[SGVLUG] Reminder: Meeting on Thu 11/14: Big Data! 24 Hour Near Real Time Processing and Computation for the JPL Airborne Snow Observatory

[Lan Dang]

Hi all,

Reminder that there is a meeting this week.  Dr. Chris Mattmann from the Jet Propulsion Laboratory will talk about big data and the construction of an open source data processing system for the JPL Airborne Snow Observatory.  

Other items:
* RSVP for the SGVLUG/SGVHAK BBQ, which will be Saturday, Nov 16th: http://bit.ly/17oDrvK


* SGVLUG meeting room:  Last I heard, we are losing Downs 107, starting next calendar year.  Christefano is inquiring at PCC.  Junaid is going to see if he can get us a Caltech sponsor.  But basically, we're gonna be hurting for a meeting place come January.   Since it affects the club as a whole, I'd like to discuss it at the meeting. If you can help out, speak up, come to the meeting.  

* SCALE Call for Papers ends Dec 15th.  If you want to speak at SCALE, get your proposal in.  https://www.socallinuxexpo.org/cfp



SGVLUG meeting, Thu 11/14/2013 7-9pm 
WEBSITE: http://sgvlug.org 
TOPIC: Big Data! 24 Hour Near Real Time Processing and Computation for the JPL Airborne Snow Observato 
PRESENTERS: Chris Mattmann
LOCATION: Caltech - Downs 107. The Downs building is across from the tennis courts on California at Arden.
ABSTRACT:

JPL’s Airborne Snow Observatory is an integrated imaging spectrometer and scanning LIDAR for measuring mountain snow albedo, snow depth/snow 
water equivalent, and ice height (once exposed), led by PI Dr. Tom 
Painter.  The team recently wrapped our “Snow On” campaign where over a 
course of 3 months, we flew the Tuolumne River Basin, Sierra Nevada, 
California above the O’Shaughnessy Dam of the Hetch Hetchy reservoir; 
focusing initial on the Tuolumne, and then moving to weekly flights over the Uncompahgre Basin, Colorado.

To meet the needs of its customers including Water Resource managers 
who are keenly interested in Snow melt, the ASO team had to develop and 
end to end 24 hour latency capability for processing spectrometer and 
LIDAR data from Level 0 to Level 4 (“ish”) products. Fondly referring to these processing campaigns as “rodeos” the team rapidly constructed an 
open source data processing system at minimal cost and risk that not 
only met our processing demands, but taught the entire team many lessons about remote sensing of snow and dust properties, algorithm 
integration, the relationship between computer scientists, and snow 
hydrologist; flight and engineering teams, geographers, and most 
importantly lessons about camaraderie that will engender highly 
innovative and rapid data systems development, and quality science 
products for years to come.

BIO: Chris Mattmann is the Compute Lead for the ASO project and he will humbly tell the story of the Compute processing capability on behalf of the larger team, highlighting contributions of its key members along the way

Thanks to Junaid for reserving Downs 107.

Lan