Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data

Tian, Xin; Li, Z.Y.; Tol, Christiaan van der; Su, Zhongbo; Li, X.; He, Qibin; Bao, Yan; Chen, E.X.; Li, L.H.

doi:10.1016/j.rse.2011.04.033

Cited by 43 publications

(32 citation statements)

References 38 publications

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“…Some work on characterizing profileamenable roughness over forest (e.g., Bosveld, 1997;Tian et al, 2011;Boudreault et al, 2015) implies that z 0 over forest is larger than what has been typically assigned in wind resource assessment (i.e., z 0 > 1, not z 0 1), despite such underestimates being used for decades in the wind industry (Troen and Petersen, 1989;Mortensen et al, 2001;Emeis, 2013;Landberg, 2016). We now see an explanation for this looking at Fig.…”

Section: Applications and Implicationsmentioning

confidence: 99%

Statistical characterization of roughness uncertainty and impact on wind resource estimation

Kelly¹,

Jørgensen²

2017

Wind Energ. Sci.

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Abstract. In this work we relate uncertainty in background roughness length (z 0 ) to uncertainty in wind speeds, where the latter are predicted at a wind farm location based on wind statistics observed at a different site. Sensitivity of predicted winds to roughness is derived analytically for the industry-standard European Wind Atlas method, which is based on the geostrophic drag law. We statistically consider roughness and its corresponding uncertainty, in terms of both z 0 derived from measured wind speeds as well as that chosen in practice by wind engineers. We show the combined effect of roughness uncertainty arising from differing wind-observation and turbine-prediction sites; this is done for the case of roughness bias as well as for the general case. For estimation of uncertainty in annual energy production (AEP), we also develop a generalized analytical turbine power curve, from which we derive a relation between mean wind speed and AEP. Following our developments, we provide guidance on approximate roughness uncertainty magnitudes to be expected in industry practice, and we also find that sites with larger background roughness incur relatively larger uncertainties.

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Section: Applications and Implicationsmentioning

confidence: 99%

Statistical characterization of roughness uncertainty and impact on wind resource estimation

Kelly¹,

Jørgensen²

2017

Wind Energ. Sci.

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“…Thus, it poses a challenge to scale those up in a model grid cell (Schaudt and Dickinson, 2000) in atmospheric transport models. Using remote sensing, robust scaling of these parameter values is achieved, which could be used to acquire representative values in a model grid cell (Tian et al, 2011). However, addressing the issue of a model's structural uncertainty in a detailed manner was outside the scope of this paper.…”

Section: Discussionmentioning

confidence: 99%

“…As opposed to the local sensitivity analysis (e.g., OAT approach), the Sobol' method is a global sensitivity approach, in which a set of input parameters of a model can be varied simultaneously over their entire parameter value space to identify their relative contributions to the overall model output variance. The Sobol' method has been applied in environmental modeling applications (Tang et al, 2007;Pappenberger et al, 2008;van Werkhoven et al, 2008;Yang, 2011) but has not yet been applied in dry deposition modeling research. Given that in most of the dry deposition parameterizations, model inputs can span a wide range within their physical realms, the application of a global sensitivity analysis used in this study should be viewed as a critical step toward the understanding of different subphysical processes of particle deposition.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…We note that the evaluation of dry deposition parameterizations with field measurements is very limited and not up to date. Van Aalst (1986) evaluated the performance of six dry deposition parameterizations against field measurements and reported large discrepancies in terms of the modeled deposition velocities. He reported that over water surfaces the modeled deposition velocities for 1.0 µm particles by the Williams (1982) scheme were factors of 10 to 50 higher than those predicted by the Sehmel and Hodgson (1978) scheme.…”

Section: Introductionmentioning

confidence: 99%

“…The largest discrepancy was observed for particle sizes of 0.3 to approximately 5.0 µm. Studies also suggest that in many dry deposition parameterizations, the largest uncertainty exists for 0.1-1.0 µm particles because of the differing treatments of some key particle deposition processes such as Brownian diffusion (Van Aalst, 1986;Petroff and Zhang, 2010;Zhang and Shao, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of five dry particle deposition parameterizations for incorporation into atmospheric transport models

Khan

Perlinger

2017

Geosci. Model Dev.

