Estimation of turbulence dissipation rate and its variability from sonic anemometer and wind Doppler lidar during the XPIA field campaign

Despite turbulence being a fundamental transport process in the boundary layer, the capability of current numerical models to represent it is undermined by the limits of the adopted assumptions, notably that of local equilibrium. Here we leverage the potential of extensive observations in determining the variability in turbulence dissipation rate (ϵ). These observations can provide insights towards the understanding of the scales at which the major assumption of local equilibrium between generation and dissipation of turbulence is invalid. Typically, observations of ϵ require time- and labor-intensive measurements from sonic and/or hot-wire anemometers. We explore the capability of wind Doppler lidars to provide measurements of ϵ. We refine and extend an existing method to accommodate different atmospheric stability conditions. To validate our approach, we estimate ϵ from four wind Doppler lidars during the 3-month XPIA campaign at the Boulder Atmospheric Observatory (Colorado), and we assess the uncertainty of the proposed method by data intercomparison with sonic anemometer measurements of ϵ. Our analysis of this extensive dataset provides understanding of the climatology of turbulence dissipation over the course of the campaign. Further, the variability in ϵ with atmospheric stability, height, and wind speed is also assessed. Finally, we present how ϵ increases as nocturnal turbulence is generated during low-level jet events.</p

An improved WRF for urban-scale and complex-terrain applications

Simulations of atmospheric flow through urban areas must account for a wide range of physical phenomena including both mesoscale and urban processes. Numerical weather prediction models, such as the Weather and Research Forecasting model (WRF), excel at predicting synoptic and mesoscale phenomena. With grid spacings of less than 1 km (as is required for complex heterogeneous urban areas), however, the limits of WRF's terrain capabilities and subfilter scale (SFS) turbulence parameterizations are exposed. Observations of turbulence in urban areas frequently illustrate a local imbalance of turbulent kinetic energy (TKE), which cannot be captured by current turbulence models. Furthermore, WRF's terrain-following coordinate system is inappropriate for high-resolution simulations that include buildings. To address these issues, we are implementing significant modifications to the ARW core of the Weather Research and Forecasting model. First, we are implementing an improved turbulence model, the Dynamic Reconstruction Model (DRM), following Chow et al. (2005). Second, we are modifying WRF's terrain-following coordinate system by implementing an immersed boundary method (IBM) approach to account for the effects of urban geometries and complex terrain. Companion papers detailing the improvements enabled by the DRM and the IBM approaches are also presented (by Mirocha et al., paper 13.1, and K.A. Lundquist et al., paper 11.1, respectively). This overview of the LLNL-UC Berkeley collaboration presents the motivation for this work and some highlights of our progress to date. After implementing both DRM and an IBM for buildings in WRF, we will be able to seamlessly integrate mesoscale synoptic boundary conditions with building-scale urban simulations using grid nesting and lateral boundary forcing. This multi-scale integration will enable high-resolution simulations of flow and dispersion in complex geometries such as urban areas, as well as new simulation capabilities in regions of complex terrain

Characterization of flow recirculation zones at the Perdigão site using multi-lidar measurements

Because flow recirculation can generate significant amounts of turbulence, it can impact the success of wind energy projects. This study uses unique Doppler lidar observations to quantify occurrences of flow recirculation on lee sides of ridges. An extensive dataset of observations of flow over complex terrain is available from the Perdigão 2017 field campaign over a period of 3 months. The campaign site was selected because of the unique terrain feature of two nearly parallel ridges with a valley-to-ridge-top height difference of about 200&thinsp;m and a ridge-to-ridge distance of 1.4&thinsp;km. Six scanning Doppler lidars probed the flow field in several vertical planes orthogonal to the ridges using range–height indicator scans. With this lidar setup, we achieved vertical scans of the recirculation zone at three positions along two parallel ridges. We construct a method to identify flow recirculation zones in the scans, as well as define characteristics of these zones. According to our data analysis, flow recirculation, with reverse flow wind speeds greater than 0.5&thinsp;m&thinsp;s−1, occurs over 50 % of the time when the wind direction is perpendicular to the direction of the ridges. Atmospheric conditions, such as atmospheric stability and wind speed, affect the occurrence of flow recirculation. Flow recirculation occurs more frequently during periods with wind speeds above 8&thinsp;m&thinsp;s−1. Recirculation within the valley affects the mean wind and turbulence fields at turbine heights on the downwind ridge in magnitudes significant for wind resource assessment.</p

Do wind turbines pose roll hazards to light aircraft?

