Abstract

As a convective boundary layer over land decays in the late afternoon, the atmosphere responds to the release of turbulent stresses. For many years, this response has been presumed to take the form of an inertial oscillation, a horizontal circulation with a frequency equal to the local Coriolis frequency, though published documentation of inertial oscillations in the atmosphere has been rare. In fact, documentation of inertial oscillations has been more associated with frontal passages than with the evening transition of the atmospheric boundary layer.

A month of boundary layer wind profiler data from the Cooperative Atmosphere–Surface Exchange Study-1999 field program is analyzed here with the Hilbert–Huang transform (HHT), which allows analysis of intermittent, nonstationary, and amplitude-varying wave events. Inertial motions are found in this dataset, but neither the onset times of these inertial motions nor the preferred levels of occurrence are consistent with the evening-transition hypothesis. Rather, significant correlations of inertial motions with frontal passages are observed. The elliptical nature of the observed inertial motions is consistent with amplification by deformation frontogenesis.

The HHT is first demonstrated with a 5-day time series of temperature data to illustrate how the technique allows simultaneous identification of the stationary diurnal temperature cycle, as well as intermittent and nonstationary cooling events like frontal passages and density currents. The age of one density current is calculated from its dispersion characteristics, verifying that the density current in question results from rapid cooling near sunset.

Abstract

Because accurate modeling of atmospheric flows in urban environments requires sophisticated representation of complex urban geometries, much work has been devoted to treatment of the urban surface. However, the importance of the larger-scale flow impinging upon the urban complex to the flow, transport, and dispersion within it and downwind has received less attention. Building-resolving computational fluid dynamics (CFD) models are commonly employed to investigate interactions between the flow and three-dimensional structures that make up the urban environment; however, such models are typically forced with simplified boundary conditions that fail to include important regional-scale phenomena that can strongly influence the flow within the urban complex and downwind. This paper investigates the interaction of an important and frequently occurring regional-scale phenomenon, the nocturnal low-level jet (LLJ), with urban-scale turbulence and dispersion in Oklahoma City, Oklahoma, using data from the Joint Urban 2003 (JU2003) field experiment. Two simulations of nocturnal tracer release experiments from JU2003 using Lawrence Livermore National Laboratory’s Finite-Element Model in 3 Dimensions and Massively Parallelized (FEM3MP) CFD model yield differing levels of agreement with the observations in wind speed, turbulence kinetic energy (TKE), and concentration profiles in the urban wake, approximately 750 m downwind of the central business district. Profiles of several observed turbulence parameters at this location indicate characteristics of both bottom-up and top-down boundary layers during each of the experiments. These data are consistent with turbulence production due to at least two sources, the complex flow structures of the urban area and the region of strong vertical wind shear occurring beneath the LLJs present each night. Strong LLJs occurred each night, but their structures varied considerably, resulting in significant differences in the magnitudes of the turbulence parameters observed during the two experiments. Because FEM3MP was forced only with an upwind velocity profile that did not adequately represent the LLJ, the downward propagation of TKE observed during the experiments was absent from the simulations. As such, the differing levels of agreement between the simulations and observations during the two experiments can, in part, be explained by their exclusion of this important larger-scale influence. The ability of the Weather Research and Forecast Model (WRF) to simulate accurate velocity fields during each night was demonstrated, and the use of regional-scale simulation data was identified as a promising approach for representing the effects of important regional-scale phenomena such as the LLJ on urban-scale simulations.