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Abstract. Despite considerable effort to develop mechanistic dry particle deposition parameterizations for atmospheric transport models, current knowledge has been inadequate to propose quantitative measures of the relative performance of available parameterizations. In this study, we evaluated the performance of five dry particle deposition parameterizations developed by Zhang et al. (2001) ("Z01"), Petroff and Zhang (2010) ("PZ10"), Kouznetsov and Sofiev (2012) ("KS12"), Zhang and He (2014) ("ZH14"), and Zhang and Shao (2014) ("ZS14"), respectively. The evaluation was performed in three dimensions: model ability to reproduce observed deposition velocities, V d (accuracy); the influence of imprecision in input parameter values on the modeled V d (uncertainty); and identification of the most influential parameter(s) (sensitivity). The accuracy of the modeled V d was evaluated using observations obtained from five land use categories (LUCs): grass, coniferous and deciduous forests, natural water, and ice/snow. To ascertain the uncertainty in modeled V d , and quantify the influence of imprecision in key model input parameters, a Monte Carlo uncertainty analysis was performed. The Sobol' sensitivity analysis was conducted with the objective to determine the parameter ranking from the most to the least influential. Comparing the normalized mean bias factors (indicators of accuracy), we find that the ZH14 parameterization is the most accurate for all LUCs except for coniferous forest, for which it is second most accurate. From Monte Carlo simulations, the estimated mean normalized uncertainties in the modeled V d obtained for seven particle sizes (ranging from 0.005 to 2.5 µm) for the five LUCs are 17, 12, 13, 16, and 27 % for the Z01, PZ10, KS12, ZH14, and ZS14 parameterizations, respectively. From the Sobol' sensitivity results, we suggest that the parameter rankings vary by particle size and LUC for a given parameterization. Overall, for d p = 0.001 to 1.0 µm, friction velocity was one of the three most influential parameters in all parameterizations. For giant particles (d p = 10 µm), relative humidity was the most influential parameter. Because it is the least complex of the five parameterizations, and it has the greatest accuracy and least uncertainty, we propose that the ZH14 parameterization is currently superior for incorporation into atmospheric transport models.

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Implementation of spaceborne lidar‐retrieved canopy height in the WRF model

Lee

Hong

2016

JGR Atmospheres

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Canopy height is closely related to biomass and aerodynamic properties, which regulate turbulent transfer of energy and mass at the soil‐vegetation‐atmosphere continuum. However, this key information has been prescribed as a constant value in a fixed plant functional type in atmospheric models. This paper is the first to report impacts of using realistic forest canopy height, retrieved from spaceborne lidar, on regional climate simulation by using the canopy height data in the Weather Research and Forecasting (WRF) model's land surface model. Numerical simulations were conducted over the Amazon Basin during summer season. Over this region, the lidar‐retrieved canopy heights were higher than the default values used in the WRF, which are dependent only on plant functional type. By modifying roughness length and zero‐plane displacement height, the change of canopy height resulted in changes in surface energy balance by regulating aerodynamic conductances and vertical temperature gradient, thus modifying the lifting condensation level and equivalent potential temperature in the atmospheric boundary layer. Our analysis also showed that the WRF model better reproduced the observed precipitation when lidar‐retrieved canopy height was used over the Amazon Basin.

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Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data

Cited by 43 publications

References 38 publications

Statistical characterization of roughness uncertainty and impact on wind resource estimation

Statistical characterization of roughness uncertainty and impact on wind resource estimation

Evaluation of five dry particle deposition parameterizations for incorporation into atmospheric transport models

Implementation of spaceborne lidar‐retrieved canopy height in the WRF model

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