Skip to main content

Advertisement

Log in

Large-Eddy Atmosphere–Land-Surface Modelling over Heterogeneous Surfaces: Model Development and Comparison with Measurements

  • Article
  • Published:
Boundary-Layer Meteorology Aims and scope Submit manuscript

Abstract

A model is developed for the large-eddy simulation (LES) of heterogeneous atmosphere and land-surface processes. This couples a LES model with a land-surface scheme. New developments are made to the land-surface scheme to ensure the adequate representation of atmosphere–land-surface transfers on the large-eddy scale. These include, (1) a multi-layer canopy scheme; (2) a method for flux estimates consistent with the large-eddy subgrid closure; and (3) an appropriate soil-layer configuration. The model is then applied to a heterogeneous region with 60-m horizontal resolution and the results are compared with ground-based and airborne measurements. The simulated sensible and latent heat fluxes are found to agree well with the eddy-correlation measurements. Good agreement is also found in the modelled and observed net radiation, ground heat flux, soil temperature and moisture. Based on the model results, we study the patterns of the sensible and latent heat fluxes, how such patterns come into existence, and how large eddies propagate and destroy land-surface signals in the atmosphere. Near the surface, the flux and land-use patterns are found to be closely correlated. In the lower boundary layer, small eddies bearing land-surface signals organize and develop into larger eddies, which carry the signals to considerably higher levels. As a result, the instantaneous flux patterns appear to be unrelated to the land-use patterns, but on average, the correlation between them is significant and persistent up to about 650 m. For a given land-surface type, the scatter of the fluxes amounts to several hundred W \(\text{ m }^{-2}\), due to (1) large-eddy randomness; (2) rapid large-eddy and surface feedback; and (3) local advection related to surface heterogeneity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Albertson JD, Kustas WP, Scanlon TM (2001) Large-eddy simulation over heterogeneous terrain with remotely sensed land surface conditions. Water Resour Res 37(7):1939–1953

    Article  Google Scholar 

  • Ament F, Simmer C (2006) Improved representation of land-surface heterogeneity in a non-hydrostatic numerical weather prediction model. Boundary-Layer Meteorol 121:153–174

    Article  Google Scholar 

  • Avissar R, Schmidt T (1998) An evaluation of the scale at which ground-surface heat flux patchiness affects the convective boundary layer using large-eddy simulation model. J Atmos Sci 55:2666–2689

    Article  Google Scholar 

  • Beare RJ, Macvean MK, Holtslag AAM, Cuxart J, Esau I, Golaz J-C, Jimenez MA, Khairoutdinov M, Kosovic B, Lewellen D, Lund TS, Lundquist JK, McCabe A, Moene AF, Noh Y, Raasch S, Sullivan PP (2004) An intercomparison of large-eddy simulations of the stable boundary layer. Boundary-Layer Meteorol 118:247–272

    Article  Google Scholar 

  • Bhumralker CM (1975) Numerical experiments on the computation of ground surface temperature in an atmospheric general circulation model. J Appl Meteorol 14:1246–1258

    Article  Google Scholar 

  • Chen F, Dudhia J (2001) Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129:569–585

    Article  Google Scholar 

  • Clough SA, Shephard MW, Mlawer EJ, Delamere JS, Iacono MJ, Cady-Pereira K, Boukabara S, Brown PD (2005) Atmospheric radiative transfer modeling: a summary of the AER codes. JQSRT 91:233–244

    Article  Google Scholar 

  • Deardorff JW (1970) A numerical study of three-dimensional turbulent channel flow at large Reynolds numbers. J Fluid Mech 41:453–480

    Article  Google Scholar 

  • Deardorff JW (1974) On the entrainment rate of a stratocumulus-topped mixed layer. Q J R Meteorol Soc 102:563–582

    Article  Google Scholar 

  • Deardorff JW (1978) Efficient prediction of ground surface temperature and moisture with inclusion of a layer of vegetation. J Geophys Res 83:1889–1903

    Article  Google Scholar 

  • Deardorff JW (1980) Stratocumulus-caped mixed layers derived from a three-dimensional model. Boundary-Layer Meteorol 18:495–527

    Article  Google Scholar 

  • Dickinson RE, Henderson-Sellers A, Kennedy P (1993) Biosphere-aAtmosphere Transfer Scheme (BATS) Version 1e as coupled to the NCAR Community Climate Model. TN387+STR, NCAR

