Additional Areas Mike Zink CASA Deputy Director University of Massachusetts NSF Year 9 Visit, July 2 nd, 2012.

Slides:



Advertisements
Similar presentations
Data-Intensive Cloud Control for GENI GEC 8 demo Orca control framework July 20, 2010 Michael Zink, Prashant Shenoy, Jim Kurose, David Irwin and Emmanuel.
Advertisements

Sponsored by the National Science Foundation GENI Alpha Demonstration Nowcasting: UMass/CASA Weather Radar Demonstration David Irwin November 3, 2010
U.S. Department of Energy’s Office of Science Basic Energy Sciences Advisory Committee Dr. Daniel A. Hitchcock October 21, 2003
Design and Operation of Infrasound Stations for Hazardous Weather Detection David Pepyne, Sean Klaiber, Jerry Brotzge, and Michael Zink Presented at the.
Glenn Ricart | Chief Technology Officer New Technologies New Applications.
Unisys Weather Information Services Presentation for NWS Partners Meeting Partner Perspective June 2010 Ron Guy, Director Unisys Weather
GENI: Global Environment for Networking Innovations Larry Landweber Senior Advisor NSF:CISE Joint Techs Madison, WI July 17, 2006.
MOTOROLA and the Stylized M Logo are registered in the US Patent and Trademark Office. All other product or service names are the property of their respective.
Sensors in Sustainability Jim Kurose Department of Computer Science University of Massachusetts Amherst MA USA NSF WICS Workshop Salt Lake City.
Collaborative Adaptive Sensing of the Atmosphere: End User and Social Integration 2009 American Meteorological Association Summer Community Meeting Walter.
0 Future NWS Activities in Support of Renewable Energy* Dr. David Green NOAA, NWS Office of Climate, Water & Weather Services AMS Summer Community Meeting.
Colorado State University
Oklahoma Supercomputing Symposium 2008 Oct 7 th 2008 Mining for Science and Engineering Presented by: Kenji Yoshigoe.
U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2008 ViSE: Virtualized Sensing Environment David Irwin, Mike Zink, Prashant Shenoy.
University of Kansas A KTEC Center of Excellence 1 Victor S. Frost Director, Information & Telecommunication Technology Center Dan F. Servey Distinguished.
U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science From Cloud Computing to Sensor Networks: Distributed Computing Research at LASS.
Global City Teams Challenge Funding Opportunities.
Development of NWS Satellite User Readiness Mike Johnson NWS/OST November 4, 2009.
Chapter 13 – Weather Analysis and Forecasting. The National Weather Service The National Weather Service (NWS) is responsible for forecasts several times.
Sponsored by the National Science Foundation GENI Alpha Demonstration Nowcasting: UMass/CASA Weather Radar Demonstration Mike Zink, David Irwin LEARN Workshop,
CASA – Collaborative Adaptive Sensing of the Atmosphere NWRT/PAR – National Weather Radar Testbed / Phased Array Radar Kurt D. Hondl DOC/NOAA/OAR National.
Linked Environments for Atmospheric Discovery (LEAD): Web Services for Meteorological Research and Education.
© 2012 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 1 Cisco CloudVerse for Government: Helping Agencies Reduce Costs and Respond.
V. Chandrasekar (CSU), Mike Daniels (NCAR), Sara Graves (UAH), Branko Kerkez (Michigan), Frank Vernon (USCD) Integrating Real-time Data into the EarthCube.
NOAA’s National Weather Service In Green Bay. The National Weather Service is responsible for issuing forecasts and warnings for the protection of life.
Introduction to Cloud Computing
Computing in Atmospheric Sciences Workshop: 2003 Challenges of Cyberinfrastructure Alan Blatecky Executive Director San Diego Supercomputer Center.
© 2012 IBM Corporation IBM Israel Software Lab (ILSL( Daniel Yellin, Director March 2013.
NYMTC Strategic Data Management Kuo-Ann Chiao Technical Group Director.
Sponsored by the National Science Foundation Nowcasting: UMass/CASA Weather Radar Demonstration Michael Zink CC-NIE Workshop January 7, 2013.
The Climate Prediction Project Global Climate Information for Regional Adaptation and Decision-Making in the 21 st Century.
Sponsored by the National Science Foundation GENI and Cloud Computing Niky RIga GENI Project Office
Michael Murphy, Huthasana Kalyanam, John Hess, Vance Faber, Boris Khattatov Fusion Numerics Inc. Overview of Current Research in Sensor Networks and Weather.
