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Roughness Sublayer and Canopy Layer turbulent profiles over tall vegetation

Roughness Sublayer and Canopy Layer turbulent profiles over tall vegetation. Ricardo K. Sakai D. R. Fitzjarrald Matt Czikowsky University at Albany, SUNY. Surface Layer. Inertial sublayer. Cross section from Laser Vegetation Imaging Sensor (LVIS). Constant Flux. Roughness sublayer.

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Roughness Sublayer and Canopy Layer turbulent profiles over tall vegetation

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  1. Roughness Sublayer and Canopy Layer turbulent profiles over tall vegetation Ricardo K. Sakai D. R. Fitzjarrald Matt Czikowsky University at Albany, SUNY

  2. Surface Layer Inertial sublayer Cross section from Laser Vegetation Imaging Sensor (LVIS) Constant Flux Roughness sublayer Rugosity = f(canopy topography) Jess Parker Canopy sublayer

  3. Wind Tunnel cylinders HF Foliated Deciduous HF Leafless Deciduous Boreal forest Coniferous Oak Ridge deciduos Amazon Broad Leaf Almond orchard Camp Borden Deciduous Canopy area densities (CAD, , where PAI is the plant area index) for (a) for wind tunnel (Raupach et al., 1986), (b) HF foliated (Parker, personal communication), (c) HF leafless (Parker, personal communication), (d) coniferous forest (Halldin, 1985), (e) Amazon forest (Roberts et al., 1994) (f) Oak Ridge (Meyers and Baldocchi, 1991), (g) almond orchard (Baldocchi and Hutchison, 1988), (h) Camp Borden (Neumann et al., 1989).

  4. CANOPY LAYER

  5. Normalized cumulative canopy area density: where h is the mean canopy height. PAI is plant area index CAD is the canopy area distribution

  6. σw /u*vsz/h σw /u*vszc MOS value in IL

  7. σU /u*vsz/h σU /u*vszc MOS value in IL

  8. u*(z/h)/u*(1) vsz/h u*(z/h)/u*(1) vszc

  9. Roughness Sublayer

  10. For a broad leaf forests: Displacement height (d) - mean level of momentum absorption (Thom, 1971): Traditional: New approach: Therefore: dc=0.7 h (Deciduous - Broad leaf forests) dc=0.6 h (sparse coniferous) - numerically

  11. σwvsz/h σwvs (z-dc)/(h-dc) MOS value in the IL

  12. Skw(w) vsz/h Skw(w) vs (z –dc)/(h-dc) Skewness Skewness Skewness in the lower CBL

  13. Fitted curve (above canopy):

  14. Spectral Analysis Drowning in spectra, craving cospectra

  15. Cospectral shape in RSL: -5/3 power law Su Less peaked More peaked Moraes et al., accepted in Physica A

  16. Dimensionless frequency: Where: Above canopy: z > h → z’= z-dc Inside canopy: z < h → z’= h-dc

  17. Conclusions: Seeking similarity rules for tall canopies. Scaling length is (h-dc(CAD)) in the RSL for several forests Canopy Layer (several forests): - The use of the canopy area density helps to differentiate broad leaves from coniferous forests, approaching to a more “universal relationship”. To improve, rugosity? Roughness sublayer (several forests): - Ratio [(z-dc)/(h- dc)] is about 2.4 to 3.5 - Scaling to (z-d)/(h-d) gives better generalization. - Skewness of w profile is a good indicator of the RSL Spectral analysis (only HF): - best scaling is (h-dc) within the canopy. - “Short circuit”/wake effect only during the foliated period

  18. [previous][next] Gryanik and Hartmann,2002,JAS Fig. 4. (a), (c) Skewness of the vertical wind velocity and (b), (d) the temperature. (a) and (b) Full dots represent the Reynolds-averaged skewness and open circles the mass-flux skewness [Eq. (10)] of the aircraft data. Solid lines are the LES results of free convection of MGMOW. The ordinates show normalized height. (c), (d) Mass-flux vs Reynolds skewness of the aircraft data. The ratio of the mass-flux to Reynolds skewness is 0.30 for Sθ and 0.31 for Sw, but the correlation is very low. De Laat and Duynkerke (1998) found a ratio of 0.25 for Sw for a stratocumulus case

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