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LEARN Workshop, February 18, 2011 http://geni.cs.umass.edu/vise http://geni.cs.umass.edu/dicloud

GENI Alpha Demonstration Nowcasting : UMass/CASA Weather Radar Demonstration Mike Zink, David Irwin. LEARN Workshop, February 18, 2011 http://geni.cs.umass.edu/vise http://geni.cs.umass.edu/dicloud http://www.geni.net. Problem.

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LEARN Workshop, February 18, 2011 http://geni.cs.umass.edu/vise http://geni.cs.umass.edu/dicloud

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  1. GENI Alpha DemonstrationNowcasting: UMass/CASA Weather Radar DemonstrationMike Zink, David Irwin LEARN Workshop, February 18, 2011 http://geni.cs.umass.edu/vise http://geni.cs.umass.edu/dicloud http://www.geni.net

  2. Problem • CASA (an NSF ERC) is studying experimental networks of small controllable weather radars • Better data is the foundation of better hazardous weather detection and earlier warnings • Complex modeling to detect inclement weather requires many resources: sensors, bandwidth, storage, and computation • Costly to dedicate resources for rare events • How do we generate accurate, short-term “nowcasts” using these new distributed radar systems?

  3. Solution • Today: only a few large NEXRAD radars (100s) • Tomorrow: many (1000s) smaller, less expensive radars produce data close to the ground where weather happens • Requires a flexible infrastructure for coordinated provisioning of shared sensing, networking, storage, and computing resources on-demand

  4. Today’s Aircraft and Weather Surveillance Weather at 3 km (~10k ft) Aircraft at 5k ft Weather at 1 km (~3200 ft) Aircraft at 1k ft

  5. Example: Puerto Rico Testbed • UPRM Student Testbed • Led by Jorge Trabal, Prof. Sandra Cruz-Pol, and Prof. Jose Colom • http://www.youtube.com/watch?v=7TR64BhwMlI

  6. Demo Background • Dynamic end-to-end Nowcasting on GENI • Use GENI/Orca Control Framework (RENCI/Duke) • https://geni-orca.renci.org/trac/ • http://geni-ben.renci.org:11080/orca/ • Reserve heterogeneous slice of resources • Sensing Slice: UMass ViSE radars • Networking Slice: NLR, BEN-RENCI • Computation Slice: Amazon EC2 + UMass and RENCI VMs • Storage Slice: Amazon S3

  7. Demo Data Flow • Dynamic end-to-end Nowcasting • Mapping Nowcast Workflows onto GENI archived netcdf data aggregated multi-radar data Nowcast images for display “raw” live data Radar Nodes Archival Storage Upstream LDM feed Nowcast Processing Post to Web

  8. Generate “raw” live data ViSE/CASA radar nodes http://stb.ece.uprm.edu/current.jsp Ingest mulit-radar data feeds Merge and grid multi-radar data Generate 1min, 5min, and 10min Nowcasts Send results over NLR to Umass Repeat Use proxy to track usage-based spending on Amazon and enforce quotas and limits http://geni.cs.umass.edu/vise/dicloud.php ViSE views steerable radars as shared, virtualized resources http://geni.cs.umass.edu/vise “raw” live data Nowcast images for display DiCloud Archival Service (S3) LDM Data Feed (EC2) Multi-radar NetCDF Data Nowcast Processing

  9. GENI Technologies and Credits • UMass-Amherst • ViSE and DiCloud projects • University of Puerto Rico, Mayaguez • Jorge Trabal, Prof. Cruz-Pol, and Prof. Colom • OTG Radars • Colorado State University • Prof. V. Chandrasekar • Nowcasting Software • RENCI/Duke • Orca Control Framework • BEN network • Starlight

  10. Conclusion • GENI is critical for next-generation applications • Enable nowcasting in experimental radar systems • GENI capabilities: “sliceability”/virtualization, federation, network programmability • Provide domain scientists a new platform • Experiment with tightly integrated systems combining sensing, storage, networking, computing • Engage domain scientists in CASA and elsewhere • Extend GENI network to Puerto Rico

  11. Wrap-up

  12. Demo Data Flow • Dynamic end-to-end Nowcasting on GENI archived netcdf data aggregated multi-radar data Nowcast images for display “raw” live data Radar Nodes Archival Storage Upstream LDM feed Nowcast Processing Post to Web ViSE views steerable radars as shared, virtualized resources http://geni.cs.umass.edu/vise Use proxy to track usage-based spending on Amazon and enforce quotas and limits http://geni.cs.umass.edu/vise/dicloud.php http://vise-testbed.cs.umass.edu/nowcast/nowcast.html DiCloud Archival Service on Amazon S3 1. Ingest data feeds from multiple radars 2. Merge multi-radar data Generate 1min, 5min, and 10min Nowcasts Repeat Generate Nowcasts Data publicly available to downstream nodes Generate “raw” data ViSE/CASA radar nodes http://stb.ece.uprm.edu/current.jsp

  13. Thank you UPRM: • Gianni Pablos • José Ortiz • WilsonCastellanos • MelissaAcosta • José Cordero • BenjamínDe Jesús • Sandra Cruz-Pol • José Colom UMass: Emmanuel Cecchet PrashantShenoy Jim Kurose Eric Lyons CSU: V. Chandrasekar Evan Ruzanski Yanting Wang RENCI: IliaBaldine Jeff Chase Anirban Mandel BBN: Mark Berman Niky Riga Harry Mussman

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