Sponsored by the National Science Foundation GENI I&M Workshop GIMI: Large-scale GENI Instrumentation and Measurement Infrastructure Mike Zink November.
CloudCast: Cloud Computing for Short-term Mobile Weather Forecasts Dilip Kumar Krishnappa, David Irwin, Eric Lyons and Michael Zink IPCCC 2012.
Wright Brothers Institute Innovation Overview Lester McFawn Director 3 rd Annual OAI Industry Member’s Forum Innovation & Product Development November.
OnTimeMeasure-GENI: Centralized and Distributed Measurement Orchestration Software Prasad Calyam, Ph.D. (PI) Paul Schopis, (Co-PI) Weiping Mandrawa (Network.
Dr. Mark Askelson | 4149 University Avenue Stop 9006, Grand Forks, ND phone | fax Ganged Phased Array Radar – Risk Mitigation.
Dr. Sandra Cruz-Pol RF Systems and Remote Sensing.
Luis Russi¹, Carlos R. Senna¹, Edmundo R. M. Madeira¹, Xuan Liu², Shuai Zhao², and Deep Medhi² Hadoop-in-a-Hybrid-Cloud GEC21 The 21st GENI Engineering.
V I SE/D I C LOUD S TATUS J ULY 28 TH, 2011 Michael Zink ECE Department University of Massachusetts Amherst.
GIMI I&M and Monitoring Mike Zink University of Massachusetts Amherst GEC 15, Houston, October 23 rd 1.
GRID Overview Internet2 Member Meeting Spring 2003 Sandra Redman Information Technology and Systems Center and Information Technology Research Center National.
CASA Update for MPAR Group David McLaughlin University of Massachusetts – Amherst V. Chandrasakar Colorado State University March 20, 2007 – OFCM/Silver.
Yanlei Diao, University of Massachusetts Amherst Future Directions in Sensor Data Management Yanlei Diao University of Massachusetts, Amherst.
 2007, Verizon. All rights reserved. Advanced Emergency Network Capabilities & Communications Solutions Presentation to The Joint Advisory Committee on.
Data-Intensive Cloud Control for GENI GEC 10 Orca control framework March 15 th, 2011 Michael Zink, Prashant Shenoy, Jim Kurose, David Irwin and Emmanuel.
6/23/2005 R. GARDNER OSG Baseline Services 1 OSG Baseline Services In my talk I’d like to discuss two questions:  What capabilities are we aiming for.
PaaSport Introduction on Cloud Computing PaaSport training material.
1) The Oklahoma City Micronet Project 2) Network Design and Implementation Dr. Jeffrey Basara Director of Research Oklahoma Climatological Survey University.
TWO-YEAR ASSESSMENT OF NOWCASTING PERFORMANCE IN THE CASA SYSTEM Evan Ruzanski 1, V. Chandrasekar 2, and Delbert Willie 2 1 Vaisala, Inc., Louisville,
Create & Innovate ICTs for Education? Technology without Context lacks Purpose.
1 Earth Science Technology Office The Earth Science (ES) Vision: An intelligent Web of Sensors IGARSS 2002 Paper 02_06_08:20 Eduardo Torres-Martinez –
Status Organization Overview of Program of Work Education, Training It’s the People who make it happen & make it Work.
| nectar.org.au NECTAR TRAINING Module 2 Virtual Laboratories and eResearch Tools.
U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Sensor Networks and Platforms for Advancing Water Research Prashant Shenoy University.
Advanced research and education networking in the United States: the Internet2 experience Heather Boyles Director, Member and Partner Relations Internet2.
Challenges in PBL and Innovative Sensing Techniques Walter Bach Army Research Office
NASA Earth Exchange (NEX) A collaborative supercomputing environment for global change science Earth Science Division/NASA Advanced Supercomputing (NAS)
Software as a Service (SaaS) Fredrick Dande, MBA, PMP.
© 2007 IBM Corporation IBM Software Strategy Group IBM Google Announcement on Internet-Scale Computing (“Cloud Computing Model”) Oct 8, 2007 IBM Confidential.
EUB Brazil: IoT Pilots HORIZON 2020 WP EUB Brazil: IoT Pilots DG CONNECT European Commission.
Prashant Shenoy Lab Description Seminar 2009
Latency and Communication Challenges in Automated Manufacturing
Nowcasting: UMass/CASA Weather Radar Demonstration David Irwin
Bringing Large Commercial Airport Capabilities to Your Local Community
Thales Alenia Space Competence Center Software Solutions
Road Infrastructure for Road Vehicles Automation
Digital Policy -Transformation Towards Society 5.0-
Cg-18 Special Sessions Topic 4; Infrastructure -
Presentation transcript:

