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- Author or Editor: J. K. Lundquist x
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Abstract
Two formulations of a nonlinear turbulence subfilter-scale (SFS) stress model were implemented into the Advanced Research Weather Research and Forecasting model (ARW-WRF) version 3.0 for improved large-eddy simulation performance. The new models were evaluated against the WRF model’s standard Smagorinsky and 1.5-order turbulence kinetic energy (TKE) linear eddy-viscosity SFS stress models in simulations of geostrophically forced, neutral boundary layer flow over both flat terrain and a shallow, symmetric transverse ridge. Comparisons of simulation results with similarity profiles indicate that the nonlinear models significantly improve agreement with the expected profiles near the surface, reducing the overprediction of near-surface stress characteristic of linear eddy-viscosity models with no near-wall damping. Comparisons of simulations conducted using different mesh sizes indicate that the nonlinear model simulations at coarser resolutions agree more closely with the higher-resolution results than corresponding lower-resolution simulations using the standard WRF SFS stress models. The nonlinear models produced flows featuring a broader range of eddy sizes, with less spectral power at lower frequencies and more spectral power at higher frequencies. In simulated flow over the transverse ridge, distributions of flow separation and reversal near the surface simulated at higher resolution were likewise better depicted in coarser-resolution simulations using the nonlinear models.
Abstract
Two formulations of a nonlinear turbulence subfilter-scale (SFS) stress model were implemented into the Advanced Research Weather Research and Forecasting model (ARW-WRF) version 3.0 for improved large-eddy simulation performance. The new models were evaluated against the WRF model’s standard Smagorinsky and 1.5-order turbulence kinetic energy (TKE) linear eddy-viscosity SFS stress models in simulations of geostrophically forced, neutral boundary layer flow over both flat terrain and a shallow, symmetric transverse ridge. Comparisons of simulation results with similarity profiles indicate that the nonlinear models significantly improve agreement with the expected profiles near the surface, reducing the overprediction of near-surface stress characteristic of linear eddy-viscosity models with no near-wall damping. Comparisons of simulations conducted using different mesh sizes indicate that the nonlinear model simulations at coarser resolutions agree more closely with the higher-resolution results than corresponding lower-resolution simulations using the standard WRF SFS stress models. The nonlinear models produced flows featuring a broader range of eddy sizes, with less spectral power at lower frequencies and more spectral power at higher frequencies. In simulated flow over the transverse ridge, distributions of flow separation and reversal near the surface simulated at higher resolution were likewise better depicted in coarser-resolution simulations using the nonlinear models.
Abstract
The mixing depth of the boundary layer is an input to most atmospheric transport and dispersion (ATD) models, which obtain mixing depths in one of four ways: 1) observations by radiosondes, sodars, or other devices; 2) simulations by regional or mesoscale meteorological models; 3) parameterizations based on boundary layer similarity theory; or 4) climatological averages. This paper describes a situation during a field experiment when exceptionally low mixing depths persisted in the morning and led to relatively high observed tracer concentrations. The low mixing depths were caused by synoptic effects associated with a nearby stationary front and the outflow from a mesoscale thunderstorm complex located 20–50 km away. For the same time period, the ATD model-parameterized mixing depth was a factor of 5–10 higher, leading to predicted concentrations that were less than the observations by a factor of 5–10. The synoptic situation is described and local radiosonde and radar observations of mixing depth are presented, including comparisons with other more typical days. Time series of local observations of near-surface sensible heat fluxes are also plotted to demonstrate the suppression of turbulence by negative sensible heat fluxes during the period in question.
Abstract
The mixing depth of the boundary layer is an input to most atmospheric transport and dispersion (ATD) models, which obtain mixing depths in one of four ways: 1) observations by radiosondes, sodars, or other devices; 2) simulations by regional or mesoscale meteorological models; 3) parameterizations based on boundary layer similarity theory; or 4) climatological averages. This paper describes a situation during a field experiment when exceptionally low mixing depths persisted in the morning and led to relatively high observed tracer concentrations. The low mixing depths were caused by synoptic effects associated with a nearby stationary front and the outflow from a mesoscale thunderstorm complex located 20–50 km away. For the same time period, the ATD model-parameterized mixing depth was a factor of 5–10 higher, leading to predicted concentrations that were less than the observations by a factor of 5–10. The synoptic situation is described and local radiosonde and radar observations of mixing depth are presented, including comparisons with other more typical days. Time series of local observations of near-surface sensible heat fluxes are also plotted to demonstrate the suppression of turbulence by negative sensible heat fluxes during the period in question.