  • Ek MB, Mitchell KE, Lin Y, Rogers E, Grummann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational Mesoscale Eta Model. J Geophys Res 108:8851. doi:10.1029/2002JD003296

    Article  Google Scholar 

  • Foken T (2006) 50 Years of the Monin–Obukhov similarity theory. Boundary-Layer Meteorol 119:431–447. doi:10.1007/s10546-006-9048-6

    Article  Google Scholar 

  • Giorgi F, Avissar R (1997) Representation of heterogeneity effects in earth system modeling: experience from land surface modeling. Rev Geophys 35:413–437

    Article  Google Scholar 

  • Hechtel LM, Moeng C-H, Stull RB (1990) The effects of nonhomogeneous surface fluxes on the convective boundary layer: a case study using large-eddy simulation. J Atmos Sci 47:1721–1741

    Article  Google Scholar 

  • Heinemann G, Kerschgens M (2005) Comparison of methods for area-averaging surface energy fluxes over heterogeneous land surfaces using high-resolution non-hydrostatic simulations. Int J Climatol 25:379–403. doi:10.1002/joc.1123

    Article  Google Scholar 

  • Holtslag AAM, Moeng C-H (1991) Eddy diffusivity and countergradient transport in the convective atmospheric boundary layer. J Atmos Sci 48:1690–1698

    Article  Google Scholar 

  • Huang HY, Margulis SA (2009) On the impact of surface heterogeneity on a realistic convective boundary layer. Water Resour Res 45:W04425. doi:10.1029/2008WR007175

    Article  Google Scholar 

  • Huang HY, Margulis SA (2010) Evaluation of a fully coupled large-eddy simulation-land surface model and its diagnosis of land-atmosphere feedbacks. Water Resour Res 46:W06512. doi:10.1029/2009WR008232

  • Huang HY, Stevens B, Margulis SA (2008) Application of dynamic subgrid-scale models for large-eddy simulation of the daytime convective boundary layer over heterogeneous surfaces. Boundary-Layer Meteorol 126:327–348. doi:10.1007/s10546-007-9239-9

    Article  Google Scholar 

  • Iacono MJ, Delamere JS, Mlawer EJ, Shephard MW, Clough SA, Collins WD (2008) Radiative forcing by long-lived greenhourse gases: calculations with the AER radiative transfer models. J Geophys Res 113:D13103. doi:10.1029/2008JD009944

  • Irannejad P, Shao Y (1998) Description and validation of the atmosphere–land-surface interaction scheme (ALSIS) with HAPEX and Cabauw data. Glob Planet Change 19:87–114

    Article  Google Scholar 

  • Kleissl J, Kumar V, Menevea C, Parlange MB (2006) Numerical study of dynamic Smagorinsky models in large-eddy simulation of the atmospheric boundary layer: Validation in stable and unstable conditions. Water Resour Res 42: doi:10.1029/2005WR004685

  • Kumar V, Kleissl J, Meneveau C, Parlange MB (2006) Large-eddy simulation of a diurnal cycle of the atmospheric boundary layer: atmospheric stability and scaling issues. Water Resour Res 42: doi:10.1029/2005WR004651

  • Letzel MO, Raasch S (2003) Large eddy simulation of thermally induced oscillation in the convective boundary layer. J Atmos Sci 60:2328–2341

    Article  Google Scholar 

  • Liu S, Shao Y (2013) Soil layer configuration requirement for large-eddy atmosphere and land surface coupled modeling. Atmos Sci Lett. doi:10.1002/asl.426

  • Manabe S (1969) Climate and ocean circulation: 1. The atmospheric circulation and the hydrology of the earth’s surface. Mon Weather Rev 97:739–774

    Article  Google Scholar 

  • Maronga B, Raasch S (2013) Large-eddy simulation of surface heterogeneity effects on the convective boundary layer during the LITFASS-2003 experiment. Boundary-Layer Meteorol 146:17–44

    Article  Google Scholar 

  • Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J Geophys Res 102(D14):16663–16682

    Google Scholar 

  • Moeng C-H (1984) A large-eddy simulation model for the study of planetary boundary-layer turbulence. J Atmos Sci 41:2052–2062

