CoopEUS User Scenario: Tsunami Modelling and Early Warning Systems for Near Source Areas (Mediterranean, Juan de Fuca).

Transcription

1 CoopEUS User Scenario: Tsunami Modelling and Early Warning Systems for Near Source Areas (Mediterranean, Juan de Fuca).

2 Contributors EMSO Laura Beranzoli, Mairi Best, Francesco Chierici, Davide Embriaco, Massimiliano Rossi OOI Robert Weller, Nan Galbraith, Deborah Kelly, Oscar Scofield, Scott Glenn, John Delaney, John Orcutt ONC Martin Heeseman, Martin Scherwath, Benoit Pirenne, Tania Insua

3 Tsunami User Scenario Need Tsunami modelling and early warning system for near source areas (Meditteranean, Juan de Fuca) Common need of tsunami events field data and modelling to deepen our experience in testing methodology and developing real-time data processing

4 Tsunami User Scenario Develop based on 1. Available data: Review sensors/settings/locations Tsunami field data are already available through ocean observatories for past events 2. Real time data Review sensors/settings/locations Review algorithms available for in-line processing

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6 OOI

7 ONC

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9 Metadata The research infrastructures are using a range of metadata approaches, but arrive at a comprehensive documentation through different paths. EMSO metadata through MOIST follows a conceptual model developed through the ESA: ML 0: filed level (material/physical), ML 1: sensor detection level (commercial), ML 2: exploitation, preservation and dissemination level (documentation), ML 3: operational/field features ONC metadata system has been in development for over a decade, and has benefited from a range of standards from different fields. Ocean Networks Canada keeps and provides metadata on ALL aspects of the data, including instrument details, configurations, calibrations, deployment locations, quality status and associated Principal Investigators. They follow the Federal Geographic Data Committee (FGDC) international metadata standards. OOI metadata systems are in development, and will support the US Marine Metadata Interoperability initiative of NSF.

10 Available tsunami data Select type of parameters: Ground motion Bottom water pressure Sea-level.. Compare and harmonise measurement metadata (attributes) Compare sensor types/models for resolution/sensitivity/configurations: BPR, seismometers, Hydrophones, etc Quality assessment/control processes: specify basic key indicators for users (gaps, out-of-range, corruption, etc )

11 OOI Data and Metadata (Pre-release)

12 EMSO Data and Metadata

13 ONC Data and Metadata

14 OOI Pre-release Documentation

15 EMSO Query Result - Pressure Node DeviceCat Depth Lat Lon Sampling Rate Link to Manual Start date End date Iberian Margin APG sample / 15 sec Paroscientific 8CB-4000-I - Manual.pdf :15: :55:30 Iberian Margin APG sample / 15 sec Paroscientific 8CB-4000-I - Manual.pdf :46: :14:45 Western Ionian Sea APG sample / 15 sec Paroscientific 8CB-4000-I - Manual.pdf :00: :52:30 Iberian Margin CTD sample / hour SBE 16plus - Manual.pdf Iberian Margin CTD sample / hour SBE 16plus - Manual.pdf Western Ionian Sea CTD sample / 10 min SBE 37-SM - Manual.pdf Western Ionian Sea CTD sample / 12 min SBE 37-SM - Manual.pdf Western Ionian Sea CTD sample / hour SBE 37-SM - Manual.pdf Marmara Sea CTD sample / 10 min SBE 16plus - Manual 6047.pdf Marmara Sea CTD sample / 10 min SBE 16plus - Manual 6047.pdf :18: :18: :31: :24: :00: :08: :00: :18: :18: :21: :48: :03: :08: :00:00 Ionian Sea lagoons and embayments CTD sample / min SBE 37-SI - Manual.pdf Weddell Sea CTD sample / hour SBE 16 - Manual.pdf :00: :00: :59: :00:00

