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

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

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3 Modeling Convective PBL

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

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

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

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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, 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

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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.

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

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

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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

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12 Topography Metropolitan Region of São Paulo Valley

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13 LES domain Relatively flat

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

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

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

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

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18 Time rate of change of CO in June

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19 LES Model

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

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21 Reynolds Average f

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22 LES Filter f large eddies

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

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

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

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26 Caso DA2= TKE budget

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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

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

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

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30 Sub Grid TKE equation Turbulent diffisivity coefficients where Convective Stable

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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)

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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=Δy m5 K Δz m Γ θ 5 K km -1 Time step1 sec z m Total time36000 time steps c ini 2.5 ppm z ini 300 m2.30 ppm m (6 levels). Γ c 0 ppm km -1 Numeric Model

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33 Initial Conditions – Vertical profiles

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

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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.

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

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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.

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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

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39 Time (hour) L (m)z i (m) (m s -1 ) (s) (K) PBL characteristic scales

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40 PBL height

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41 Potential temperature and sensible heat flux

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42 Zonal component and momentum flux

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43 Variance of the wind speed components and TKE

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44 CO concentration and vertical flux

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45 Comparison with observation – Potential temperature at the surface

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46 Comparison with observation – CO concentration at the surface

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47 Entrainment intensity

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48 Surface emission, entrainment and hypothetical horizontal advection 48

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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.

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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.

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51 Where advection is important

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