Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

The physics background of the BDE SC5 pilot cases

711 visualizaciones

Publicado el

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

Publicado en: Tecnología
  • Sé el primero en comentar

  • Sé el primero en recomendar esto

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
  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)
  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”
  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
  6. 6. Scales of atmospheric motions • 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
  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 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
  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. 11. Example of nested domains
  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
  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
  14. 14. Global-scale dispersion modelling 2 days 4 days 6 days 8 days 10 days 12 days
  15. 15. Regional scale dispersion modelling Dispersion of ash from the Eyjafjallajökull volcano in Iceland
  16. 16. Meso-scale urban pollution  Ozone concentrations for different emission scenarios
  17. 17. Local scale dispersion modelling 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)
  19. 19. Inverse modelling example  Sensitivity of ozone concentration at a specific site and time on NO2 concentrations at previous times
  20. 20. Inverse modelling example  Sensitivity of ozone concentration at a specific site and time on NO2 emissions accumulated until that time
  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
  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
  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
  24. 24. Thank you for your attention!