Abstract
In the U.S. state of Iowa, the increase in wind power production has motivated interest into the impacts of low-level jets on turbine performance. In this study, two commercial lidar systems were used to sample wind profiles in August 2013. Jets were systematically detected and assigned an intensity rating from 0 (weak) to 3 (strong). Many similarities were found between observed jets and the well-studied Great Plains low-level jet in summer, including average jet heights between 300 and 500 m above ground level, a preference for southerly wind directions, and a nighttime bias for stronger jets. Strong vertical wind shear and veer were observed, as well as veering over time associated with the LLJs. Speed, shear, and veer increases extended into the turbine-rotor layer during intense jets. Ramp events, in which winds rapidly increase or decrease in the rotor layer, were also commonly observed during jet formation periods. The lidar data were also used to evaluate various configurations of the Weather Research and Forecasting Model. Jet occurrence exhibited a stronger dependence on the choice of initial and boundary condition data, while reproduction of the strongest jets was influenced more strongly by the choice of planetary boundary layer scheme. A decomposition of mean model winds suggested that the main forcing mechanism for observed jets was the inertial oscillation. These results have implications for wind energy forecasting and site assessment in the Midwest.
Abstract
In the U.S. state of Iowa, the increase in wind power production has motivated interest into the impacts of low-level jets on turbine performance. In this study, two commercial lidar systems were used to sample wind profiles in August 2013. Jets were systematically detected and assigned an intensity rating from 0 (weak) to 3 (strong). Many similarities were found between observed jets and the well-studied Great Plains low-level jet in summer, including average jet heights between 300 and 500 m above ground level, a preference for southerly wind directions, and a nighttime bias for stronger jets. Strong vertical wind shear and veer were observed, as well as veering over time associated with the LLJs. Speed, shear, and veer increases extended into the turbine-rotor layer during intense jets. Ramp events, in which winds rapidly increase or decrease in the rotor layer, were also commonly observed during jet formation periods. The lidar data were also used to evaluate various configurations of the Weather Research and Forecasting Model. Jet occurrence exhibited a stronger dependence on the choice of initial and boundary condition data, while reproduction of the strongest jets was influenced more strongly by the choice of planetary boundary layer scheme. A decomposition of mean model winds suggested that the main forcing mechanism for observed jets was the inertial oscillation. These results have implications for wind energy forecasting and site assessment in the Midwest.
Abstract
Mesoscale atmospheric models are increasingly used for high-resolution (<3 km) simulations to better resolve smaller-scale flow details. Increased resolution is achieved using mesh refinement via grid nesting, a procedure where multiple computational domains are integrated either concurrently or in series. A constraint in the concurrent nesting framework offered by the Weather Research and Forecasting (WRF) Model is that mesh refinement is restricted to the horizontal dimensions. This limitation prevents control of the grid aspect ratio, leading to numerical errors due to poor grid quality and preventing grid optimization. Herein, a procedure permitting vertical nesting for one-way concurrent simulation is developed and validated through idealized cases. The benefits of vertical nesting are demonstrated using both mesoscale and large-eddy simulations (LES). Mesoscale simulations of the Terrain-Induced Rotor Experiment (T-REX) show that vertical grid nesting can alleviate numerical errors due to large aspect ratios on coarse grids, while allowing for higher vertical resolution on fine grids. Furthermore, the coarsening of the parent domain does not result in a significant loss of accuracy on the nested domain. LES of neutral boundary layer flow shows that, by permitting optimal grid aspect ratios on both parent and nested domains, use of vertical nesting yields improved agreement with the theoretical logarithmic velocity profile on both domains. Vertical grid nesting in WRF opens the path forward for multiscale simulations, allowing more accurate simulations spanning a wider range of scales than previously possible.
Abstract
Mesoscale atmospheric models are increasingly used for high-resolution (<3 km) simulations to better resolve smaller-scale flow details. Increased resolution is achieved using mesh refinement via grid nesting, a procedure where multiple computational domains are integrated either concurrently or in series. A constraint in the concurrent nesting framework offered by the Weather Research and Forecasting (WRF) Model is that mesh refinement is restricted to the horizontal dimensions. This limitation prevents control of the grid aspect ratio, leading to numerical errors due to poor grid quality and preventing grid optimization. Herein, a procedure permitting vertical nesting for one-way concurrent simulation is developed and validated through idealized cases. The benefits of vertical nesting are demonstrated using both mesoscale and large-eddy simulations (LES). Mesoscale simulations of the Terrain-Induced Rotor Experiment (T-REX) show that vertical grid nesting can alleviate numerical errors due to large aspect ratios on coarse grids, while allowing for higher vertical resolution on fine grids. Furthermore, the coarsening of the parent domain does not result in a significant loss of accuracy on the nested domain. LES of neutral boundary layer flow shows that, by permitting optimal grid aspect ratios on both parent and nested domains, use of vertical nesting yields improved agreement with the theoretical logarithmic velocity profile on both domains. Vertical grid nesting in WRF opens the path forward for multiscale simulations, allowing more accurate simulations spanning a wider range of scales than previously possible.
