Stable Boundary Layer Depth from High-Resolution Measurements of the Mean Wind Profile

Pichugina, Yelena L.; Banta, Robert M.

doi:10.1175/2009jamc2168.1

Cited by 44 publications

(41 citation statements)

References 28 publications

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“…The typical nighttime structure of the LLJ (Fig. 4B) with a mean stable boundary layer height of 40 m (12-124 m, 5th-95th percentile, respectively) from JuneSeptember 2001 in the WRF control mean is consistent with height observations of about 50-350 m in southeastern Kansas during October 1999 (30). Observed LLJ maxima at about 100 m after sunset with an increase in height to about 225 m over the course of the night were also observed for this region on October 25, 1999 (30).…”

Section: Resultssupporting

confidence: 78%

Two methods for estimating limits to large-scale wind power generation

Miller

Brunsell

Mechem

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Wind turbines remove kinetic energy from the atmospheric flow, which reduces wind speeds and limits generation rates of large wind farms. These interactions can be approximated using a vertical kinetic energy (VKE) flux method, which predicts that the maximum power generation potential is 26% of the instantaneous downward transport of kinetic energy using the preturbine climatology. We compare the energy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped with a wind turbine parameterization over a 10 5 km 2 region in the central United States. The WRF simulations yield a maximum generation of 1.1 W e ·m −2 , whereas the VKE method predicts the time series while underestimating the maximum generation rate by about 50%. Because VKE derives the generation limit from the preturbine climatology, potential changes in the vertical kinetic energy flux from the free atmosphere are not considered. Such changes are important at night when WRF estimates are about twice the VKE value because wind turbines interact with the decoupled nocturnal low-level jet in this region. Daytime estimates agree better to 20% because the wind turbines induce comparatively small changes to the downward kinetic energy flux. This combination of downward transport limits and wind speed reductions explains why large-scale wind power generation in windy regions is limited to about 1 W e ·m −2 , with VKE capturing this combination in a comparatively simple way.generation limits | turbine-atmosphere interactions | wind resource | kinetic energy flux | extraction limits W ind power has progressed from being a minor source of electricity to a technology that accounted for 3.3% of electricity generation in the United States and 2.9% globally in 2011 (1, 2). Combined with an increase in quantity, the average US wind turbine also changed from 2001 to 2012; hub height increased by 40%, rotor-swept area increased by 180%, and rated capacity increased by 100% (2). Likely a combination of both the above-noted technological innovations and improved siting, the per-turbine capacity factor, the ratio of the electricity generation rate (MW e ) to the rated capacity (MW i ), increased globally from 17% in 2001 to 29% in 2012 (1, 2), making a recently deployed wind farm likely to generate about 70% more electricity from the same installed capacity.Combining climate datasets with these observed trends of greater-rated capacities and capacity factors, several academic and government research studies estimate large-scale wind power electricity generation rates of up to 7 W e ·m −2 (3-7). However, a growing body of research suggests that as larger wind farms cover more of the Earth's surface, the limits of atmospheric kinetic energy generation, downward transport, and extraction by wind turbines limits large-scale electricity generation rates in windy regions to about 1.0 W e ·m −2 (8-14). Ideally, these inherent atmospheric limitations to generating electricity with wind power could be considered without scenario-and technol...

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Section: Resultssupporting

confidence: 78%

Two methods for estimating limits to large-scale wind power generation

Miller

Brunsell

Mechem

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…The NBL depths are close between 0000 and 0700 LST, but differ significantly between about 2000 and 2400 LST; sunset is at around 1945 LST in July and 1915 LST in August. For these cases, the NBL depths using f = 0.1 and f = 0.05 have been compared to subjective estimates of NBL depths based on the lidar wind-speed profiles (e.g., Banta et al 2003;Pichugina and Banta 2010). Comparing results from all profiles during the study period, the value of f = 0.1 produced a better agreement with the subjectively determined NBL depths; therefore we selected f = 0.1 for this dataset.…”

Section: Defining the Nbl Depth Using Fractional σ 2 Wmentioning

confidence: 99%

Estimate of Boundary-Layer Depth Over Beijing, China, Using Doppler Lidar Data During SURF-2015

