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 SystemsSameer Tilak, Tony Fountain, Peter Shin, Brian McMahon, ArunAgarwal, K. V. Subbarao, Peter Arzberger
2 Streaming Data Middleware Common programming layer for real-time systemsEnables integration of real-time componentsProvides abstractions over vendor-specific productsSupports in-network processing (buffering, time synch …)Make data streams first class objectsAddressableEfficient operationsMonitoring, QA/QCEvent detectionReplication and subscriptionReliable transport
3 Open Source DataTurbine Initiative http://www.dataturbine.org In-network buffered data management and archiving for streaming dataScalable support for in-network intelligent routing, data processing, filtering, and topology managementRobust bridge environment between diverse data sources and distributed data destinationsOptimized for high-speed streaming dataAll-software solution (Java)Used in NSF, NASA, NOAA, DOE projectsDeveloped by Creare Inc.,OPEN SOURCE SOFTWARE - Apache 2.0 License, Jan 07NSF support from SDCI program (funding started on Sept 07)
5 DataTurbine GoogleEarth Plug-in Credit Matt Miller, Creare Inc.
6 System ArchitectureOpen Scalable, Modular architecture based on OGC-SWE standards
7 Real-World Deployments GLEONCREONAnimal TrackingEarthquake EngineeringSmart BuildingsNASA etc. etc.
8 Modeling and Prediction Open Ocean ForecastOnlineOffline
9 Tsunami SensorsIncois uses data streams from tide gauges, bottom pressure readers (BPRs), and seismic stations to detect possible tsunami activityPotential events are checked against precalculated mathematical models to aid in decision makingIntegrating 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 InternationalTsunami Buoys National InternationalTide Gauges National InternationalThis is an example of a message of tsunami warning.
11 Infrastructure Details Tsunami and Storm SurgesObservational NetworkInfrastructure DetailsSeismic NetworkBottom Pressure RecordersTide GuagesComplementaryObservationsBhujBhopalBokaroChennaiDehradunSamlaDharamshalaDELHIHYDERABADGoaPuneShillongThiruvananthapuramMinicoyVishakapattinamDiglipurPort BlairCampbell BayKANNIYAKUMARIMAGDALLAJAIGARHRAMESHWARAMPONDICHERRYNIZAMPATNAMAERIAL BAYANDROTHCAMPBELL BAYKAVARATTI (+1)VERAVALENNOREEXISTING TIDE GAUGE STATIONSPROPOSED TIDE GAUGE STATIONSCHANDIPURCHENNAI (+1)MACHALIPATNAMVISHAKHAPATNAMPARADIP (+1)BEYPOREMINICOYCOCHIN (+1)TUTICORIN (+1)NAGAPATNAM (+1)MANGALORE (+1)KARWARGOA (+1)MUMBAI (+1)PORABANDARVADINAGAROKHAKANDLANANCOWRYPORT BLAIR (1+2)RANGAT BAY (2)GARDEN REACHDIAMOND ARBOUR (+1)HALDIA (+1)SAGARKAKINADAVIZHINJAMPIPAVTB4TB1TB5TB6TB3TB2TB7TB8TB9TB10TB12TB11I N D I A5 Coastal Radars2 Current Meter Moorings26 Surface Drifters2 XBT LinesSurface, Met-Ocean observing platformsObservations from other Systems on InternetNetwork of 17 Seismic stations with Central Receiving Stations at IMD Delhi and INCOIS, Hyderabad for monitoring the seismic activityNetwork of 12 Deep Ocean Assessment and Reporting Systems (DOARS) for detection of Tsunami WavesNetwork of 50 Tide Gauges for monitoring the progress of Tsunami WavesBuoy 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 ModelDatabaseofScenariosGenerationSeismic DeformationPropagationModels Cannot be run during the event due to large computing time and non-availability of Fault Parameters in real-time from Seismic Wave Form DataHence for Tsunami Forecasting, database of pre-run scenarios is essentialBathymetryRun up HeightsandInundationCoastal TopographyGLOBAL RELATIONS BETWEEN SEISMIC FAULT PARAMETERS ANDMOMENT 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 15GLEON 1San Diego USAMarch 2005GLEON 4Lammi FIMarch 2007GLEON 2Hsinchu TWOctober 2006GLEON 3 Townsville AUMarch 200615
16 A Typical GLEON Site Infrastructure Portable Lake Metabolism BuoyNorth Temperate Lakes LTERWisconsinInstrumented 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 LinkCellular LinkLake Sunapee, NHLake Erken, SwedenNorthern Temperate Lake, WiThanks to GLEON community!
24 Acknowledgements INCOIS staff members, India University of Hyderabad, IndiaOpen Source DataTurbine Initiative Team and communityFunding AgenciesNSFGordon and Betty Moore FoundationGLEON, CREON, communitiesCorporate Partners