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 second "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 products. Fondly referring to these processing campaigns as "rodeos" the team rapidly constructed a Big Data 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.
Chris Mattmann, Paul Ramirez, and Cameron Goodale for the ASO project will present this talk and will detail the story of the Compute processing capability on behalf of the larger team, highlighting contributions of its key members along the way. We will cover the blending of open source technologies and proprietary software packages that have helped us attain our goals and discuss areas that we are actively investigating to expand our use of open source.