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Open Source DataTurbine for Tsunami Detection in Indian Ocean and other Environmental Observing Systems Sameer Tilak, Tony Fountain, Peter Shin, Brian.

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Presentation on theme: "Open Source DataTurbine for Tsunami Detection in Indian Ocean and other Environmental Observing Systems Sameer Tilak, Tony Fountain, Peter Shin, Brian."— Presentation transcript:

1 Open Source DataTurbine for Tsunami Detection in Indian Ocean and other Environmental Observing Systems Sameer Tilak, Tony Fountain, Peter Shin, Brian McMahon, ArunAgarwal, K. V. Subbarao, Peter Arzberger

2 Streaming Data Middleware
Common programming layer for real-time systems Enables integration of real-time components Provides abstractions over vendor-specific products Supports in-network processing (buffering, time synch …) Make data streams first class objects Addressable Efficient operations Monitoring, QA/QC Event detection Replication and subscription Reliable transport

3 Open Source DataTurbine Initiative
In-network buffered data management and archiving for streaming data Scalable support for in-network intelligent routing, data processing, filtering, and topology management Robust bridge environment between diverse data sources and distributed data destinations Optimized for high-speed streaming data All-software solution (Java) Used in NSF, NASA, NOAA, DOE projects Developed by Creare Inc., OPEN SOURCE SOFTWARE - Apache 2.0 License, Jan 07 NSF support from SDCI program (funding started on Sept 07)

4 DataTurbine: Generalized Architecture

5 DataTurbine GoogleEarth Plug-in
Credit Matt Miller, Creare Inc.

6 System Architecture Open Scalable, Modular architecture based on OGC-SWE standards

7 Real-World Deployments
GLEON CREON Animal Tracking Earthquake Engineering Smart Buildings NASA etc. etc.

8 Modeling and Prediction
Open Ocean Forecast Online Offline

9 Tsunami Sensors Incois uses data streams from tide gauges, bottom pressure readers (BPRs), and seismic stations to detect possible tsunami activity Potential events are checked against precalculated mathematical models to aid in decision making Integrating all of this data into a single DataTurbine server that can be mirrored and used for event detection

10 Observation Network in Indian Ocean (Earthquake & Sea Level)
Seismic Stations National International Tsunami Buoys National International Tide Gauges National International This is an example of a message of tsunami warning.

11 Infrastructure Details
Tsunami and Storm Surges Observational Network Infrastructure Details Seismic Network Bottom Pressure Recorders Tide Guages Complementary Observations Bhuj Bhopal Bokaro Chennai Dehradun Samla Dharamshala DELHI HYDERABAD Goa Pune Shillong Thiruvananthapuram Minicoy Vishakapattinam Diglipur Port Blair Campbell Bay KANNIYAKUMARI MAGDALLA JAIGARH RAMESHWARAM PONDICHERRY NIZAMPATNAM AERIAL BAY ANDROTH CAMPBELL BAY KAVARATTI (+1) VERAVAL ENNORE EXISTING TIDE GAUGE STATIONS PROPOSED TIDE GAUGE STATIONS CHANDIPUR CHENNAI (+1) MACHALIPATNAM VISHAKHAPATNAM PARADIP (+1) BEYPORE MINICOY COCHIN (+1) TUTICORIN (+1) NAGAPATNAM (+1) MANGALORE (+1) KARWAR GOA (+1) MUMBAI (+1) PORABANDAR VADINAGAR OKHA KANDLA NANCOWRY PORT BLAIR (1+2) RANGAT BAY (2) GARDEN REACH DIAMOND ARBOUR (+1) HALDIA (+1) SAGAR KAKINADA VIZHINJAM PIPAV TB4 TB1 TB5 TB6 TB3 TB2 TB7 TB8 TB9 TB10 TB12 TB11 I N D I A 5 Coastal Radars 2 Current Meter Moorings 26 Surface Drifters 2 XBT Lines Surface, Met-Ocean observing platforms Observations from other Systems on Internet Network of 17 Seismic stations with Central Receiving Stations at IMD Delhi and INCOIS, Hyderabad for monitoring the seismic activity Network of 12 Deep Ocean Assessment and Reporting Systems (DOARS) for detection of Tsunami Waves Network of 50 Tide Gauges for monitoring the progress of Tsunami Waves Buoy under Lab Test

12 Tsunami Modelling for Operational Early Warning
Epicenter (Assumed Epicenters) Depth of Fault Top Edge (0, 20, 40, 60, 80, 100) Magnitude (5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5) Fault length (log L = 0.55 M – 2.19) Fault width (log W = 0.31 M – 0.63) Displacement (log D = 0.64 M – 2.78) Strike angle (Parallel to Trench – Worst Case) Dip angle (45 deg – Worst Case) Slip angle (90 deg – Worst Case) Tsunami N2 Model Database of Scenarios Generation Seismic Deformation Propagation Models Cannot be run during the event due to large computing time and non-availability of Fault Parameters in real-time from Seismic Wave Form Data Hence for Tsunami Forecasting, database of pre-run scenarios is essential Bathymetry Run up Heights and Inundation Coastal Topography GLOBAL RELATIONS BETWEEN SEISMIC FAULT PARAMETERS AND MOMENT MAGNITUDE OF EARTHQUAKES – Papazachos B C, etal

13 PRIME student at Univ. of Hydebrad
Set up a DataTurbine server at INCOIS with their tide gauge, bottom pressure reader (BRP) and seismic data streams feeding into it as sources. This server is mirrored to a DataTurbine server at the University of Hyderabad, where RDV is used to view the real time sensor data from INCOIS. Goal is to automate the process. Test to prove the setup is working.

14 Accomplishments Set up DataTurbine server at INCOIS and UoH (mirrored)
Developed parser for various sensors. Real-time data acquisition and processing system was deployed at INCOIS for a variety of sensors including NOAA data.

15 People and groups in GLEON
15 GLEON 1 San Diego USA March 2005 GLEON 4 Lammi FI March 2007 GLEON 2 Hsinchu TW October 2006 GLEON 3 Townsville AU March 2006 15

16 A Typical GLEON Site Infrastructure
Portable Lake Metabolism Buoy North Temperate Lakes LTER Wisconsin Instrumented Platforms make high frequency observations of key variables and send data to the field-station - Sonde as well as gas diffuser. 16

17 Status of DataTurbine GLEON Deployments
Freeway Serial Radio Link Cellular Link Lake Sunapee, NH Lake Erken, Sweden Northern Temperate Lake, Wi Thanks to GLEON community!

18 Coral Reef Environmental Observatory Network (CREON)
GBR NOAA Taiwan UCSB Source: Stuart Kininmonth, AIMS Source : Fang-Pang Lin, NCHC 18

19 Network of Underwater Cameras at Kenting
Collaboration with NCHC, Thanks to Fang-Pang Lin, Ebbe, and other staff members

20 Screen Capture of Acquired Video
streams via RDV

21 Integration with Tile Display Wall (TDW)
TDW at UCSD showing real-time streaming data from underwater cameras at Kenting

22 Moorea Coral Reef Deployment

23 Tsunami Detection at MCR

24 Acknowledgements INCOIS staff members, India
University of Hyderabad, India Open Source DataTurbine Initiative Team and community Funding Agencies NSF Gordon and Betty Moore Foundation GLEON, CREON, communities Corporate Partners

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