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School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

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Page 1: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

School of Ocean and Earth Science and TechnologyUniversity of Hawai’i at Manoa

11 February 2013

ALOHA Cabled Observatory:Data Considerations

Page 2: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

R. Lukas, D. Karl and many others

ALOHA Cabled Observatory (ACO)Node installed June 2011 – deepest at 4728 m

Station ALOHA

ACO

F. Duennebier et al. started 2002

Page 3: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

ACO bottom configuration

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Page 4: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

Data and results

Aloha.manoa.hawaii.edu

Page 5: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

CameraOBS light failed 3 JulyCAM lights failed 18 July

Jason on left – last moments on bottom “shrimp” swimming by

Page 6: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

Deep-sea lizard fish attempting to eat an aristeid shrimp

Jeff Drazen and Aharon Fleury, UHawaii

Page 7: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

One day, one minute of sound

Page 8: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

First blues 31 October 2012

x10

Page 9: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

Humpbacks returning 25 Nov 2012

Page 10: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

ACO Network

Seafloor 100 Mb/s

Makaha 1 Gb/s

Makaha – Manoa HT 3 Mb/s MPLS

SOEST network 1 Gb/s

Internet 2 10 Gb/sReal-time data

display and access50 in-water IPs55 dry IPs

http://aloha.manoa.hawaii.edu

Future 1 Gb/s

Page 11: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

ACO Data Management

NB ! MBARI SSDS and SIAM

Page 12: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

Data numbers• Two hydrophones, each sampled (16 bit) 96,000 Hz, total

3 Mb/s, 12 TB/y• Other – temperature, velocity, etc – small• When video starts working again this September, H.264 <

1 Mb/s• Using NAS drives now• Starting to use Blu-ray• Buffering needed at shore station• 3 Mb/s link to UH Manoa now ($)• In process: 1 Gb/s link over state/UH network (< $)

• Network complexity – IP addresses– Subsea 55– Shore 50

Page 13: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

Data Management

• SIAM (Software infrastructure and application for MOOS) – instrument servers, from MBARI (Monterey Bay Aquarium Research Institute)

• SSDS – Shore-side Data System – MBARI• Custom – hydrophone, pressure, …

• Practical problems – what are best practices for acoustic and video data?

Page 14: School of Ocean and Earth Science and Technology University of Hawai’i at Manoa 11 February 2013 ALOHA Cabled Observatory: Data Considerations

Lessons• Don’t underestimate shore-side needs

– Network management – intensive!– Data management – intensive!– Documentation – intensive!– Real-time – intensive!

• Include good time stamps – cross-correlating data (and problems)

• Understanding problems with data – intensive!• Observatories – sustained long-term

observations – prove value with un-aliased data