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Computational Challenges in Air Pollution Modelling Z. Zlatev National Environmental Research Institute 1. Why air pollution modelling? 2. Major physical.

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Presentation on theme: "Computational Challenges in Air Pollution Modelling Z. Zlatev National Environmental Research Institute 1. Why air pollution modelling? 2. Major physical."— Presentation transcript:

1 Computational Challenges in Air Pollution Modelling Z. Zlatev National Environmental Research Institute 1. Why air pollution modelling? 2. Major physical and chemical processes 2. Major physical and chemical processes 3. Need for splitting 3. Need for splitting 4. Computational difficulties 5. Need for faster and accurate algorithms 6. Different matrix computations 7. Inverse and optimization problems 8. Unresolved problems 4. Computational difficulties 5. Need for faster and accurate algorithms 6. Different matrix computations 7. Inverse and optimization problems 8. Unresolved problems

2 1. Why air pollution models? n Distribution of the air pollution levels n Trends in the development of air pollution levels n Establishment of relationships between air pollution levels and key parameters (emissions, meteorological conditions, boundary conditions, etc.). n Predicting appearance of high levels

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9 2. Major physical processes n Horizontal transport (advection) n Horizontal diffusion n Deposition (dry and wet) n Chemical reactions + emissions n Vertical transport and diffusion Describe these processes mathematically

10 3. Air Pollution Models

11 4. Need for splitting n Bagrinowskii and Godunov 1957 n Strang 1968 n Marchuk 1968, 1982 n McRay, Goodin and Seinfeld 1982 n Lancer and Verwer 1999 n Dimov, Farago and Zlatev 1999 n Zlatev 1995

12 4. Criteria for choosing the splitting procedure n Accuracy n Efficiency n Preservation of the properties of the involved operators

13 5. Resulting ODE systems

14 6. Size of the ODE systems n (480x480x10) grid and 35 species results in ODE systems with more than 80 mill. equations (8 mill. in the 2-D case). n More than time-steps are to be carried out for a run with meteorological data covering one month. n Sometimes the model has to be run over a time period of up to 10 years. n Different scenarios have to be tested.

15 7. Chemical sub-model n Parallel tasks The calculations at a given grid-point The calculations at a given grid-point n Numerical methods QSSA (Hesstvedt et al., 1978) QSSA (Hesstvedt et al., 1978) Backward Euler (Alexandrov et al., 1997) Backward Euler (Alexandrov et al., 1997) Trapezoidal Rule (Alexandrov et al., 1997) Trapezoidal Rule (Alexandrov et al., 1997) Runge-Kutta methods (Zlatev, 1981) Runge-Kutta methods (Zlatev, 1981) Rosenbrock methods (Verwer et al., 1998) Rosenbrock methods (Verwer et al., 1998) Criteria for choosing the numerical method?

16 8. Advection sub-model n Parallel tasks The calculations for a given compound The calculations for a given compound n Numerical methods Pseudo-spectral discretization (Zlatev, 1984) Pseudo-spectral discretization (Zlatev, 1984) Finite elements (Pepper et al., 1979) Finite elements (Pepper et al., 1979) Finite differences (up-wind) Finite differences (up-wind) “Positive” methods (Bott, 1989; Holm, 1994) “Positive” methods (Bott, 1989; Holm, 1994) Semi-Lagrangian algorithms (Neta, 1995) Semi-Lagrangian algorithms (Neta, 1995) Wavelets (not tried yet) Wavelets (not tried yet)

17 9. Discretization of the derivatives

18 10. Pseudo-spectral discretization

19 11. Convergence of the Fourier series If f(x) is continuous and periodic and if f´(x) is piece-wise continuous, then the Fourier series of f(x) converges uniformly and absolutely to f(x). Davis (1963)

20 12. Accuracy of the Fourier series It can be proved (Davis, 1963) that if

21 13. Drawbacks of the pseudo-spectral method

22 14. Finite elements The application of finite elements in the advection module leads to an ODE system: P is a constant matrix, H depends on the wind Choice of method

23 15. Matrix Computations n Fast Fourier Transforms n Banded matrices n Tri-diagonal matrices n General sparse matrices n Dense matrices Typical feature: The matrices are not large, but these are to be handled many times in every sub-module during every time-step

24 16. Major requirements n Efficient performance on a single processor n Reordering of the operations What about parallel tasks? “Parallel computation actually reflects the concurrent character of many applications” D. J. Evans (1990) D. J. Evans (1990)

25 17. Chunks on one processor SIZE Fujitsu SGI IBM SMP First line: the straight-forward call of the box routine Last line: the vectorized option Second line: using 192 chunks Owczarz and Zlatev (2000)

26 18. “Non-optimized” code Module Comp. time Percent Chemistry Advection Initialization Input operations Output operation Total IBM SMP computer, one processor

27 19. Parallel runs on IBM SMP Processors Advection Chemistry Total IBM “Night Hawk” (2 nodes); NSIZE=48

28 20. Scalability Process (288x288) (96x96) Ratio Advection Chemistry Total IBM “Night Hawk” (2 nodes); NSIZE=48

29 22. Why is a good performance needed? Grid Comp. Time Grid Comp. Time (96x96) 424 (45.8) (96x96) 424 (45.8) (288x288) 6209 ( 3.1) (288x288) 6209 ( 3.1) Non-optimized code: IBM “Night Hawk” (2 nodes); NSIZE=48

30 23. PLANS FOR FUTURE WORK n Improving the spatial resolution of the model used to obtain information. n Object-oriented code n Predicting occurrences where the critical levels will be exceeded. n Evaluating the losses due to long exposures to high pollution levels. n Finding optimal solutions.

31 24. Unresolved problems n 3-D models on fine grids n Local refinement of the grids n Data assimilation n Inverse problems n Optimization problems Important for decision makers


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