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Impacts from current and future wind turbine (WT) deployments necessary to achieve 20% electricity from wind are analyzed using high resolution numerical simulations over the eastern USA. Theoretical scenarios for future deployments are based on repowering (i.e. replacing with higher capacity WTs) thus avoiding competition for land. Simulations for the contemporary climate and current WT deployments exhibit good agreement with observed electricity generation efficiency (gross capacity factors (CF) from simulations = 45–48%, while net CF for WT installed in 2016 = 42.5%). Under the scenario of quadrupled installed capacity there is a small decrease in system-wide efficiency as indicated by annual mean CF. This difference is approximately equal to that from the two simulation years and may reflect saturation of the wind resource in some areas. WT modify the local near-surface climate in the grid cells where they are deployed. The simulated impact on near-surface climate properties at both the regional and local scales does not increase with increasing WT installed capacity. Climate impacts from WT are modest compared to regional changes induced by historical changes in land cover and to the global temperature perturbation induced by use of coal to generate an equivalent amount of electricity.

The notion that the nature of a measurement is critical to its outcome is usually associated with quantum phenomena. In this paper, we show that the observed statistical properties are also a function of the measurement technique in the case of simple classical populations. In particular, the measured and intrinsic statistics of a single population may be different, while correlation and transfer of individuals between two populations may be hidden from the observer.

As wind turbine average hub-height (H) and rotor diameter (D) grow, it is assumed that the benefit derived from larger swept areas and higher wind speeds at higher altitudes will outweigh any increase in fatigue loading due to higher shear and manufacturing/installation costs. The impact of increasing wind turbine H and D on power production and the occurrence of extreme positive and negative shear is examined using high-resolution simulations with the Weather Research and Forecasting (WRF) model over Iowa. Three wind turbine scenarios are considered; S#1: H=83m, D=100m; S#2: H=100 m, D=100 m and S#3: H=100 m, D=133 m. Increasing H from 83 m to 100 m while maintaining D=100 m increases power by 16% relative to scenario 1. Increasing D to 133 m from 100 m with H=100 m doubles the power output compared to S#1. Extreme shear across the rotor plane (shear exponent α > 0.2 or α < 0) is frequently observed, but only modestly impacted by the changes in wind turbine dimensions. Thus, the increase in power output from increasing H and D to these levels seems to incur little penalty in terms of increased occurrence of high positive or negative shear.

Projected power output and wake extents are presented from new simulations with the Weather Research and Forecasting (WRF) model v4.2.2 for the large offshore wind energy lease areas along the U.S. east coast. These simulations assume nearly 2000 IEA 15 MW reference turbines are deployed with a spacing equal to the mean of smaller European offshore wind farms (7.7 rotor diameters). Results show marked differences across two wind farm parameterizations. Generally, the modified Fitch parameterization (wherein TKE generation by the rotor has been decreased) generates lower power production estimates, and more spatially extensive and deeper wind farm wakes than are manifest in output from the Explicit Wake Parameterization (EWP). For example, under conditions of moderate freestream wind speeds (∼ 4-10 ms −1 at hub-height) and turbulent kinetic energy (TKE ∼ 0.2 to 1 m 2 s −2 ), cumulative power output (summed over all 15 lease areas) is substantially greater (∼ 25% higher) in output from EWP than Fitch. These differences have real implications for power production and thus both expected revenues and grid integration. The cumulative power production and mean normalized wake extent also exhibit sensitivity to the order in which the overlapping inner domains are computed and the number of inner domains. This effect is smaller than differences from two wind farm parameterizations. Analyses focusing on the seven adjoining lease areas south of Massachusetts indicate differences in the two schemes are magnified over the largest offshore wind clusters (with expected installed capacity of > 10 GW and spatial extent of 3675 km 2 ).

High-resolution simulations with the Weather Research and Forecasting (WRF) model are analyzed to characterize the frequency, intensity, height, and duration of springtime low-level jets (LLJ) and their implications for wind energy resource assessment and planning in Iowa. The time evolution of short-duration LLJ is analyzed to understand wind behavior around LLJ events and to illustrate their importance for high-frequency (few hours) variability in wind speeds and rotor plane turbulent kinetic energy (TKE). During spring, the LLJ core height has a spatiotemporal mean value of 217 m, but the LLJ depth means it frequently intersects typical wind turbine rotor planes. Nearly one-quarter of LLJ exhibit a maximum within the height interval 50-150 m AGL. LLJ profiles are found to have higher mean wind speeds across typical wind turbine rotor planes than non-LLJ profiles and to exhibit lower values of TKE. LLJ occur under stable stratification (i.e. positive Richardson numbers) and are associated with low TKE and the occurrence of high vertical wind shear. The frequency and duration of LLJ exhibit geospatial variability across Iowa with highest values in the northeast of the state. Analyses of daytime and night-time LLJ indicate topographic variability is an important factor in the development of LLJ.