16 ONC Query Result Pressure Currently Deployed Subset Node DeviceCat Depth (m) Lon Lat Sample period (s) Resolution Instrument Endeavour BPR < 100 ppb full 800 ms integration time NRCan Bottom Pressure Recorder 80 Cascadia Basin BPR < 100 ppb full 800 ms integration time NRCan Bottom Pressure Recorder 52 Clayoquot Slope BPR < 100 ppb full 800 ms integration time NRCan Bottom Pressure Recorder 88 Barkley Upper Slope BPR < 100 ppb full 800 ms integration time NRCan Bottom Pressure Recorder 59 Folger Deep BPR < 100 ppb full 800 ms integration time NRCan Bottom Pressure Recorder 5 Cascadia Basin CORK < 100 ppb full 800 ms integration time CORK P (1026B) Cascadia Basin CTD ppb full 1 s integration time Sea-Bird SeaCAT SBE16plus V Clayoquot Slope CTD ppb full 1 s integration time Sea-Bird SeaCAT SBE16plus V Barkley Canyon CTD ppb full 1 s integration time Sea-Bird SeaCAT SBE16plus V Barkley Canyon CTD ppb full 1 s integration time Sea-Bird SeaCAT SBE16plus V Barkley Canyon CTD ppb full 1 s integration time Sea-Bird SeaCAT SBE16plus 6002 Barkley Upper Slope CTD ppb full 1 s integration time Sea-Bird SeaCAT SBE19plus V Folger Deep CTD ppb full 1 s integration time Sea-Bird SeaCAT SBE16plus V Folger Pinnacle CTD ppb full 1 s integration time Sea-Bird SeaCAT SBE16plus 6001

17 Common Portal/Access A prototype was started to be developed by EMSO, however delays in OOI data release made this a less fruitful path within the period of the project. EMSO and OOI participated in the GEOSS workshop, with moderate success see R. Huber s presentation to follow. OOI and ONC now deliver their seismic data to IRIS, OOI also sends their pressure data and ONC is considering contributing theirs as well. EMSO partners will be approached to consider the idea of contributing data to IRIS.

18 ONC Data via IRIS

19 OOI Data via IRIS

20 Algorithm Test Comparison of DART vs TDA algorithms EMSO algorithm (TDA) tested on Juan de Fuca (ONC) data next steps: further tests on newly released OOI data joint sensor registries

21 Real time data Tsunami wave detection (A) One month of sea-level data recorded by the tsunameter during the first mission in North-Eastern Atlantic ( ). The ordinate unit of measurement is given in Equivalent Water Height (EWH): 1cm EWH 100 Pa 1 mbar. (B) Output of the Tsunami Detection Algorithm (TDA) applied to a portion of the data. Note the strong reduction of the final dynamic range (< 2 cm; red line), compared with raw data range (> 3 m). In blue the data after de-tiding performed by the real-time TDA are shown. Monna et al., Marine Sys., in press

22 Real time data Tsunami wave detection (C) Example of bottom pressure data acquired by the Tsunameter installed onboard EMSO NEMO-SN1 abyssal cabled observatory. (D) Detail of the pressure data in (C) which shows the submillimetric resolution achieved by the tsunami detector.

23 New real-time Tsunami Detection Algorithm (site adaptive, high reliability and accuracy, low computational cost) Pressure data acquired every 15 s (stand alone) or s (cabled) are processed by the Real time Algorithm, which perform Tide removal Spike removal Low pass + Prediction algorithm (Newton linear predictor) or Band Pass like filter (the more effective: after15 days, 8 astronomical tide coeff.) All parameters can be re configured in real time 200 cm Tsunami 100 hours 2 cm 2 hours Pressure Raw data Processed data 23

24 The two algorithms, DART type and new developed TDA, have been tested and compared using real bottom pressure data with synthetic Tsunami of different period, amplitude, phase and shape overlapped (MONTECARLO simulation). pure linear prediction (DART) TDA

25 ONC Tsunami data using TDA 2012 October 28th Haida Gwaii Seismic and Tsunami event, as recorded by Clayquot Slope cabled bottom pressure recorder (BPR) of the ONC NEPTUNE network.

26 Global Extension Similar geographic and seismic scenarios Similar instruments Canadian ONC - already involved Japanese DONET

27 Progress discussions over the years were held with EMSO, OOI, and ONC about opportunities and timing for coordination work a working workshop was held at AGU 2014, involving key people from EMSO, OOI, and ONC joint posters were contributed to AGU 2014 and EGU 2015 data and metadata are available from EMSO and ONC online, OOI system is to be released shortly pressure and seismometer data just released through IRIS EMSO algorithms were applied to ONC data

28 Ongoing work: Continue to compare instruments and settings as OOI information becomes available Gap analysis of sensor coverage (temporal and spatial) Compare/harmonise/align sensor metadata and registries Continue to evolve data policies and access Collaborative framework for testing already available algorithms for tsunami detection Common form of data/metadata exposure Common repository for best practices, processes, protocols, workflows, algorithms Common language for levels of QA/QC Common language for levels of data processing/data products Controlled vocabularies ontologies metadata