S. C. Pryor

Comparison of Wake Model Simulations with Offshore Wind Turbine Wake Profiles Measured by Sodar

Abstract This paper gives an evaluation of most of the commonly used models for predicting wind speed decrease (wake) downstream of a wind turbine. The evaluation is based on six experiments where free-stream and wake wind speed profiles were measured using a ship-mounted sodar at a small offshore wind farm. The experiments were conducted at varying distances between 1.7 and 7.4 rotor diameters downstream of the wind turbine. Evaluation of the models compares the predicted and observed velocity deficits at hub height. A new method of evaluation based on determining the cumulative momentum deficit over the profiles is described. Despite the apparent simplicity of the experiments, the models give a wide range of predictions. Overall, it is not possible to establish any of the models as having individually superior performance with respect to the measurements.

Quantifying the Impact of Wind Turbine Wakes on Power Output at Offshore Wind Farms

Abstract There is an urgent need to develop and optimize tools for designing large wind farm arrays for deployment offshore. This research is focused on improving the understanding of, and modeling of, wind turbine wakes in order to make more accurate power output predictions for large offshore wind farms. Detailed data ensembles of power losses due to wakes at the large wind farms at Nysted and Horns Rev are presented and analyzed. Differences in turbine spacing (10.5 versus 7 rotor diameters) are not differentiable in wake-related power losses from the two wind farms. This is partly due to the high variability in the data despite careful data screening. A number of ensemble averages are simulated with a range of wind farm and computational fluid dynamics models and compared to observed wake losses. All models were able to capture wake width to some degree, and some models also captured the decrease of power output moving through the wind farm. Root-mean-square errors indicate a generally better model pe...

A review of measurement and modelling results of particle atmosphere–surface exchange

Atmosphere–surface exchange represents one mechanism by which atmospheric particle mass and number size distributions are modified. Deposition velocities (vd) exhibit a pronounced dependence on surface type, due in part to turbulence structure (as manifest in friction velocity), with minima of approximately 0.01 and 0.2 cm s-1 over grasslands and 0.1–1 cm s-1 over forests. However, as noted over 20 yr ago, observations over forests generally do not support the pronounced minimum of deposition velocity (vd) for particle diameters of 0.1–2 μm as manifest in theoretical predictions. Closer agreement between models and observations is found over less-rough surfaces though those data also imply substantially higher surface collection efficiencies than were originally proposed and are manifest in current models. We review theorized dependencies for particle fluxes, describe and critique model approaches and innovations in experimental approaches, and synthesize common conclusions of experimental and modelling studies. We end by proposing a number of research avenues that should be pursued in to facilitate further insights and development of improved numerical models of atmospheric particles.

Can Satellite Sampling of Offshore Wind Speeds Realistically Represent Wind Speed Distributions

Wind speeds over the oceans are required for a range of applications but are difficult to obtain through in situ methods. Hence, remote sensing tools, which also offer the possibility of describing spatial variability, represent an attractive proposition. However, the uncertainties inherent in application of current remote sensing methodologies have yet to be fully quantified. Aside from known issues regarding absolute accuracy and precision, there are a number of biases inherent in remote retrieval of wind speeds using satellite-borne instrumentation that lead to overestimation of the wind resource and are demonstrated here to be of sufficient magnitude to merit further consideration. As an interim measure, error bounds are proposed for the wind speed probability distribution parameters, which may be applied to sparse datasets such as those likely to be obtained from satelliteborne instrumentation.

Assessing climate change impacts on the near-term stability of the wind energy resource over the United States.

The energy sector comprises approximately two-thirds of global total greenhouse gas emissions. For this and other reasons, renewable energy resources including wind power are being increasingly harnessed to provide electricity generation potential with negligible emissions of carbon dioxide. The wind energy resource is naturally a function of the climate system because the “fuel” is the incident wind speed and thus is determined by the atmospheric circulation. Some recent articles have reported historical declines in measured near-surface wind speeds, leading some to question the continued viability of the wind energy industry. Here we briefly articulate the challenges inherent in accurately quantifying and attributing historical tendencies and making robust projections of likely future wind resources. We then analyze simulations from the current generation of regional climate models and show, at least for the next 50 years, the wind resource in the regions of greatest wind energy penetration will not move beyond the historical envelope of variability. Thus this work suggests that the wind energy industry can, and will, continue to make a contribution to electricity provision in these regions for at least the next several decades.

