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Alastair Gemmell Jon Blower Keith Haines

The use of standard OGC web services in integrating distributed model, satellite and in-situ datasets. Alastair Gemmell Jon Blower Keith Haines Environmental Systems Science Centre & Reading e-Science Centre, University of Reading, UK Martin Price UK Met Office Keiran Millard

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Alastair Gemmell Jon Blower Keith Haines

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  1. The use of standard OGC web services in integrating distributed model, satellite and in-situ datasets Alastair Gemmell Jon Blower Keith Haines Environmental Systems Science Centre & Reading e-Science Centre, University of Reading, UK Martin Price UK Met Office Keiran Millard Quillon Harpham HR Wallingford, UK AGU Fall Meeting December 2008

  2. Talk Outline • Context to the work • Potential problems • Useful tools and technologies • Our solution • Summary

  3. Context to the work • Part of ECOOP project: European Coastal Operational Oceanography. 71 partners across most European countries. • Task was to integrate model, in-situ, and satellite data feeds from various project partners into one portal. • Users should be able to compare observed data with the model outputs. • Initial focus was on Ecosystem-relevant data in the North Sea. This has been expanded to other data.

  4. Potential problems • Lack of interoperability • Heterogeneity in data formats • Data dispersed around Europe

  5. Useful tools and technologies • OGC standards (www.opengeospatial.org) • Web Map Service (WMS) • Web Feature Service (WFS) • Climate Science Modelling Language (CSML) • THREDDS / OPeNDAP

  6. Useful tools and technologies – OGC standards • WMS = Web Map Service. Serve geo-referenced images. Ideal for model output (also satellite data). • WFS = Web Feature Service. Serve geo-referenced points, lines, polygons. Ideal for in-situ observations, trajectories etc. • OGC has specifications for these services, allowing data to be served in a consistent manner. • Applications know what data format to expect and how to ask for it. • ncWMS: OGC-compliant WMS for NetCDF developed at Reading e-Science Centre (ncwms.sf.net). • Connects to Godiva2 web client which uses OpenLayers to display data.

  7. Useful tools and technologies – CSML • Climate Science Modelling Language (csml.badc.rl.ac.uk) • A standards-based way of representing data features pertinent to the Climate Sciences. • 13 main feature types including profiles, trajectories, swaths, timeseries. • Provides a common view onto datasets, independent of their storage format or physical location. Ideal for integrating diverse data products • We are developing a set of reusable Java libraries that embody the CSML concepts - can then apply these techniques to a number of other projects.

  8. Useful tools and technologies – THREDDS • THematicRealtime Environmental Distributed Data Services (www.unidata.ucar.edu/projects/THREDDS/) • Enables data providers to serve NetCDF and similar data easily online via OPeNDAP protocol. Subsetting of data can be built into request • THREDDS now contains ncWMS bundled in (http://www.unidata.ucar.edu/projects/THREDDS/tech/TDS.html). • Data can be served by THREDDS either via OPeNDAP (e.g. obs data), or WMS (e.g. model images). • Diverse datasets held in different places can read in via THREDDS servers at each institute.

  9. ASCII PML THREDDS Server PML THREDDS Server HR Wallingford WFS CF-NetCDF CSML ESSC Web Portal ESSC ncWMS web app. ESSC ECOOP Obs web app. Our solution PML POLCOMS-ERSEM MRCS model (Biological) CEFAS SmartBuoys SMHI SEPRISE In-situ data REMOTE ESSC POLCOMS MRCS model (Physical) ESSC Ferrybox (NetCDF) LOCAL

  10. This looks like a suspiciously large and constant difference between obs and model Comparing / co-plotting datasets can catch errors!

  11. Summary • Historically data providers in different disciplines used different formats. This created barriers to multidisciplinary work. • Increasingly there are common standards and conventions for formatting and serving data. There is gradual uptake of these by providers. • We have found OGC WMS and WFS effective solutions for working with model and observed data respectively. • CSML was useful as a means of representing the diverse data as common feature types. • These tools and technologies allows diverse data to be brought together for the first time – this can reveal new trends in the data, as well as helping with quality control.

  12. Summary Thanks for your attention a.l.gemmell@reading.ac.uk www.resc.reading.ac.uk

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