    Article  Google Scholar 

  • Monin AS, Obukhov AM (1954) Basic laws of turbulent mixing in the ground layer of the atmosphere (in Russian). Tr Geofiz Inst Akad Nauk SSSR 151:163–187

    Google Scholar 

  • Noilhan J, Planton S (1989) A simple parametrization of land surface processes for meteorological models. Mon Weather Rev 117:536–549

    Google Scholar 

  • Oleson KW, Niu GY, Yang ZL, Lawrence DM, Thornton PE, Lawrence PJ, Stockli R, Dickinson RE, Bonan GB, Levis S (2007) CLM3.5 Documentation. UCAR, http://cgd.ucar.edu/tss/clm/distribution/clm3.5

  • Patton EG, Sullivan PP, Moeng C-H (2005) The influence of idealized heterogeneity on wet and dry planetary boundary layers coupled to the land surface. J Atmos Sci 62:2078–2097

    Article  Google Scholar 

  • Raasch S, Harbusch G (2001) An analysis of secondary circulations and their effects caused by small-scale surface inhomogeneities using large-eddy simulation. Boundary-Layer Meteorol 101:31–59

    Article  Google Scholar 

  • Raupach MR (1992) Drag and drag partition on rough surfaces. Boundary-Layer Meteorol 60:374–396

    Article  Google Scholar 

  • Schmitgen S, Gei H, Ciais P, Neininger B, Brunet Y, Reichstein M, Kley D, Volz-Thomas A (2004) Carbon dioxide uptake of a forested region in southwest France derived from airborne CO\(_{2}\) and CO measurements in a quasi-Lagrangian experiment. J Geophys Res 109(D14302). doi:10.1029/2003JD004335.

  • Shao Y, Yang Y (2008) A theory for drag partition over rough surfaces. J Geophys Res 113. doi:10.1029/2007JF000791

  • Shao Y, Sogalla M, Kerschgens M, Brücher W (2001) Effects of land-surface heterogeneity upon surface fluxes and turbulent conditions. Meteorol Atmos Phys 78:157–181

    Article  Google Scholar 

  • Shaw R, Schumann U (1992) Large-eddy simulation of turbulent flow above and within a forest. Boundary-Layer Meteorol 61:47–64. doi:10.1007/BF02033994

    Article  Google Scholar 

  • Shaw RH, Hartog G, Neumann HH (1988) Influence of foliar density and thermal stability on profiles of Reynolds stress and turbulence intensity in a deciduous forest. Boundary-Layer Meterol 45:391–409

    Article  Google Scholar 

  • Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang X-Y, Wang W, Powers JG (2008) A description of the advanced research WRF Version 3. NCAR/TN-475+STR

  • Smagorinsky J (1963) General circulation experiments with the primitive equations, Part I: the basic experiment. Mon Weather Rev 91:99–164

    Google Scholar 

  • Sullivan PP, Moeng C-H, Stevens B, Lenschow DH, Mayor SD (1998) Structure of the entrainment zone capping the convective atmospheric boundary layer. J Atmos Sci 55:3042–3064

    Article  Google Scholar 

  • Vereecken H, Kollet S, Simmer C (2010) Patterns in soil–vegetation–atmosphere systems: monitoring, modeling, and data assimilation. Vadose Zone J 9:821–827. doi:10.2136/vzj2010.0122

    Article  Google Scholar 

  • Waldhoff G (2010) Land use classification of 2009 for the Rur catchment. doi:10.1594/GFZ.TR32.1

  • Zacharias S, Reyers M, Pinto JG, Schween JH, Crewell S, Kerschgens M (2012) Heat and moisture budgets from airborne measurements and high resolution model simulations. Meteorol Atmos Phys 117:47–61. doi:10.1007/s00703-012-0188-6

    Google Scholar 

Download references

Acknowledgments

This work is supported by the DFG Transregional Cooperative Research Centre 32 “Patterns in Soil-Vegetation-Atmosphere-Systems: Monitoring, Modelling and Data Assimilation”. We thank Bruno Neininger (MetAir) for performing and processing of the aircraft measurements, Heiner Geiss (Juelich Research Center), Martin Lennefer, Dirk Schüttemeyer, Stefan Kollet (University Bonn) who supported the micrometeorological measurements, Gerritt Maschwitz for launching the radiosondes.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yaping Shao.