Improved quantification of the spatial extent and intensity of wind farm wakes is urgently needed given the rapid pace of expansion of installed capacity both on-and off-shore. We present and analyse long-term, high-resolution simulations of whole wind farm wakes conducted for real-world wind turbine installations performed with the two wind farm parameterizations (EWP and Fitch) designed for use with the Weather Research and Forecasting model. We document differences in the formulation of these two parameterizations and demonstrate their impact on simulated wind farm wakes. Divergence between the schemes in terms of wake spatial extent and magnitude is maximized under low/moderate turbulent kinetic energy (TKE < 0.3 m2s−2) and wind speeds between cut-in and rated (U » 4-12 ms−1). Thus, it is under those conditions that model predictions of the intensity/spatial extent of wind farm wakes are inferred to have highest uncertainty. A framework is introduced based on these simulations that can be used to aid planning for experiments such as AWAKEN. It could be used to identify where and when observational data would be most beneficial in differentiating relative skill of the two parameterizations and identifying areas where modifications to the schemes are necessary to improve fidelity.

High-resolution regional simulations of the downstream effects of wind turbine arrays are presented. The simulations are conducted with the Weather Research and Forecasting (WRF) model using two different wind turbine parameterizations for a domain centered on the highest density of current wind turbine deployments in the contiguous US. The simulations use actual wind turbine geolocations and turbine specifications (e.g. power and thrust curves). Resulting analyses indicate that for both WT parameterizations impacts on temperature, specific humidity, precipitation, sensible and latent heat fluxes from current wind turbine deployments are statistically significant only in summer, are of very small magnitude, and are highly localized. It is also shown that use of the relatively recently developed new explicit wake parameterization (EWP) results in faster recovery of full array wakes. This in turn leads to smaller climate impacts and reduced array-array interactions, which at a system-wide scale lead to higher summertime capacity factors (2-6% higher) than those from the more commonly applied 'Fitch' parameterization. Our research implies that further expansion of wind turbine deployments can likely be realized without causing substantial downstream impacts on weather and climate, or array-array interactions of a magnitude that would yield substantial decreases in capacity factors.

Large-area face-centred cubic photonic crystals are fabricated by the two-dimensional shear-alignment of stabilized 720 nm diameter PMMA spheres dispersed in an epoxy resin. The resulting crystal is made permanent by exposure to UV light, thus solidifying the epoxy resin. The technique is rapid and produces high quality, large-area crystals.

The lymphocyte genome is prone to many threats, including programmed mutation during differentiation 1 , antigen-driven proliferation and residency in diverse microenvironments. Here, after developing protocols for expansion of single-cell lymphocyte cultures, we sequenced whole genomes from 717 normal naive and memory B and T cells and haematopoietic stem cells. All lymphocyte subsets carried more point mutations and structural variants than haematopoietic stem cells, with higher burdens in memory cells than in naive cells, and with T cells accumulating mutations at a higher rate throughout life. Off-target effects of immunological diversification accounted for approximately half of the additional differentiation-associated mutations in lymphocytes. Memory B cells acquired, on average, 18 off-target mutations genome-wide for every on-target IGHV mutation during the germinal centre reaction. Structural variation was 16-fold higher in lymphocytes than in stem cells, with around 15% of deletions being attributable to off-target recombinase-activating gene activity. DNA damage from ultraviolet light exposure and other sporadic mutational processes generated hundreds to thousands of mutations in some memory cells. The mutation burden and signatures of normal B cells were broadly similar to those seen in many B-cell cancers, suggesting that malignant transformation of lymphocytes arises from the same mutational processes that are active across normal ontogeny. The mutational landscape of normal lymphocytes chronicles the off-target effects of programmed genome engineering during immunological diversification and the consequences of differentiation, proliferation and residency in diverse microenvironments. Sequencing of individual human lymphocyte clones shows that they are highly prone to mutations, with higher burdens in memory cells than in naive cells arising from mutational processes associated with differentiation and tissue residency.