Huang

Gao

Miao

et al. 2016

Boundary-Layer Meteorol

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Planetary boundary-layer (PBL) structure was investigated using observations from a Doppler lidar and the 325-m Institute of Atmospheric Physics (IAP) meteorological tower in the centre of Beijing during the summer 2015 Study of Urban-impacts on Rainfall and Fog/haze (SURF-2015) field campaign. Using six fair-weather days of lidar and tower data under clear to cloudy skies, we evaluate the ability of the Doppler lidar to probe the urban boundary-layer structure, and then propose a composite method for estimating the diurnal cycle of the PBL depth using the Doppler lidar. For the convective boundary layer (CBL), a threshold method using vertical velocity variance (σ 2 w > 0.1 m 2 s −2 ) is used, since it provides more reliable CBL depths than a conventional maximum wind-shear method. The nocturnal boundary-layer (NBL) depth is defined as the height at which σ 2 w decreases to 10 % of its near-surface maximum minus a background variance. The PBL depths determined by combining these methods have average values ranging from ≈270 to ≈1500 m for the six days, with the greatest maximum depths associated with clear skies. Release of stored and anthropogenic heat contributes to the maintenance of turbulence until late evening, keeping the NBL near-neutral and deeper at night than would be expected over a natural surface. The NBL typically becomes more shallow with time, but grows in the presence of low-level nocturnal jets. While current results are promising, data over a broader range of conditions are needed to fully develop our PBL-depth algorithms.

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“…For two days during LAFE, the daily data-acquisition operations were expanded to include 565 the evening transition period and to sample the nocturnal stable boundary layer and associated 566 low-level jets (e.g., Banta 2008, Pichugina et al 2010. Figure 10 demonstrates the excellent 567 information contents of the 6-point scanning pointing mode of the UHOH DLID for providing 568 the horizontal wind, TKE, and momentum flux profiles continuously during day and night.…”

mentioning

confidence: 99%

A New Research Approach for Observing and Characterizing Land–Atmosphere Feedback

Wulfmeyer

Turner

Baker

et al. 2018

Bulletin of the American Meteorological Society

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38Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely 39 correspond to the limited capability of current earth system models to capture and simulate 40 land-atmosphere feedback. To facilitate its realistic simulation in next generation models, an 41 improved process understanding of the related complex interactions is essential. To this end, 42 accurate 3D observations of key variables in the land-atmosphere (L-A) system with high 43 vertical and temporal resolution from the surface to the free troposphere are indispensable. 44Recently, we developed a synergy of innovative ground-based, scanning active remote sens-45 ing systems for 2D to 3D measurements of wind, temperature, and water vapor from the sur-46 face to the lower troposphere that is able to provide comprehensive data sets for characteriz-47 Motivation 71The land-atmosphere (L-A) system includes the soil, the land cover such as vegetation, and 72 the overlying atmosphere. The interaction of variables, e.g. related to the water and energy 73 budgets, results in characteristic natural variabilities and regimes as well as their changes due 74 to anthropogenic influences. The planetary boundary layer (PBL) is part of the L-A system 75 and represents the interface between the land surface and the free troposphere. Through an 76 exchange of momentum, energy and water, the dynamics, the thermodynamic structure, and 77 the evolution of the PBL affect the formation of shallow and deep clouds, convection initia-78 tion, and thus precipitation (Sherwood et al. 2010, Behrendt et al. 2011, Santanello et al. 79 2011, van den Hurk et al. 2011. One of the most complex feedback 80 loops is between soil moisture and precipitation (Seneviratne et al. 2010, Guillod et al. 2015, 81 Santanello et al. 2017). Precipitation can be influenced directly by the surface fluxes (Ek and 82Holtslag 2004), and indirectly via PBL dynamics and mesoscale circulations (Taylor et al. 83 2012). 84The PBL state and its evolution are strongly influenced by non-linear feedbacks in the L-A 85 system. These are due to two-way interactions between radiation, soil, vegetation, and atmos-86 pheric variables, which result in the diurnal cycles of surface fluxes. The feedbacks are rele-87 vant from local to global scales (Mahmood et al. 2013, Stéfanon et al. 2014, and their 88 strength varies both regionally and seasonally in dependence of soil moisture, advection, and 89 climate regimes. In locations where these feedbacks play an important role, it is likely that 90 they will become even more important due to anthropogenic climate change (Dirmeyer et al. 91 2012). Thus, to improve our understanding of the state and the evolution of the L-A system as 92 well as the dynamics and thermodynamics of the PBL, it is critical that feedbacks and fluxes 93 between the different components, including entrainment at the top of the PBL, are well char-94 4 acterized and appropriately represented in weather, climate, and earth system models (e.g., 95 Se...

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Stable Boundary Layer Depth from High-Resolution Measurements of the Mean Wind Profile

Cited by 44 publications

References 28 publications

Two methods for estimating limits to large-scale wind power generation

Two methods for estimating limits to large-scale wind power generation

Estimate of Boundary-Layer Depth Over Beijing, China, Using Doppler Lidar Data During SURF-2015

A New Research Approach for Observing and Characterizing Land–Atmosphere Feedback

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