Appendix: Canopy Temperature Scheme

Appendix: Canopy Temperature Scheme

The equation for canopy temperature, \(T_\mathrm{c}\), can be written as

$$\begin{aligned} c_\mathrm{vg} \frac{\partial T_\mathrm{c} }{\partial t}=-{\vec {\nabla }}\cdot \vec {R}-\alpha _\mathrm{t} \varepsilon \sigma T_\mathrm{c}^4 -\rho c_\mathrm{p} S_\mathrm{T} -\rho LS_q \end{aligned}$$
(26)

where \(c_\mathrm{vg}\) is the volumetric vegetation heat capacity (J m\(^{-3}\) s\(^{-1}\)), i.e., the energy required to increase the temperature of vegetation per unit (air) volume, \(\alpha _\mathrm{t}\) is the vegetation area density (total area per unit volume), \(\varepsilon \) is vegetation emissivity, \(\rho \) is air density, \(c_\mathrm{p}\) is air specific heat at constant pressure, \(L\) is the latent heat of vaporization of water, \(S_\mathrm{T}\) and \(S_\mathrm{q}\) are as given in Eqs. 7 and 8, \(\vec {R}\) is net radiation flux. Suppose net radiation is horizontally homogeneous, then, Eq. 26 becomes

$$\begin{aligned} c_\mathrm{vg} \frac{\partial T_\mathrm{c} }{\partial t}=-\frac{\partial R_\mathrm{n} }{\partial z}-\alpha _\mathrm{t} \varepsilon \sigma T_\mathrm{c}^4 -\rho c_\mathrm{p} S_\mathrm{T} -\rho LS_q , \end{aligned}$$
(27)

where \(R_\mathrm{n}\) is the vertical component of the net radiation. For simplicity, we divide the radiation spectrum into the shortwave (solar) and longwave (terrestrial) bands. Then, as illustrated in Fig. 12, \(R_\mathrm{n}\) for any given level can be expressed as

$$\begin{aligned} R_\mathrm{n} =(R_{\mathrm{s}\uparrow } -R_{\mathrm{s}\downarrow } )+(R_{\mathrm{l}\uparrow } -R_{\mathrm{l}\downarrow } ). \end{aligned}$$
(28)

In general, radiation passing through a vegetation layer of thickness, d\(s\), is scattered and absorbed by leaves. The dependence of \(R\) on \(s\) can be expressed as

$$\begin{aligned} \text{ d }R=-kR\text{ d }s, \end{aligned}$$
(29)

where \(k\) is the canopy extinction coefficient. It therefore follows that

$$\begin{aligned} -\frac{\partial R_\mathrm{n} }{\partial z}=k_\mathrm{s} (R_{\mathrm{s}\uparrow } +R_{\mathrm{s}\downarrow } )+k_\mathrm{l} (R_{\mathrm{l}\uparrow } +R_{\mathrm{l}\downarrow } ), \end{aligned}$$
(30)

noting that \(\text{ d }R_\downarrow =-kR_\downarrow \text{ d }s\), \(\text{ d }s = -\text{ d }z\), and therefore,

$$\begin{aligned} \frac{\partial R_\uparrow }{\partial z}&= -kR_\uparrow ,\end{aligned}$$
(31a)
$$\begin{aligned} \frac{\partial R_\downarrow }{\partial z}&= kR_\downarrow . \end{aligned}$$
(31b)

In Eq. 30, \(k_\mathrm{s}\) and \(k_\mathrm{l}\) are respectively the extinction coefficients for shortwave and longwave radiation. It follows that Eq. 27 becomes

$$\begin{aligned} c_\mathrm{vg} \frac{\partial T_\mathrm{c} }{\partial t}=k_\mathrm{s} (R_{\mathrm{s}\uparrow } +R_{\mathrm{s}\downarrow } )+k_\mathrm{l} (R_{\mathrm{l}\uparrow } +R_{\mathrm{l}\downarrow } )-\alpha _\mathrm{t} \varepsilon \sigma T_\mathrm{c}^4 -\rho c_\mathrm{p} S_\mathrm{T} -\rho LS_q. \end{aligned}$$
(32)

