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Summer School Rio de Janeiro March 2009 6. MODELING CONVECTIVE PBL Amauri Pereira de Oliveira Group of Micrometeorology.

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Presentation on theme: "Summer School Rio de Janeiro March 2009 6. MODELING CONVECTIVE PBL Amauri Pereira de Oliveira Group of Micrometeorology."— Presentation transcript:

1 Summer School Rio de Janeiro March 2009 6. MODELING CONVECTIVE PBL Amauri Pereira de Oliveira Group of Micrometeorology

2 2 Topics 1.Micrometeorology 2.PBL properties 3.PBL modeling 4.Modeling surface-biosphere interaction 5.Modeling Maritime PBL 6.Modeling Convective PBL

3 3 Modeling Convective PBL

4 4 Convective PBL Nieuwstadt, F.T.M. and Duynkerke, P.G., 1996: Turbulence in the boundary layer, Atmospheric Research, 40, 111-142.

5 5 Similarity Theory - CBL Monin and Obukhov similarity Holstlag and Neuiwastadt 1988. Mixing Layer Similarity Free Convection Similarity

6 6 LES MODEL Investigation of Carbon Monoxide in the city of Sao Paulo using LES

7 7 Codato, G., Oliveira, A.P., Soares, J., Marques Filho, E.P., and Rizza, U., 2008: Investigation of carbon monoxide in the city of São Paulo using large eddy simulation. Proceedings of 15th Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA, 88th Annual Meeting, 20-24 January 2008, New Orleans, LA (CDROM). Codato. G., 2008: Simulação numérica da evolução diurna do monóxido de carbono na camada limite planetária sobre a RMSP com modelo LES. Dissertação de Mestrado. Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, SP, Brasil, 94 pp. Available at

8 8 Objective To investigate the statistical properties of the convective planetary boundary layer (PBL) over a homogeneous urban surface using LES. Emphasis in the characterization of the turbulent transport of carbon monoxide at the top of the PBL during daytime.

9 9 Metropolitan region of São Paulo (MRSP) Conurbation of 39 cities 20 million habitants 7 millions vehicles 1.48 tons of CO per year

10 10 Air pollution problem in São Paulo is particularly dramatic during winter

11 11 Location LES domain CO measurements Air pollution monitoring Network Stations 8,051 km 2 23º33S, 46º44W Altitude 742m 60 km far from Atlantic ocean

12 12 Topography Metropolitan Region of São Paulo Valley

13 13 LES domain Relatively flat

14 14 Carbon Monoxide – Seasonal Evolution (1996 to 2005) Winter Maximum

15 15 Carbon Monoxide – Diurnal evolution June (1996 -2005) First maximumSecond maximum

16 16 Wind – Seasonal evolution (1996 -2005) Winds in São Paulo are weak.

17 17 Wind – Diurnal evolution - June ( 1996 -2005) Morning winds are weaker than in the afternoon Stronger SE wind in the afternoon is due to Sea Breeze

18 18 Time rate of change of CO in June

19 19 LES Model

20 20 LES Model The motion equation are filtered in order to describe only motions with a length scale larger than a given threshold.

21 21 Reynolds Average f

22 22 LES Filter f large eddies

23 23 Convective Boundary Layer Updraft Source: Marques Filho (2004) Cross section

24 24 Convective PBL – LES Simulation Source: Marques Filho (2004) ( z i /L ~ - 800)

25 25 Spectral Properties – LES Simulation Fonte: Marques Filho (2004)

26 26 Caso DA2= TKE budget

27 27 LES Model – Moeng It was developed by Moeng (1984) and modified by Sullivan et al. (1994): 6 prognostic equations 1 diagnostic Filtering all variables by

28 28 (1) (2) (3) Set of equations used in the LES model (4) (5) (6) (7)

29 29 homogeneousnon-homogeneous Sullivan et al. (1994) subgrid parametrization

30 30 Sub Grid TKE equation Turbulent diffisivity coefficients where Convective Stable

31 31 LES Model- Moeng Boundary conditions Periodic in the lateral Rigid at surface Radiative at the top Surfaces Horizontally Homogeneous Sensible heat flux (prescribed) Momentum flux (MOST)

32 32 Grid points(128, 128, 128) u g,v g (2ms -1 ; 0ms -1 ) (L x, L y, L z )(10 km; 10 km; 2 km ) θ ini 295 K Δx=Δy78.125 m5 K Δz15.625 m Γ θ 5 K km -1 Time step1 sec z 0 0.16 m Total time36000 time steps c ini 2.5 ppm z ini 300 m2.30 ppm 93.75 m (6 levels). Γ c 0 ppm km -1 Numeric Model

33 33 Initial Conditions – Vertical profiles

34 34 Boundary Condition Sensible heat flux B θ = 0.209 K m s -1 t = time in hours

35 35 Boundary Condition – CO flux at surface The amplitude of CO flux at the surface is based on the total emission of CO in the MRSP (1.48 million of tons per year) divided by number of days in one year and by the area representative of traffic in São Paulo (8,051 km 2 ). In reality the value of B co was set equal to 1/6 of the value above. This was obtained by trial and error and there is no apparent reason.

36 36 Boundary condition CO flux at the surface B CO = 0.024 ppm ms -1 t 1 = 9 hour t 2 = 19 hour = 3 hour

37 37 Results The results are based on the three-dimensional fields generated after turbulence has reached quasi- steady equilibrium; The statistics were obtained ensemble averaging 15 outputs, separated by 1200 time steps each, corresponding to 20 minutes. Important to emphasize that the time step is 1 second; Statistical properties are estimated at 8:30, 9:30, 10:30, 11:30 and 12:30 LT.

38 38 Initial jump Time evolution of turbulent kinetic energy per unit of mass volume- averaged in the PBL. E= 0.5 (u´ 2 +v´ 2 +w´ 2 ). Quasi-steady equilibrium after 1000 s

39 39 Time (hour) L (m)z i (m) (m s -1 ) (s) (K) 1-6.731647.561.04305.30.10 2-4.834472.181.20331.60.13 3-4.138995.921.33375.20.14 4-4.1465114.131.46448.80.14 5-4.4615140.671.60593.40.13 6-5.5820148.131.70791.00.11 7-6.7992148.861.70957.10.09 8-8.01110138.581.561070.80.07 9-12.5116593.331.241124.40.04 10-229.811265.120.331086.70.00 PBL characteristic scales

40 40 PBL height

41 41 Potential temperature and sensible heat flux

42 42 Zonal component and momentum flux

43 43 Variance of the wind speed components and TKE

44 44 CO concentration and vertical flux

45 45 Comparison with observation – Potential temperature at the surface

46 46 Comparison with observation – CO concentration at the surface

47 47 Entrainment intensity

48 48 Surface emission, entrainment and hypothetical horizontal advection 48

49 49 Conclusion Simulation of daytime evolution PBL over the MRSP carried out using LES model indicated several characteristics consistent with a convective PBL. The simulated diurnal evolution of CO concentration indicates that entrainment of clean air at the top of the PBL is one of the dominant mechanism reducing the concentration of CO at the surface as observed in São Paulo during the winter.

50 50 Conclusion Comparison between entrainment, surface emission and hypothetical horizontal advection indicates that this late mechanism could be responsible by considerable reducing in the CO diurnal evolution in the city of Sao Paulo. Next step would be evaluated the role of horizontal advection.

51 51 Where advection is important

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