Board for a Future NSF Budget
Includes:
Coastal Observatories
Plate Scale Observatory
Global Mooring Observatory
Partners
University of Washington
Woods Hole Oceanographic Institution
National Aeronautics & Space Administration, JPL
Monterey Bay Aquarium Research Institute
NEPTUNE Canada (IPOST)
MARS (the Monterey Accelerated Research System). A cabled observatory in Monterey Bay built with NEPTUNE infrastructure as a proof of concept.
Tropical Atmosphere Ocean project
Real-time data from moored ocean buoys for improved
detection, understanding and prediction of El Niño and La Niña.
Issues - • Existing ocean observing systems such as TAO & ARGO deal with relatively simple data sets of well known format - e.g. vertical T & Salinity profiles.
• New sensors cannot be easily fit into the platform or data system.
• Data transmission from unattended platforms in the middle of the ocean is a problem.
ARGO Global Profiling
Drifter Array
Temp. & Salinity of Ocean Interior
E.g., the MBARI OASIS System - Now two moorings in Monterey Bay & two in the equatorial Pacific.
M2M1
MBARI OASIS
Moorings
The OASIS moorings communicate by ARGOS, microwave or packet radio.
Adding new sensors becomes a large software configuration issue, particularly for complicated sensors.
M1 Mooring Configuration Schedule
• 07/03 Initial configuration meeting
• 08/02 Final science list
• 08/03 Evaluate configuration
• 08/04 Instruments on hand for testing
• 08/07 Assemble instruments & configure - 5 days
• 08/14 Configure software - 15 days
• 09/04 Software test - 5 days
• 09/11 Close controller and install - 5 days
• 09/19 Complete system test - 5 days
• 09/28 Data download test - 1 day
• 10/04 Deploy mooring & cross your fingers
Software Infrastructure for the MBARI Ocean Observing
System
Tom O’Reilly
Duane Edgington
MOOS software
• Application software for sensors, user interfaces, databases, etc.
• Distributed applications are “glued” together by smart network infrastructure
Smart network
• Self-organizing; applications are notified by infrastructure when network configuration changes– E.g., database ingest engine always “aware” of
available sensors
• Enables automatic configuration, remote control, and autonomous coordination of sensors
Network configuration
• “Just plug the sensor in, and it works!”
• Easily replace, remove, or add a new kind of sensor
• Remote update of sensor’s driver software
• Minimal configuration effort by human operators lower maintenance cost
Leading technology candidates
• CORBA
• Java - JINI
• LonWorks
• Universal Plug-n-Play
What kind of data will we assimilate in global observing systems?
• Physical, geophysical observations (T, S, seismicity) - generally sensors are mature.
• Chemical - nutrients (nitrate, phosphate, iron), gases (carbon dioxide, oxygen) - sensors exist but have not gone commercial.
• Biological - bio-optical systems are mature, but not very specific. DNA based systems are beginning to appear.
In Situ Ultraviolet Spectrophotometry
Measurement of pCO2 from Moorings:
Gernot FriederichPeter WalzMike BurzcynskiFrancisco Chavezall at MBARI
El Niño / La Niña Cycle in Monterey BayP
CO
2 (s
ea -
air
) a
tm
Tem
pera
ture
DNA Based Detection of Phytoplankton Species Using the Environmental Sample
Processor (ESP)
• Chris Scholin
• Roman Marin
• Gene Massion
• all at MBARI
NOTE: The entire phytoplankton standing stock of the ocean turns over each 4 days on average!
+
Solid Supportwith
Capture Probe
Colored Productor
Chemiluminescence
ASignal enzyme (E)
conjugated to avidin (A)
Sandwich of
Capture Probe : Analyte : Signal Probe
Captured Target Analyte Biotinytated Signal Probe
Probe Hybridization
ColorlessSubstrate
Target
E
reporter haptenvisualization
< 1 hr.
Non-Target
CompleteAssay
wash
hybridize
wash
hybridize
Detection of Target Species in Sample Homogenates
Sandwich hybridization bioassay of rRNA in phytoplankton
Iron (nM) Pseudo-nitzschia australis (cells/L)
Does iron regulate toxic plankton blooms (e.g., Wells et al., Evaluation of iron as a triggering factor for red
tide blooms. MEPS, 69, 93, 1991)?
MOOS will be operational in 2003 for biogeochemical studies.