Addendum to “Wind speed trends over the contiguous United States”

[1] An earlier paper (Pryor et al., 2009) reports linear trends for annual percentiles of 10 m wind speeds from across the United States based on ordinary linear regression applied without consideration of temporal autocorrelation. Herein we show significant temporal autocorrelation in annual metrics from approximately half of all surface and upper air wind speed time series and present analyses that indicate at least some fraction of the temporal autocorrelation at the annual time scale may be due to the influence of persistent low-frequency climate modes as manifest in teleconnection indices. Treatment of the temporal autocorrelation slightly reduces the number of stations for which linear trends in10 m wind speeds are deemed significant but does not alter the trend magnitudes relative to those presented by Pryor et al. (2009). Analyses conducted accounting for the autocorrelation indicate 55% of annual 50th percentile 10 m wind speed time series, and 45% of 90th percentile annual 10 m wind speed time series derived from the National Climate Data Center DS3505 data set exhibit significant downward trends over the period 1973-2005. These trends are consistent with previously reported declines in pan evaporation but are not present in 10 m wind speeds from reanalysis products or upper air wind speeds from the radiosonde network.

Can Satellite Sampling of Offshore Wind Speeds Realistically Represent Wind Speed Distributions? Part II: Quantifying Uncertainties Associated with Distribution Fitting Methods

Abstract Remote sensing tools represent an attractive proposition for measuring wind speeds over the oceans because, in principle, they also offer a mechanism for determining the spatial variability of flow. Presented here is the continuation of research focused on the uncertainties and biases currently present in these data and quantification of the number of independent observations (scenes) required to characterize various parameters of the probability distribution of wind speeds. Theoretical and empirical estimates are derived of the critical number of independent observations (wind speeds derived from analysis of remotely sensed scenes) required to obtain probability distribution parameters with an uncertainty of ±10% and a confidence level of 90% under the assumption of independent samples, and it is found that approximately 250 independent observations are required to fit the Weibull distribution parameters. Also presented is an evaluation of Weibull fitting methods and determination of the fitting...

Human exposure to outdoor air pollution (IUPAC Technical Report)

Human exposure to outdoor air pollution is believed to cause severe health effects, especially in urban areas where pollution levels often are high, because of the poor dispersion conditions and high density of pollution sources. Many factors influence human health, and a good assessment of human air pollution exposure is, therefore, crucial for a proper determination of possible links between air pollution and health effects. Assessment of human exposure is, however, not straightforward, and this is the background for the present paper, which recommends how to carry out such assessments. Assessment of human exposure to air pollution may be carried out by use of: categorical classification, application of biomarkers, analysis of air pollution data from routine monitoring networks, personal portable exposure monitors, or application of mathematical air pollution exposure models. The categorical classification is a crude indirect method based on indicators of exposure such as type of residence, type of job, presence of indoor sources, etc. Categorical classification is generally inadequate for application in air pollution epidemiology. Biomarkers can be a strong instrument in assessment of health effects and provide information about air pollution exposure and dose. Use of biomarkers is, therefore, particularly useful when applied in combination with exposure assessment through one of the methods 3 to 5. The main focus of this paper is on these three methods for determination of human air pollution exposure. The optimal solution is clearly a combination of methods 2 to 5, but the available resources often set a limit to how far the assessment is carried out, and the choice of strategy will, therefore, often be very important for the outcome of the final study. This paper describes how these approaches may be applied and outlines advantages and disadvantages of the approaches used individually and in combination. Furthermore, some examples of specific applications in Denmark and the Netherlands are given for illustration.

Ten Years of Meteorological Measurements for Offshore Wind Farms

Riso has been monitoring wind resources and power output from offshore wind farms since 1993. A considerable degree of expertise has been developed in optimizing measurements and in using these databases to develop and validate models for offshore environments. This paper describes the evolution of monitoring strategies to a fully automated satellite based retrieval that provides near-real time access to offshore data, even at remote stand-alone masts. An overview of wind speed and turbulence at offshore sites in Denmark is given. Finally, three methods of generating long-term wind resource estimates from short-term measurements are outlined.

Hebdomadal and diurnal cycles in ozone time series from the Lower Fraser Valley, B.C.

Abstract The variability of tropospheric ozone concentrations on diurnal and hebdomadal time scales provides information regarding the processes of ozone formation, destruction and transport. This paper presents the statistics of ozone concentrations at 12 sites in the Lower Fraser Valley, B.C. Canada, with particular emphasis upon these time scales. Using a range of statistical techniques, the presence of hebdomadal cycles in the summertime ozone and oxides of nitrogen (nitrogen dioxide and nitric oxide) time series is demonstrated. Ozone concentrations at all sites are found to vary by day of the week and to be uniformly higher on weekends than mid-week. Conversely, oxides of nitrogen concentrations are typically, but not uniformly, higher mid-week than during weekends. The magnitude of the hebdomadal cycles of ozone and oxides of nitrogen (weekend-mid-week differences in concentration divided by the mean summertime concentration) is spatially variable and apparently has increased over the period 1984–1991. The methodology of Lefohn and Shadwick (1991) is used to show that at five sites in the LFV the form of the summertime average diurnal profiles of ozone and oxides of nitrogen on weekends and mid-week also changed between 1984 and 1991. The spatial and temporal variability of hebdomadal and diurnal ozone cycles are interpreted in the context of precursor emissions variability.