Single-Column Model Intercomparison for a Stably Stratified Atmospheric Boundary Layer

Cuxart, Joan; Holtslag, A.A.M.; Beare, Robert J.; Bazile, Eric; Beljaars, Anton; Cheng, Anning; Conangla, L.; Ek, Michael; Freedman, F.; Hamdi, Rafiq; Kerstein, Allan R.; Kitagawa, H.; Lenderink, Geert; Lewellen, D. C.; Mailhot, Jocelyn; Mauritsen, Thorsten; Perov, Veniamin; Schayes, G.; Steeneveld, G.J.; Svensson, Gunilla; Taylor, Peter A.; Weng, Wensong; Wunsch, Scott; Xu, Kuan‐Man

doi:10.1007/s10546-005-3780-1

Cited by 312 publications

(381 citation statements)

References 40 publications

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“…A possible explanation is that, to avoid numerical instability potentially resulting from thermal decoupling of the surface and atmosphere, operational models tend to overestimate the level of background turbulence in very stable conditions. This is a common problem for climate models as well (Tjernström et al, 2005), and was also revealed in the Global Energy and Water Cycle Experiment (GEWEX) Atmospheric Boundary Layer Study (GABLS) experiments (Cuxart et al, 2006;Steeneveld et al, 2006;Svensson et al, 2011).…”

Section: Discussionmentioning

confidence: 81%

Evaluation of NWP results for wintertime nocturnal boundary‐layer temperatures over Europe and Finland

Atlaskin

Vihma

2012

Quart J Royal Meteoro Soc

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The results demonstrated a T2m bias increasing with decreasing temperature and strengthening temperature inversion. When a strong temperature inversion was observed in Sodankylä, the models underestimated it, whereas in nearneutral conditions the stratification was overestimated. Comparison of observed and modelled 3 h temperature tendencies showed that the T2m tendency in the models was on average only 17-20% of the observed one. The warm bias in T2m forecast in Sodankylä during periods of observed temperature inversion partly resulted from a warm bias in the initial conditions. This was due to problems in data assimilation in IFS and HIRLAM, in initialization in AROME, and in either or both procedures in GFS. In particular, the IFS data assimilation increased the T2m bias. Evaluation of modelled T2m against grid-averaged T2m observed at Helsinki Testbed demonstrated that the T2m model error dominated over the spatial variability of observed T2m. This suggests that over an almost flat terrain horizontal resolution is not a major factor for the accuracy of T2m forecast at low T2m typically associated with temperature inversions.

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Section: Discussionmentioning

confidence: 81%

Evaluation of NWP results for wintertime nocturnal boundary‐layer temperatures over Europe and Finland

Atlaskin

Vihma

2012

Quart J Royal Meteoro Soc

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“…gradient Richardson number or Obukhov length, see Delage and Girard (1992), Delage (1997), Mahrt and Vickers (2003), Han et al (2009)]. 1.5-order models formulate diffusivity as a function of turbulent kinetic energy (e = 1 2 (u 2 + v 2 + w 2 )) and/or other variances of turbulent fluctuating components in addition to mixing length and stability (Mellor and Yamada 1974;Bougeault and Lacarrere 1989;Bélair et al 1999;Cuxart et al 2006). Full second-and third-order models may eliminate the need for momentum and heat diffusivity parametrization because they formulate and solve all second-and third-moment equations, respectively.…”

Section: Downgradient Transportmentioning

confidence: 99%

“…by vertical diffusion of momentum, heat, moisture, and other atmospheric constituents above the surface layer (Cuxart et al 2006). In this approach all relevant equations are simplified by Reynolds averaging, where each variable of interest is decomposed into a mean and a turbulent fluctuating part (X = X + X ).…”

Section: Introductionmentioning

confidence: 99%

Characterization and Parametrization of Reynolds Stress and Turbulent Heat Flux in the Stably-Stratified Lower Arctic Troposphere Using Aircraft Measurements

Aliabadi

Staebler

Liu

et al. 2016

Boundary-Layer Meteorol

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Aircraft measurements are used to characterize properties of clear-air turbulence in the lower Arctic troposphere. For typical vertical resolutions in general circulation models, there is evidence for both downgradient and countergradient vertical turbulent transport of momentum and heat in the mostly statically stable conditions within both the boundary layer and the free troposphere. Countergradient transport is enhanced in the free troposphere compared to the boundary layer. Three parametrizations are suggested to formulate the turbulent heat flux and are evaluated using the observations. The parametrization that accounts for the anisotropic nature of turbulence and buoyancy flux predicts both observed downgradient and countergradient transport of heat more accurately than those that do not. The inverse turbulent Prandtl number is found to only weakly decrease with increasing gradient Richardson number in a statistically significant way, but with large scatter in the data. The suggested parametrizations can potentially improve the performance of regional and global atmospheric models.

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“…Even at the top of the rotor, where we expect fetch to matter most, reduction of the patch area, from 56.3 2 to 20 2 km 2 , yields a wind-speed increase that is only 22 % smaller than the original result. Some caution is advised, however, as studies have found the WRF model to produce unrealistically large vertical mixing (e.g., Cuxart and Holtslag 2006;Storm et al 2010), which may explain some of the insensitivity of rotor-layer wind speed to patch size. …”

Section: Impacts Across the Wind Farm In Various Stability Regimesmentioning

confidence: 99%

Could Crop Height Affect the Wind Resource at Agriculturally Productive Wind Farm Sites?

Vanderwende

Lundquist

2015

Boundary-Layer Meteorol

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The collocation of cropland and wind turbines in the US Midwest region introduces complex meteorological interactions that could influence both agriculture and wind-power production. Crop management practices may affect the wind resource through alterations of land-surface properties. We use the weather research and forecasting (WRF) model to estimate the impact of crop height variations on the wind resource in the presence of a large turbine array. A hypothetical wind farm consisting of 121 1.8-MW turbines is represented using the WRF model wind-farm parametrization. We represent the impact of selecting soybeans rather than maize by altering the aerodynamic roughness length in a region approximately 65 times larger than that occupied by the turbine array. Roughness lengths of 0.1 and 0.25 m represent the mature soy crop and a mature maize crop, respectively. In all but the most stable atmospheric conditions, statistically significant hub-height wind-speed increases and rotor-layer wind-shear reductions result from switching from maize to soybeans. Based on simulations for the entire month of August 2013, wind-farm energy output increases by 14 %, which would yield a significant monetary gain. Further investigation is required to determine the optimal size, shape, and crop height of the roughness modification to maximize the economic benefit and minimize the cost of such crop-management practices. These considerations must be balanced by other influences on crop choice such as soil requirements and commodity prices.

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Single-Column Model Intercomparison for a Stably Stratified Atmospheric Boundary Layer

Cited by 312 publications

References 40 publications

Evaluation of NWP results for wintertime nocturnal boundary‐layer temperatures over Europe and Finland

Evaluation of NWP results for wintertime nocturnal boundary‐layer temperatures over Europe and Finland

Characterization and Parametrization of Reynolds Stress and Turbulent Heat Flux in the Stably-Stratified Lower Arctic Troposphere Using Aircraft Measurements

Could Crop Height Affect the Wind Resource at Agriculturally Productive Wind Farm Sites?

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