nowcasting : umass/casa weather radar demonstration michael zink
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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 - PowerPoint PPT PresentationTRANSCRIPT
Sponsored by the National Science Foundation
Nowcasting: UMass/CASA Weather Radar Demonstration
Michael ZinkCC-NIE Workshop
January 7, 2013
Sponsored by the National Science Foundation 2January 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– 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?
Sponsored by the National Science Foundation 3January 7, 2013
Why more and smaller radars?
10,000 ft
tornado
wind
earth surface
snow
3.05
km
0 40 80 120 160 200 240RANGE (km)
Horz. Scale: 1” = 50 kmVert. Scale: 1” -=- 2 km
5.4
km
1 km
2 km
4 km
gap
gap - earth curvature prevents 72% of the troposphere below 1 km from being observed.
10,000 ft
tornado
wind
earth surface
snow
3.05
km
3.05
km
0 40 80 120 160 200 240RANGE (km)
Sponsored by the National Science Foundation 4January 7, 2013
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
Sponsored by the National Science Foundation 5January 7, 2013
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
Sponsored by the National Science Foundation 6January 7, 2013
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
Sponsored by the National Science Foundation 7January 7, 2013
What is a Nowcast?
• Up to 15 minute weather forecast• Works only in the case of precipitation
Sponsored by the National Science Foundation 8January 7, 2013
Demo Data Flow
• Dynamic end-to-end Nowcasting – Mapping Nowcast Workflows onto GENI
Archival Storage
Radar Nodes
“raw” live data
Upstream LDM feed
archived netcdf data
Nowcast Processing
aggregatedmulti-radar data
Post to Web
Nowcast images for display
Sponsored by the National Science Foundation 9January 7, 2013
Multi-radar NetCDF Data
Nowcast Processing
1. DiCloud Archival Service (S3)2. LDM Data Feed (EC2)
“raw” live data
Generate “raw” live dataViSE/CASA radar nodes
http://stb.ece.uprm.edu/current.jsp
Use proxy to track usage-based spending on Amazon and enforce quotas and limits
http://geni.cs.umass.edu/vise/dicloud.php
1. Ingest mulit-radar data feeds2. Merge and grid multi-radar data2. Generate 1min, 5min, and 10min Nowcasts3. Send results over NLR to Umass4. Repeat
ViSE views steerable radars as shared, virtualized resources
http://geni.cs.umass.edu/vise
Nowcast images for display
Sponsored by the National Science Foundation 10January 7, 2013
Bigger Picture
• Analysis of Nowcast in the cloud– Compare networking and compute capabilities of
different clouds
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Instance Type
Memory (GB)
Disk (GB)
CPU Cost/hr ($)
Total Cost ($)
Exec. Time (sec)
Total Time (sec)
Amzon EC2 7.5 850 4 0.34 1.13 74.34 95.08
Rackspace 8.0 320 4 0.48 1.63 96.53 120.33
GENICloud 8.0 20 4 - - 67.45 78.60
ExoGENI 8.0 20 4 - - 56.83 72.07
Computation Time Analysis
Sponsored by the National Science Foundation 12January 7, 2013
US Ignite – Ultra-high Bandwidth
Sponsored by the National Science Foundation 13January 7, 2013
Future Experiments
DFW
UMass
RENCI
I2/NLR
BBN
LEARN
UoH
Sponsored by the National Science Foundation 14January 7, 2013
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
Sponsored by the National Science Foundation 15January 7, 2013
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