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The Rationale and Methodology of the 2nd SC5 Pilot

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Rationale and methodology regarding the 2nd BDE SC5 pilot, as presented during the 3rd online hangout

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The Rationale and Methodology of the 2nd SC5 Pilot

  2. 2. Framework ¥ Computational modelling of atmospheric dispersion of hazardous pollutants ¥ How can BigDataEurope Integrator tools contribute to performing more efficiently computational tasks related to atmospheric dispersion of hazardous pollutants? 11-oct.-16www.big-data-europe.eu
  3. 3. Purposes and means ¥ Air pollution abatement / early warning / countermeasures o Anthropogenic emissions: routine, accidental (nuclear, chemical), malevolent (terrorist) – unannounced releases o Natural emissions (e.g., volcanic eruptions) ¥ Measurements (from earth or space) ¥ Mathematical modelling ¥ Combination of the above → “forward” or “inverse” modelling through “data assimilation” 11-oct.-16www.big-data-europe.eu
  4. 4. Input data for dispersion modelling ¥ Meteorology ¥ “Source term”: knowledge of the emitted pollutant(s) source(s): Location, quantity and conditions of release, timing ¥ Terrain characteristics, geometry of buildings etc. ¥ Depending on available input and measurement data: “forward” or “inverse” modelling 11-oct.-16www.big-data-europe.eu
  5. 5. Cases of “inverse” computations ¥ The pollutant emission sources are NOT known: location and / or quantity of emitted substances o Technological accidents (e.g., chemical, nuclear), natural disasters (e.g., volcanos): known location, unknown emission o Un-announced technological accidents (e.g. Chernobyl), malevolent intentional releases (terrorism), nuclear tests ¥ Inverse “source-term” estimation techniques 11-oct.-16www.big-data-europe.eu
  6. 6. Inverse source-term estimation ¥ Available information: o Measurements indicating the presence of air pollutant o Meteorological data for now and recent past ¥ Mathematical techniques blending the above with results of dispersion models to infer position and strength of emitting source o Special attention: multiple solutions 11-oct.-16www.big-data-europe.eu
  7. 7. Introducing the 2nd BDE SC5 Pilot ¥ The previously mentioned mathematical techniques require large computing times ¥ Purpose: fast estimation of source location in emergencies ¥ Proposed solution: pre-calculate a large number of scenarios, store them, and at the time of an emergency select the “most appropriate” ¥ BDE will provide the tools to perform this functionality efficiently 11-oct.-16www.big-data-europe.eu
  8. 8. Structure of the 2nd BDE SC5 Pilot ¥ Geographic area: Europe ¥ Cases of interest: accidents at Nuclear Power Plants ¥ Weather calculations: o Re-analysis data for 20 years o Clustering → “typical” weather circulation patterns o Downscaling through WRF for the “typical” weather circulation patterns 11-oct.-16www.big-data-europe.eu
  9. 9. Structure of the 2nd BDE SC5 Pilot ¥ Dispersion calculations: o Calculation of dispersion patterns from NPPs for the above downscaled typical weather circulation patterns o Dispersion results: gridded and (optionally) at monitoring stations 11-oct.-16www.big-data-europe.eu
  10. 10. Structure of the 2nd BDE SC5 Pilot ¥ In the event of radiation signals at some stations: o Matching of current and recent weather to closest typical circulation pattern o From the stored dispersion results pertaining to the matched weather circulation patterns select the one that closest matches the monitoring data o The matched dispersion pattern will reveal the most probable emission source 11-oct.-16www.big-data-europe.eu
  11. 11. So far … ¥ Preliminary clustering studies on limited amount of re-analysis data (while waiting for full download) o On the basis of different variables on different pressure levels ¥ Dispersion calculations for a selected NPP for the revealed weather classes 11-oct.-16www.big-data-europe.eu
  12. 12. So far … ¥ Selected a random date, taken as “true” accident day ¥ Matching of the “true” day’s weather data with the closest weather class from the clustering procedure ¥ Dispersion calculations with the weather data of the “true” day ¥ Comparison of dispersion results based on “true” and matched weather data 11-oct.-16www.big-data-europe.eu
  13. 13. Workflow www.big-data-europe.eu ECMWF Weather reanalysis data (20+years) WRF Pre-processed weather data Clustering Predominant weather patterns DIPCOT Dispersions for weather patterns, for a number of fixed nuclear sites Detector Detection of dangerous release Weather service Recent weather (e.g. 3 days) Batch processing Interactive workflow Comparison Candidate release origins
  14. 14. Data ¥ ECMWF Reanalysis data ¥ NCAR-UCAR Archive o Better compatibility with WPS/WRF ¥ 20-30 years o Approx. 6 TB in total ¥ Grib2 format – again for better compatibility with WRF o NetCDF via WPS ¥ Many variables at multiple geopotential heights www.big-data-europe.eu
  15. 15. Architectural Overview www.big-data-europe.eu Possible additions as BDE pilot components: (1) POSTGIS (2) DIPCOT
  16. 16. Clustering ¥ Traditional methods o Agglomerative hierarchical o K-means ¥ Soon to implement o NN-based feature extraction (e.g. autoencoders, convolution nets) o (Possibly) followed by k-means www.big-data-europe.eu
  17. 17. Evaluation ¥ Incremental o Clustering outcome o Closeness of constituent weather within clusters / distance between clusters o Dispersion characteristics o Different cluster descriptors for v Creating cluster-based dispersions v Matching “real data” to clusters ¥ Complete o Compare cluster-based dispersion against o “Real data” dispersion v For a number of hypothetical scenarios www.big-data-europe.eu
  18. 18. Preliminary results ¥ Clustering over 2-year period (1986, 1987) o K=6 clusters ¥ Multiple geopotentials ¥ Other variables – notably wind speed – at different heights ¥ “Visual comparison” against “real data” dispersions ¥ Incrementally combining more vars www.big-data-europe.eu
  19. 19. Cluster quality / GHT 500hPa www.big-data-europe.eu • 1986, 1987 • Resolution= • Items (6-hr snapshots) = • K-means, for K-6 • Geopotential height=500hPa • Dispersions well differentiated for a specific hypothetical origin • Real data:
  20. 20. Different Clustering Algorithms www.big-data-europe.eu
  21. 21. Immediate Future Work ¥ Feature extraction o Taking into account multiple variables o At more heights ¥ Automatic evaluation o For a number of pre-selected scenarios ¥ Dockerisation and inclusion into the BDE architecture www.big-data-europe.eu
  22. 22. 11-oct.-16www.big-data-europe.eu Thank you for your attention!