Suppose \(c_\mathrm{vg}\) is small, then the canopy temperature can be determined from the following diagnostic equation

$$\begin{aligned} \alpha _\mathrm{t} \varepsilon \sigma {T_\mathrm{c}^{4}} =k_\mathrm{s} (R_{\mathrm{s}\uparrow } +R_{\mathrm{s}\downarrow } )+k_\mathrm{l} (R_{\mathrm{l}\uparrow } +R_{\mathrm{l}\downarrow } )-\rho c_\mathrm{p} S_\mathrm{T} -\rho LS_{q}. \end{aligned}$$
(33)

The treatment of the radiation fluxes is straightforward. Suppose the shortwave flux at the top of the canopy, \(h\), is \(R_\mathrm{sh}\). Then, the fraction of the shortwave radiation entering the canopy is \((1-a_\mathrm{vg})R_\mathrm{sh}\) and the fraction reaching the surface is

$$\begin{aligned} R_\mathrm{s0} =(1-a_\mathrm{vg} )R_\mathrm{sh} \exp \left( -\int \limits _0^h {k_\mathrm{s} } \text{ d }z\right) . \end{aligned}$$
(34)

where \(a_\mathrm{vg}\) is vegetation albedo. Thus, for a level \(z\),

$$\begin{aligned} R_{\mathrm{s}\uparrow } +R_{\mathrm{s}\downarrow }&= a_{0} (1-a_\mathrm{vg} )R_\mathrm{sh} \exp \left( -\int \limits _0^h {k_\mathrm{s} \text{ d }z}\right) \cdot \exp \left( -\int \limits _0^z {k_\mathrm{s} \text{ d }z}\right) \nonumber \\&+\,(1-a_\mathrm{vg} )R_\mathrm{sh} \exp \left( -\int \limits _z^h {k_\mathrm{s} \text{ d }z}\right) \end{aligned}$$
(35)

or

$$\begin{aligned} R_{\mathrm{s}\uparrow } +R_{\mathrm{s}\downarrow } =(1 - a_\mathrm{vg})R_\mathrm{sh} \left[ {a_{0} \exp \left( -{\int \limits _{0}^{h}} {k_\mathrm{s} dz} -\int \limits _0^z {k_\mathrm{s} \text{ d }z}\right) +\exp \left( -{\int \limits _{z}^{h}} {k_\mathrm{s} \text{ d }z}\right) } \right] . \end{aligned}$$
(36)

where \(a_0\) is surface albedo. Suppose the atmospheric longwave radiation at the top of the canopy is \(R_\mathrm{lh}\) and the ground surface temperature is \(T_{0}\). Further, suppose the canopy layer between \(z\) and \(h\) is divided into \(I_\mathrm{a}\) layers, and the vegetation layer between 0 and \(z\) is divided into \(I_\mathrm{b}\) layers, each of \(\delta \)z thick (Fig. 12). Then

$$\begin{aligned} R_{\mathrm{s}\uparrow } +R_{\mathrm{s}\downarrow }&= \varepsilon \sigma T_0^4 \exp \left( -\int \limits _0^z {k_\mathrm{l} \text{ d }z} \right) +R_\mathrm{lh} \exp \left( -\int \limits _z^h {k_\mathrm{l} \text{ d }z}\right) \nonumber \\&+\sum _{i=1}^{I_\mathrm{b}} {r(z_i )}\exp \left( -\int \limits _{zi}^z {k_\mathrm{l} \text{ d }z}\right) +\sum _{i=1}^{I_\mathrm{a} } {r(z_i)}\exp \left( -\int \limits _z^{z_i } {k_\mathrm{l} \text{ d }z}\right) \end{aligned}$$
(37)

with

$$\begin{aligned} r(z_i )=\frac{1}{2}\alpha _{t} (z_i )\varepsilon \sigma {T_\mathrm{c}^{4}} (z_{i} )\delta z \end{aligned}$$
(38)

where \(\alpha _\mathrm{t} (z_i )\)is the vegetation area density at level \(z_{i}\).

Fig. 12
figure 12

Schematic illustration of radiation transfer through vegetation canopy

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shao, Y., Liu, S., Schween, J.H. et al. Large-Eddy Atmosphere–Land-Surface Modelling over Heterogeneous Surfaces: Model Development and Comparison with Measurements. Boundary-Layer Meteorol 148, 333–356 (2013). https://doi.org/10.1007/s10546-013-9823-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10546-013-9823-0

Keywords

Navigation