1. Nowcasting: UMass/CASA Weather Radar Demonstration David Irwin
November 3, 2010

2. Costly to dedicate resources for rare events
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. Example: Puerto Rico Testbed
UPRM Student Testbed
Led by Jorge Trabal, Prof. Sandra Cruz-Pol, and Prof. Jose Colom

5. Dynamic end-to-end Nowcasting on GENI
Demo Background
Dynamic end-to-end Nowcasting on GENI
Use GENI/Orca Control Framework (RENCI/Duke)
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

6. Nowcast images for display
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

7. ViSE views steerable radars as shared, virtualized resources
Generate “raw” live data
ViSE/CASA radar nodes
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
ViSE views steerable radars as shared, virtualized resources
“raw” live data
Nowcast images
for display
DiCloud Archival Service (S3)
LDM Data Feed (EC2)
Multi-radar NetCDF Data
Nowcast Processing

8. 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

9. GENI is critical for next-generation applications
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

10. Wrap-up