learn workshop, february 18, 2011

Sponsored by the National Science Foundation

GENI Alpha Demonstration

Nowcasting: UMass/CASA Weather Radar Demonstration

Mike Zink, David IrwinLEARN Workshop, February 18, 2011

http://geni.cs.umass.edu/visehttp://geni.cs.umass.edu/dicloud

http://www.geni.net

http://geni.cs.umass.edu/vise

http://geni.cs.umass.edu/dicloud

http://www.geni.net/

Sponsored by the National Science Foundation 2November 3, 2010

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?


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


Today’s Aircraft and Weather Surveillance

Aircraft at 1k ft

Aircraft at 5k ft

Weather at 1 km (~3200 ft)

Weather at 3 km (~10k ft)


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

http://www.youtube.com/watch?v=7TR64BhwMlI


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

https://geni-orca.renci.org/trac/

http://geni-ben.renci.org:8080/orca/


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


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




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


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


Wrap-up


Demo Data Flow

• Dynamic end-to-end Nowcasting on GENI

Archival Storage

Radar Nodes

Generate “raw” dataViSE/CASA radar nodes

“raw” live data


DiCloud ArchivalService on Amazon S3

Upstream LDM feed

archived netcdf data

Data publicly availableto downstream nodes

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


Nowcast Processing

aggregatedmulti-radar data

GenerateNowcasts

1. Ingest data feeds from multiple radars2. Merge multi-radar data3. Generate 1min, 5min, and 10min

Nowcasts4. Repeat

http://vise-testbed.cs.umass.edu/nowcast/nowcast.html

Post to Web




Thank you

UMass:Emmanuel

CecchetPrashant ShenoyJim KuroseEric Lyons

UPRM:Gianni PablosJosé OrtizWilson CastellanosMelissa AcostaJosé CorderoBenjamín De JesúsSandra Cruz-PolJosé Colom

CSU:V. ChandrasekarEvan RuzanskiYanting Wang

RENCI:Ilia BaldineJeff ChaseAnirban Mandel

BBN:Mark BermanNiky RigaHarry Mussman

learn workshop, february 18, 2011

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