Abstract

The validity of omitting stability considerations when simulating transport and dispersion in the urban environment is explored using observations from the Joint Urban 2003 field experiment and computational fluid dynamics simulations of that experiment. Four releases of sulfur hexafluoride, during two daytime and two nighttime intensive observing periods (IOPs), are simulated using the building-resolving computational fluid dynamics model called the Finite Element Model in 3-Dimensions and Massively Parallelized (FEM3MP) to solve the Reynolds-averaged Navier–Stokes equations with two options of turbulence parameterizations. One option omits stability effects but has a superior turbulence parameterization using a nonlinear eddy viscosity (NEV) approach, and the other considers buoyancy effects with a simple linear eddy viscosity approach for turbulence parameterization. Model performance metrics are calculated by comparison with observed winds and tracer data in the downtown area and with observed winds and turbulence kinetic energy (TKE) profiles at a location immediately downwind of the central business district in the area labeled as the urban shadow. Model predictions of winds, concentrations, profiles of wind speed, wind direction, and friction velocity are generally consistent with and compare reasonably well to the field observations. Simulations using the NEV turbulence parameterization generally exhibit better agreement with observations. To explore further the assumption of a neutrally stable atmosphere within the urban area, TKE budget profiles slightly downwind of the urban wake region in the urban shadow are examined. Dissipation and shear production are the largest terms that may be calculated directly. The advection of TKE is calculated as a residual; as would be expected downwind of an urban area, the advection of TKE produced within the urban area is a very large term. Buoyancy effects may be neglected in favor of advection, shear production, and dissipation. For three of the IOPs, buoyancy production may be neglected entirely; for one IOP, buoyancy production contributes approximately 25% of the total TKE at this location. For both nighttime releases, the contribution of buoyancy to the total TKE budget is always negligible though positive. Results from the simulations provide estimates of the average TKE values in the upwind, downtown, downtown shadow, and urban wake zones of the computational domain. These values suggest that building-induced turbulence can cause the average turbulence intensity in the urban area to increase by as much as 7 times average upwind values, explaining the minimal role of buoyant forcing in the downtown region. The downtown shadow exhibits an exponential decay in average TKE, whereas the distant downwind wake region approaches the average upwind values. For long-duration releases in downtown and downtown shadow areas, the assumption of neutral stability is valid because building-induced turbulence dominates the budget. However, farther downwind in the urban wake region, which is found to be approximately 1500 m beyond the perimeter of downtown Oklahoma City, Oklahoma, the levels of building-induced turbulence greatly subside, and therefore the assumption of neutral stability is less valid.

Abstract

To facilitate the optimization of turbine spacing at modern wind farms, computational simulations of wake effects must be validated through comparison with full-scale field measurements of wakes from utility-scale turbines operating in the real atmosphere. Scanning remote sensors are particularly well suited for this objective, as they can sample wind fields over large areas at high temporal and spatial resolutions. Although ground-based systems are useful, the vantage point from the nacelle is favorable in that scans can more consistently transect the central part of the wake. To the best of the authors’ knowledge, the work described here represents the first analysis in the published literature of a utility-scale wind turbine wake using nacelle-based long-range scanning lidar.

The results presented are of a field experiment conducted in the fall of 2011 at a wind farm in the western United States, quantifying wake attributes such as the velocity deficit, centerline location, and wake width. Notable findings include a high average velocity deficit, decreasing from 60% at a downwind distance x of 1.8 rotor diameters (D) to 40% at x = 6D, resulting from a low average wind speed and therefore a high average turbine thrust coefficient. Moreover, the wake width was measured to expand from 1.5D at x = 1.8D to 2.5D at x = 6D. Both the wake growth rate and the amplitude of wake meandering were observed to be greater for high ambient turbulence intensity and daytime conditions as compared to low turbulence and nocturnal conditions.

Abstract

Very little is known about the nature of turbulence in the transition zone of a synoptic-scale cold front, especially at the dissipative scales. Lacking this knowledge, accurate models of surface frontogenesis are compromised. To address this problem, high-frequency measurements from sonic and hot-wire anemometers are used to analyze the finescale turbulence in the atmospheric surface layer (ASL) within a cold front observed in the MICROFRONTS field experiment. To quantify the turbulence in the front, velocity spectra and dissipation rates are calculated as functions of time and stability in the ASL. The normalized first and second moments of the one-dimensional velocity spectrum conform to the scaling suggested by Kolmogorov's equilibrium hypotheses, even during the intense turbulence associated with the frontal passage. The spectra compare well with other data collected at high Re _λ in the ASL, but not as well with a recent model of the dissipative range of turbulence. Dissipation rate ϵ is calculated with one direct and two indirect techniques. The calculations from the different techniques compare well with one another and, when nondimensionalized, with a historical expression for dissipation rate as a function of ASL stability. The magnitude of the dissipation rate increases by an order of magnitude to a maximum value of ∼1.2 m² s⁻³ during the frontal passage compared to prefrontal values of ∼0.05 m² s⁻³; the latter is typical for a slightly stable nighttime boundary layer over land. These results can be used in assessing the effects of turbulence in traditional semigeostrophic models of frontal collapse. The dissipation rate calculations may be of particular use to modelers.

