1. A parameterization for sub-grid emission variability
S. Galmarini1, J.-F. Vinuesa1 and A. Martilli2
1EC-DG-Joint Research Center, Italy
2CIEMAT, Spain
Stefano Galmarini, DG-Joint Research Center, IES

2. Stefano Galmarini, DG-Joint Research Center, IES

3. Stefano Galmarini, DG-Joint Research Center, IES

4. E
Stefano Galmarini, DG-Joint Research Center, IES

5. Stefano Galmarini, DG-Joint Research Center, IES

6. sE E
Stefano Galmarini, DG-Joint Research Center, IES

7. How to transfer source intensity variability to upper atmospheric layers?
Turbulent motions are responsible for creating and generating scalars concentration variance
In RANS scalar variance is accounted for by means of the variance conservation equations
The source variability at the surface can be though as a boundary condition of scalar variance equation that will take care of describing its transport in x, y and z, creation and dissipation
Stefano Galmarini, DG-Joint Research Center, IES

8. Formulation
Stefano Galmarini, DG-Joint Research Center, IES

9. Equation closure
Stefano Galmarini, DG-Joint Research Center, IES

10. Approach U=5m.s-1 Total duration LES=3hours
The dynamic at the end of the first hour is used to fed FVM (u,v,w,theta).
Then emission is released for 2 two hours. Statistics are done over the last hour.
10 Km
10 Km
LES =100 x 100 grid cells, 100 m resolution
Sv3=64% of 5x5km2 (LES-1)
Sv4=36% of 5x5km2(LES-2)
Sv5=25% of 5x5km2(LES-3)
Sv6=16% of 5x5km2(LES-4)
Release of=0.1 ppb.m.s-1
FVM= 2 x 2 grid cells, 5 km resolution
Stefano Galmarini, DG-Joint Research Center, IES

11. Source size= 64% 5 km2 grid element
Stefano Galmarini, DG-Joint Research Center, IES

12. Source size= 16% 5 km2 grid element
Stefano Galmarini, DG-Joint Research Center, IES

13. Stefano Galmarini, DG-Joint Research Center, IES

14. Results: concentration varianceB C D
16% surface emission
64% surface emission
Stefano Galmarini, DG-Joint Research Center, IES

15. Virtual monitoring stations
Stefano Galmarini, DG-Joint Research Center, IES

16. 64%
Stefano Galmarini, DG-Joint Research Center, IES

17. 16%
Stefano Galmarini, DG-Joint Research Center, IES

18. Conclusion A simple method to account for variability of emission
Possibility to add error bars to model results
Further steps: adding the information on the spatial variability
Stefano Galmarini, DG-Joint Research Center, IES

19. Results: mean concentrationB C D
Stefano Galmarini, DG-Joint Research Center, IES

20. Stefano Galmarini, DG-Joint Research Center, IES

21. Stefano Galmarini, DG-Joint Research Center, IES

22. Stefano Galmarini, DG-Joint Research Center, IES