1 / 15

Nowcasting : UMass/CASA Weather Radar Demonstration Michael Zink

Nowcasting : UMass/CASA Weather Radar Demonstration Michael Zink. CC-NIE Workshop January 7 , 2013. 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

kyrie
Télécharger la présentation

Nowcasting : UMass/CASA Weather Radar Demonstration Michael Zink

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Nowcasting: UMass/CASA Weather Radar DemonstrationMichael Zink CC-NIE Workshop January 7, 2013

  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 • Cost of operation for weather for 75 days shows $50 of cloud usage vs. $4000 of dedicated hardware • How do we generate accurate, short-term “nowcasts” using these new distributed radar systems?

  3. Why more and smaller radars? 10,000 ft 10,000 ft 3.05 km 3.05 km snow snow wind wind 3.05 km tornado tornado earth surface earth surface Horz. Scale: 1” = 50 km Vert. Scale: 1” -=- 2 km 0 0 40 120 160 200 40 120 160 200 80 80 240 240 RANGE (km) RANGE (km) gap 4 km 1 km 2 km 5.4 km gap - earth curvature prevents 72% of the troposphere below 1 km from being observed.

  4. 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

  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 ExoGENI VMs • Storage Slice: Amazon S3

  7. What is a Nowcast? • Up to 15 minute weather forecast • Works only in the case of precipitation

  8. 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

  9. 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

  10. Bigger Picture • Analysis of Nowcast in the cloud • Compare networking and compute capabilities of different clouds

  11. Computation Time Analysis

  12. US Ignite – Ultra-high Bandwidth

  13. Future Experiments UMass I2/NLR LEARN DFW BBN RENCI UoH

  14. GENI/CASA 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

  15. 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

More Related