Wind energy accounted for 5.6&thinsp;% of all electricity generation in the United States in 2016. Much of this development has occurred in rural locations, where open spaces favorable for harnessing wind also serve general aviation airports. As such, nearly 40&thinsp;% of all United States wind turbines exist within 10&thinsp;km of a small airport. Wind turbines generate electricity by extracting momentum from the atmosphere, creating downwind wakes characterized by wind-speed deficits and increased turbulence. Recently, the concern that turbine wakes pose hazards for small aircraft has been used to limit wind-farm development. Herein, we assess roll hazards to small aircraft using large-eddy simulations (LES) of a utility-scale turbine wake. Wind-generated lift forces and subsequent rolling moments are calculated for hypothetical aircraft transecting the wake in various orientations. Stably and neutrally stratified cases are explored, with the stable case presenting a possible worst-case scenario due to longer-persisting wakes permitted by lower ambient turbulence. In both cases, only 0.001&thinsp;% of rolling moments experienced by hypothetical aircraft during down-wake and cross-wake transects lead to an increased risk of rolling.</p

Simulating atmosphere flow for wind energy applications with WRF-LES

Forecasts of available wind energy resources at high spatial resolution enable users to site wind turbines in optimal locations, to forecast available resources for integration into power grids, to schedule maintenance on wind energy facilities, and to define design criteria for next-generation turbines. This array of research needs implies that an appropriate forecasting tool must be able to account for mesoscale processes like frontal passages, surface-atmosphere interactions inducing local-scale circulations, and the microscale effects of atmospheric stability such as breaking Kelvin-Helmholtz billows. This range of scales and processes demands a mesoscale model with large-eddy simulation (LES) capabilities which can also account for varying atmospheric stability. Numerical weather prediction models, such as the Weather and Research Forecasting model (WRF), excel at predicting synoptic and mesoscale phenomena. With grid spacings of less than 1 km (as is often required for wind energy applications), however, the limits of WRF's subfilter scale (SFS) turbulence parameterizations are exposed, and fundamental problems arise, associated with modeling the scales of motion between those which LES can represent and those for which large-scale PBL parameterizations apply. To address these issues, we have implemented significant modifications to the ARW core of the Weather Research and Forecasting model, including the Nonlinear Backscatter model with Anisotropy (NBA) SFS model following Kosovic (1997) and an explicit filtering and reconstruction technique to compute the Resolvable Subfilter-Scale (RSFS) stresses (following Chow et al, 2005).We are also modifying WRF's terrain-following coordinate system by implementing an immersed boundary method (IBM) approach to account for the effects of complex terrain. Companion papers presenting idealized simulations with NBA-RSFS-WRF (Mirocha et al.) and IBM-WRF (K. A. Lundquist et al.) are also presented. Observations of flow through the Altamont Pass (Northern California) wind farm are available for validation of the WRF modeling tool for wind energy applications. In this presentation, we use these data to evaluate simulations using the NBA-RSFS-WRF tool in multiple configurations. We vary nesting capabilities, multiple levels of RSFS reconstruction, SFS turbulence models (the new NBA turbulence model versus existing WRF SFS turbulence models) to illustrate the capabilities of the modeling tool and to prioritize recommendations for operational uses. Nested simulations which capture both significant mesoscale processes as well as local-scale stable boundary layer effects are required to effectively predict available wind resources at turbine height

Assessing variability of wind speed: comparison and validation of 27 methodologies

Because wind resources vary from year to year, the intermonthly and interannual variability (IAV) of wind speed is a key component of the overall uncertainty in the wind resource assessment process, thereby creating challenges for wind farm operators and owners. We present a critical assessment of several common approaches for calculating variability by applying each of the methods to the same 37-year monthly wind-speed and energy-production time series to highlight the differences between these methods. We then assess the accuracy of the variability calculations by correlating the wind-speed variability estimates to the variabilities of actual wind farm energy production. We recommend the robust coefficient of variation (RCoV) for systematically estimating variability, and we underscore its advantages as well as the importance of using a statistically robust and resistant method. Using normalized spread metrics, including RCoV, high variability of monthly mean wind speeds at a location effectively denotes strong fluctuations of monthly total energy generation, and vice versa. Meanwhile, the wind-speed IAVs computed with annual-mean data fail to adequately represent energy-production IAVs of wind farms. Finally, we find that estimates of energy-generation variability require 10±3 years of monthly mean wind-speed records to achieve a 90&thinsp;% statistical confidence. This paper also provides guidance on the spatial distribution of wind-speed RCoV.</p

Spatial and temporal variability of turbulence dissipation rate in complex terrain

To improve parameterizations of the turbulence dissipation rate (ϵ) in numerical weather prediction models, the temporal and spatial variability of ϵ must be assessed. In this study, we explore influences on the variability of ϵ at various scales in the Columbia River Gorge during the WFIP2 field experiment between 2015 and 2017. We calculate ϵ from five sonic anemometers all deployed in a ∼4&thinsp;km2 area as well as from two scanning Doppler lidars and four profiling Doppler lidars, whose locations span a ∼300&thinsp;km wide region. We retrieve ϵ from the sonic anemometers using the second-order structure function method, from the scanning lidars with the azimuth structure function approach, and from the profiling lidars with a novel technique using the variance of the line-of-sight velocity. The turbulence dissipation rate shows large spatial variability, even at the microscale, especially during nighttime stable conditions. Orographic features have a strong impact on the variability of ϵ, with the correlation between ϵ at different stations being highly influenced by terrain. ϵ shows larger values in sites located downwind of complex orographic structures or in wind farm wakes. A clear diurnal cycle in ϵ is found, with daytime convective conditions determining values over an order of magnitude higher than nighttime stable conditions. ϵ also shows a distinct seasonal cycle, with differences greater than an order of magnitude between average ϵ values in summer and winter.</p