Additional Areas Mike Zink CASA Deputy Director University of Massachusetts NSF Year 9 Visit, July 2 nd, 2012

Overview Multifunction MC&C Infrasound SBIR submissions US Ignite/GENI –Time series data streaming –MC&C in the cloud TropiNet Cameras

How can we optimize system for the best response?

Multifunction MC&C So far, MC&C only used for –Radars –Control based on detection of atmospheric phenomena Detection of low-flying aircraft Control of camera networks –E.g., use radar data to visually verify potential flooding

Low Flyers Integrated into MC&C Initial tests in testbed UAV detect and avoid

Infrasound Two infrasound monitoring stations were deployed in the spring of –One in Cyril, Oklahoma (designated KCYR) and one in Rush Springs, Oklahoma (designated KRSP). –The two stations were ~30km apart. –The infrasound monitoring stations were collocated with two of CASA’s mechanically scanned X-band weather radars. 71 days of pressure data was collected at both stations. 20 days of wind speed and direction data was collected at the Cyril station. Cyril Rush Springs

Infrasound Spectrogram –data was unremarkable Cohereogram –strong coherence in the frequency band < ~1.5Hz.

Infrasound Radar image from:

SBIR Submissions Phase-tilt radar –Work with FirstRF (in the process of becoming an associate member) to improve commercial phase-tilt antenna design Ridgeline –CASA spin-off, licensing CASA technology and implementing it in their commercial offering EWR –As CASA members already licensed algoritms. –Implementation of algorithms

MA1 Second year of operations to support EMs on UMass Amherst campus Joint efforts to secure funding for continued operation of MA1 Continued use for testing of new software

US Ignite/GENI US Ignite –applications and services for ultra-fast broadband and software-defined networks –Foster creation of novel applications that will transform healthcare, education and job skills training, public safety, energy, and advanced manufacturing. GENI –providing collaborative and exploratory environments for academia, industry and the public to catalyze groundbreaking discoveries and innovation in these emerging global networks

Time Series Operations Control Center Short-term Forecast in the Cloud Short-term Forecast in the Cloud Long-term, Large-scale Forecast Long-term, Large-scale Forecast End Users: Public, NWS EM,Media Industry End Users: Public, NWS EM,Media Industry Up to 150 Mbps Up to n * 150 Mbps

NowCast in the Cloud CASA involved in Alpha Demo at GENI Engineering Conference in D.C. (Nov. 2010) 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 Issues: System integration Cloud designed for persistent, web-based services Data staging Sufficient on-demand bandwidth into the cloud

NowCast in the Cloud Automated initialization of Nowcast in EC2 by MC&C Analyzed network capabilities Analyzed compute capabilities InstancesMemory (GB) Disk (GB)Exec. Time (seconds) Total time (seconds) EC Rackspace GeniCloud ExoGENI

Cloud Sensing / Sensor Cloud Provide sensing, networking, and compute resources on demand Rapid provision and release Opportunistic sensing: –Sensing information from cell phones –Camera data

TropiNet Drafting an MOU between UPRM, CSU, and UMass to control TropiNet radars with MC&C Terrain and climate will pose new challenges to MC&C Courtesy of the NSF TropiNet project

Cameras Discussion with ITT Exelis to use their DFW camera network Goal: Weather validation Monitor public response First steps to put feedback loop around public response What do you see in the morning news?