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The physics background of the BDE SC5 pilot cases

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Presented by Spyros Andronopoulos (NCSR-Demokritos) during the 2nd BDE SC5 workshop, 11 October 2016, in Brussels, Belgium

Veröffentlicht in: Technologie
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The physics background of the BDE SC5 pilot cases

  2. 2. Common background  The earth’s atmosphere is the common physical background of the 2 SC5 BDE pilots  BigDataEurope provides tools contributing to more efficient management / processing of data related to different aspects of studying the atmospheric processes 11-oct.-16www.big-data-europe.eu
  3. 3. Why do we study the atmosphere?  Weather prognosis  Climate change prognosis  Air pollution abatement / early warning / countermeasures o Anthropogenic emissions: routine, accidental (nuclear, chemical), malevolent (terrorist) – unannounced releases o Natural emissions (e.g., volcanic eruptions) 11-oct.-16www.big-data-europe.eu
  4. 4. Methods and means  How do we study the atmosphere? o Measurements (from earth or space) o Mathematical modelling o Combination of the above → “forward” or “inverse” modelling through “data assimilation” 11-oct.-16www.big-data-europe.eu
  5. 5. Atmospheric motion  Atmosphere is a fluid o Energy supplier: the sun o Energy and water exchanges with the soil and oceans  Motions driven by “real” (pressure gradients, friction etc.) and “apparent” forces (due to earth’s motion)  Common characteristic of fluid flows: TURBULENCE  Atmospheric turbulence consists of eddies with vast range of size- and time-scales 11-oct.-16www.big-data-europe.eu
  6. 6. Scales of atmospheric motions 11-oct.-16www.big-data-europe.eu • Motions are connected • Energy flows from large to small scale motions
  7. 7. Mathematical description  Conservation equations for mass, momentum, energy, humidity + equation of state o Represent basic physical principles  Partial differential equations  NO analytical solution  Numerical solution in computer codes - models 11-oct.-16www.big-data-europe.eu
  8. 8. Numerical solution  We split the “computational domain” to a “grid” of points or volumes, “discretize” the equations  For each variable: number of unknowns = number of grid points  How fine should this grid be (ideally)? o Earth’s surface: 5.1 ×1014 m2 o Smallest eddies: 10-1 m o Height: 1.2 ×104 m o Time step: 1s 11-oct.-16www.big-data-europe.eu 6.12 × 1020 grid cells NOT POSSIBLE
  9. 9. Averaging / filtering  We average – in space and time – the equations o Sub-grid-scale motions are parameterized  Split the earth’s surface in grids with steps of ¼ of a degree and fewer vertical levels: 1.0 ×108 cells  Big Data tools necessary here  Possible, good enough for global weather forecasting, not good enough for local scale motions 11-oct.-16www.big-data-europe.eu
  10. 10. Downscaling / nesting  Smaller computational domain(s) are defined over area(s) of interest with finer resolution (~ 1km)  Models simulate there in greater detail local weather or climate change effects  Smaller domains interact with larger ones and with global data  1st BDE SC5 Pilot contributes in the computational simulation of this process 11-oct.-16www.big-data-europe.eu
  11. 11. Example of nested domains 11-oct.-16www.big-data-europe.eu
  12. 12. Towards the 2nd pilot case  Atmospheric dispersion of pollutants  Is totally driven by meteorology  Different spatial scales involved: transport - diffusion  Downscaled / nested meteorological data may be used to “drive” the computational dispersion simulations o Connection with 1st pilot case  Crucial information: knowledge of the emitted pollutant(s) source(s): where, when, how, how much and what 11-oct.-16www.big-data-europe.eu
  13. 13. Examples of “forward” simulations  A few examples of atmospheric dispersion simulations will follow (performed by NCSRD), involving (partially) known releases of substances o We start from the pollutants release and move forward in time as dispersion evolves 11-oct.-16www.big-data-europe.eu
  14. 14. Global-scale dispersion modelling 11-oct.-16www.big-data-europe.eu 2 days 4 days 6 days 8 days 10 days 12 days
  15. 15. Regional scale dispersion modelling 11-oct.-16www.big-data-europe.eu Dispersion of ash from the Eyjafjallajökull volcano in Iceland
  16. 16. Meso-scale urban pollution  Ozone concentrations for different emission scenarios 11-oct.-16www.big-data-europe.eu
  17. 17. Local scale dispersion modelling 11-oct.-16www.big-data-europe.eu Simulation of dispersion following an explosion in a real city centre
  18. 18. Cases of “inverse” computations (1)  The pollutant emission sources are known (location and strength) and we want to assess: o The sensitivity of pollutant concentrations at specific locations to different emission sources o The sensitivity of pollutant concentrations at specific locations to concentrations of other pollutants (photochemistry) 11-oct.-16www.big-data-europe.eu
  19. 19. Inverse modelling example  Sensitivity of ozone concentration at a specific site and time on NO2 concentrations at previous times 11-oct.-16www.big-data-europe.eu
  20. 20. Inverse modelling example  Sensitivity of ozone concentration at a specific site and time on NO2 emissions accumulated until that time 11-oct.-16www.big-data-europe.eu
  21. 21. Cases of “inverse” computations (2)  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  “Source-term” estimation techniques 11-oct.-16www.big-data-europe.eu
  22. 22. 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
  23. 23. Introducing the 2nd BDE SC5 Pilot  The previously mentioned mathematical techniques require large computing times: not suitable to run in emergency response  Way out: 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
  24. 24. 11-oct.-16www.big-data-europe.eu Thank you for your attention!