Abstract

The authors demonstrate the utility of k-means clustering for identifying relationships between winds at turbine heights and climate oscillations, thereby developing a method suited for predicting the impacts of climate change on wind resources. Fourteen years of data from an 80-m tower at the National Wind Technology Center (NWTC) in Colorado have been reduced to four dominant flow phenomena using k-means clustering. At this location, this method identifies two clusters of westerly inflow (strong and weak), another cluster of flow from the north, and one of flow from the south. Similar clusters are found for the data at all heights on the tower, and each follow distinct seasonal cycles. Time series of each cluster, as well as the mean wind speed at the NWTC, are retained for comparison with climate oscillations along with the local 500-hPa pressure gradient. The mean wind speed in the surface layer is strongly correlated with the local north–south pressure gradient. The frequency of strong westerly flow is also negatively correlated with the Niño-3.4 index, whereas weaker westerly winds are negatively correlated with the Pacific–North American pattern (PNA) and Arctic Oscillation (AO). Northerly winds at the NWTC did not strongly correlate with any of the investigated climate indices (AO, PNA, and Niño-3.4). These northerly winds occur more frequently in the summer months, suggesting that these winds are more influenced by local conditions than by mesoscale forcing. This method of identifying clusters in wind data allows objective identification of wind phenomena that may benefit the deployment of wind turbines, for example, in choosing combinations of wind speed and direction to investigate for turbine siting.

Abstract

This paper describes a three-dimensional immersed boundary method (IBM) that facilitates the explicit resolution of complex terrain within the Weather Research and Forecasting (WRF) model. Two interpolation methods—trilinear and inverse distance weighting (IDW)—are used at the core of the IBM algorithm. This work expands on the previous two-dimensional IBM algorithm of Lundquist et al., which uses bilinear interpolation. Simulations of flow over a three-dimensional hill are performed with WRF’s native terrain-following coordinate and with both IB methods. Comparisons of flow fields from the three simulations show excellent agreement, indicating that both IB methods produce accurate results. IDW proves more adept at handling highly complex urban terrain, where the trilinear interpolation algorithm fails. This capability is demonstrated by using the IDW core to model flow in Oklahoma City, Oklahoma, from intensive observation period 3 (IOP3) of the Joint Urban 2003 field campaign. Flow in Oklahoma City is simulated concurrently with an outer domain with flat terrain using one-way nesting to generate a turbulent flow field. Results from the IBM-WRF simulation of IOP3 compare well with observations from the field campaign, as well as with results from an urban computational fluid dynamics code, Finite Element Model in 3-Dimensions and Massively Parallelized (FEM3MP), which used body-fitted coordinates. Using the FAC2 performance metric from Chang and Hanna, which is the fraction of predictions within a factor of 2 of observations, IBM-WRF achieves 100% and 71% for velocity predictions using cup and sonic anemometer observations, respectively. For the passive scalar, 53% of the model predictions meet the FAC5 (factor of 5) criteria.

Abstract

This paper describes an immersed boundary method that facilitates the explicit resolution of complex terrain within the Weather Research and Forecasting (WRF) model. Mesoscale models, such as WRF, are increasingly used for high-resolution simulations, particularly in complex terrain, but errors associated with terrain-following coordinates degrade the accuracy of the solution. The use of an alternative-gridding technique, known as an immersed boundary method, alleviates coordinate transformation errors and eliminates restrictions on terrain slope that currently limit mesoscale models to slowly varying terrain. Simulations are presented for canonical cases with shallow terrain slopes, and comparisons between simulations with the native terrain-following coordinates and those using the immersed boundary method show excellent agreement. Validation cases demonstrate the ability of the immersed boundary method to handle both Dirichlet and Neumann boundary conditions. Additionally, realistic surface forcing can be provided at the immersed boundary by atmospheric physics parameterizations, which are modified to include the effects of the immersed terrain. Using the immersed boundary method, the WRF model is capable of simulating highly complex terrain, as demonstrated by a simulation of flow over an urban skyline.

Abstract

As the wind energy sector continues to grow, so does the need for reliable vertical wind profiles in the assessment of wind resources and turbine performance. In situ instrumentation mounted on meteorological towers can rarely probe the atmosphere across the full span of modern turbine rotor disks, which typically extend from 40 to 120 m above the surface. However, by measuring the Doppler shift of laser light backscattered by particles in the atmosphere, remote sensing lidar is capable of estimating wind speeds and turbulence at several altitudes in this range and above. Consequently, lidar has proven a promising technology for both wind resource assessment and turbine response characterization. The aim of this study is to quantify data availability for a coherent detection wind-profiling lidar—namely, the Leosphere Windcube.

To determine situations of suitable data return rates, a Windcube, collocated with a Vaisala CL31 ceilometer, was deployed as part of the Skywatch Observatory at the University of Colorado at Boulder. Aerosol backscatter, as measured by the ceilometer, and lidar carrier-to-noise ratio (CNR) are strongly correlated. Additionally, lidar CNR was found to depend on atmospheric turbulence characteristics and relative humidity in another deployment at a location in the United States Great Plains. These relationships suggest an ability to predict lidar performance based on widely available air quality assessments (such as PM_2.5 concentration) and other climatic conditions, thus providing guidance for determining the utility of lidar deployments at wind farms to characterize turbine performance.