Consequences of Urban Stability Conditions for Computational Fluid Dynamics Simulations of Urban Dispersion

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, are simulated using the building-resolving computational fluid dynamics model, 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 non-linear eddy viscosity (NEV) approach, while the other considers buoyancy effects with a simple linear eddy viscosity (LEV) 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 (CBD) in the area we label 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 with the field observations. Simulations using the NEV turbulence parameterization generally exhibit better agreement with observations. To further explore this 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 which 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, and 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 much as seven 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, while 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, further downwind in the urban wake region, which we find to be approximately 1500 m beyond the perimeter of downtown Oklahoma City, the levels of building-induced turbulence greatly subside, and therefore the assumption of neutral stability is less valid

Wind turbine power production and annual energy production depend on atmospheric stability and turbulence

Using detailed upwind and nacelle-based measurements from a General Electric (GE) 1.5sle model with a 77 m rotor diameter, we calculate power curves and annual energy production (AEP) and explore their sensitivity to different atmospheric parameters to provide guidelines for the use of stability and turbulence filters in segregating power curves. The wind measurements upwind of the turbine include anemometers mounted on a 135 m meteorological tower as well as profiles from a lidar. We calculate power curves for different regimes based on turbulence parameters such as turbulence intensity (TI) as well as atmospheric stability parameters such as the bulk Richardson number (RB). We also calculate AEP with and without these atmospheric filters and highlight differences between the results of these calculations. The power curves for different TI regimes reveal that increased TI undermines power production at wind speeds near rated, but TI increases power production at lower wind speeds at this site, the US Department of Energy (DOE) National Wind Technology Center (NWTC). Similarly, power curves for different RB regimes reveal that periods of stable conditions produce more power at wind speeds near rated and periods of unstable conditions produce more power at lower wind speeds. AEP results suggest that calculations without filtering for these atmospheric regimes may overestimate the AEP. Because of statistically significant differences between power curves and AEP calculated with these turbulence and stability filters for this turbine at this site, we suggest implementing an additional step in analyzing power performance data to incorporate effects of atmospheric stability and turbulence across the rotor disk

A STUDY OF STABILITY CONDITIONS IN AN URBAN AREA

Accurate numerical prediction of airflow and tracer dispersion in urban areas depends, to a great extent, on the use of appropriate stability conditions. Due to the lack of relevant field measurements or sufficiently sophisticated turbulence models, modelers often assume that nearly neutral conditions are appropriate to use for the entire urban area being simulated. The main argument for such an assumption is that atmospheric stability (as defined by the Richardson number) is determined by both mechanical stresses and buoyant forcing but, for a typical urban setting with a given thermal stability or sensible heat flux, building-induced mechanical stresses can become so dominant to drive the resulting stability toward nearly neutral conditions. Results from our recent simulations of two Joint URBAN 2003 releases, using a computational fluid dynamics (CFD) model - FEM3MP, appear to support partially the assumption that urban areas tend toward neutral stability. More specifically, based on a model-data comparison for winds and concentration in the near field and velocity and turbulence profiles in the urban wake region, Chan and Lundquist (2005) and Lundquist and Chan (2005) observed that neutral stability assumption appears to be valid for intensive operation period (IOP) 9 (a nighttime release with moderate winds) and also appears to be valid for IOP 3 (a daytime release with strong buoyant forcing) in the urban core area but is less valid in the urban wake region. Our model, developed under the sponsorship of the U.S. Department of Energy (DOE) and Department of Homeland Security (DHS), is based on solving the three-dimensional, time-dependent, incompressible Navier-Stokes equations on massively parallel computer platforms. The numerical algorithm is based on finite-element discretization for effective treatment of complex building geometries and variable terrain, together with a semi-implicit projection scheme and modern iterative solvers developed by Gresho and Chan (1998) for efficient time integration. Physical processes treated in our code include turbulence modeling via Reynolds Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) approaches described in Chan and Stevens (2000), atmospheric stability, aerosols, UV radiation decay, surface energy budgets, and vegetative canopies, etc. Predictions from our model are continuously being verified against measured data from wind tunnel and field experiments. Examples of such studies are discussed in Chan et al. (2001, 2004), Chan and Leach (2004), Calhoun et al. (2004, 2005), and Humphreys et al. (2004). In this study, the stability conditions associated with two more of the Joint URBAN 2003 releases are investigated. Through a model-data comparison of the wind and concentration fields, observed buoyancy production in the urban wake region, together with predicted values of turbulence kinetic energy (TKE) in various regions of the computational domain, a more definitive characterization of stability conditions associated with the simulated releases is presented. In the following, we first discuss briefly the field experiments being simulated, then present sample results from a model-data comparison for both the wind and concentration fields, examine the predicted TKE field and the observed buoyant forcing relative to the total TKE in the urban wake, and finally offer a few concluding remarks including the resulting stability conditions of the simulated releases