Abstract
The terrain-following coordinate system used by many atmospheric models can cause numerical instabilities due to discretization errors as resolved terrain slopes increase and the grid becomes highly skewed. The immersed boundary (IB) method, which does not require the grid to conform to the terrain, has been shown to alleviate these errors, and has been used successfully for high-resolution atmospheric simulations over steep terrain, including vertical building surfaces. Since many previous applications of IB methods to atmospheric models have used very fine grid resolution (5 m or less), the present study seeks to evaluate IB method performance over a range of grid resolutions and aspect ratios. Two classes of IB algorithms, velocity reconstruction and shear stress reconstruction, are tested within the common framework of the Weather Research and Forecasting (WRF) Model. Performance is evaluated in two test cases, one with flat terrain and the other with the topography of Askervein Hill, both under neutrally stratified conditions. WRF-IB results are compared to similarity theory, observations, and native WRF results. Despite sensitivity to the location at which the IB intersects the model grid, the velocity reconstruction IB method shows consistent performance when used with a hybrid RANS/LES surface scheme. The shear stress reconstruction IB method is not sensitive to the grid intersection, but is less consistent and near-surface velocity errors can occur at coarse resolutions. This study represents an initial investigation of IB method variability across grid resolutions in WRF. Future work will focus on improving IB method performance at intermediate to coarse resolutions.
Abstract
The terrain-following coordinate system used by many atmospheric models can cause numerical instabilities due to discretization errors as resolved terrain slopes increase and the grid becomes highly skewed. The immersed boundary (IB) method, which does not require the grid to conform to the terrain, has been shown to alleviate these errors, and has been used successfully for high-resolution atmospheric simulations over steep terrain, including vertical building surfaces. Since many previous applications of IB methods to atmospheric models have used very fine grid resolution (5 m or less), the present study seeks to evaluate IB method performance over a range of grid resolutions and aspect ratios. Two classes of IB algorithms, velocity reconstruction and shear stress reconstruction, are tested within the common framework of the Weather Research and Forecasting (WRF) Model. Performance is evaluated in two test cases, one with flat terrain and the other with the topography of Askervein Hill, both under neutrally stratified conditions. WRF-IB results are compared to similarity theory, observations, and native WRF results. Despite sensitivity to the location at which the IB intersects the model grid, the velocity reconstruction IB method shows consistent performance when used with a hybrid RANS/LES surface scheme. The shear stress reconstruction IB method is not sensitive to the grid intersection, but is less consistent and near-surface velocity errors can occur at coarse resolutions. This study represents an initial investigation of IB method variability across grid resolutions in WRF. Future work will focus on improving IB method performance at intermediate to coarse resolutions.
Abstract
Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s−1. Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s−1. Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ∼0.1 m s−1, but some reached 0.3 m s−1. All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error.
Significance Statement
To evaluate model forecast accuracy using remote sensing instruments, it is important to understand the effects of measurement uncertainties due to differences in the methods of measurement and data processing techniques, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. In this study, three types of collocated remote sensing systems are used to quantify the impact of measurement variability on the magnitude of calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m.
Abstract
Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s−1. Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s−1. Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ∼0.1 m s−1, but some reached 0.3 m s−1. All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error.
Significance Statement
To evaluate model forecast accuracy using remote sensing instruments, it is important to understand the effects of measurement uncertainties due to differences in the methods of measurement and data processing techniques, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. In this study, three types of collocated remote sensing systems are used to quantify the impact of measurement variability on the magnitude of calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m.
Abstract
An experimental study of the spatial wind structure in the vicinity of a wind turbine by a NOAA coherent Doppler lidar has been conducted. It was found that a working wind turbine generates a wake with the maximum velocity deficit varying from 27% to 74% and with the longitudinal dimension varying from 120 up to 1180 m, depending on the wind strength and atmospheric turbulence. It is shown that, at high wind speeds, the twofold increase of the turbulent energy dissipation rate (from 0.0066 to 0.013 m2 s−3) leads, on average, to halving of the longitudinal dimension of the wind turbine wake (from 680 to 340 m).
Abstract
An experimental study of the spatial wind structure in the vicinity of a wind turbine by a NOAA coherent Doppler lidar has been conducted. It was found that a working wind turbine generates a wake with the maximum velocity deficit varying from 27% to 74% and with the longitudinal dimension varying from 120 up to 1180 m, depending on the wind strength and atmospheric turbulence. It is shown that, at high wind speeds, the twofold increase of the turbulent energy dissipation rate (from 0.0066 to 0.013 m2 s−3) leads, on average, to halving of the longitudinal dimension of the wind turbine wake (from 680 to 340 m).
Abstract
Wind turbine wakes in the atmosphere are three-dimensional (3D) and time dependent. An important question is how best to measure atmospheric wake properties, both for characterizing these properties observationally and for verification of numerical, conceptual, and physical (e.g., wind tunnel) models of wakes. Here a scanning, pulsed, coherent Doppler lidar is used to sample a turbine wake using 3D volume scan patterns that envelop the wake and simultaneously measure the inflow profile. The volume data are analyzed for quantities of interest, such as peak velocity deficit, downwind variability of the deficit, and downwind extent of the wake, in a manner that preserves the measured data. For the case study presented here, in which the wake was well defined in the lidar data, peak deficits of up to 80% were measured 0.6–2 rotor diameters (D) downwind of the turbine, and the wakes extended more than 11D downwind. Temporal wake variability over periods of minutes and the effects of atmospheric gusts and lulls in the inflow are demonstrated in the analysis. Lidar scanning trade-offs important to ensuring that the wake quantities of interest are adequately sampled by the scan pattern, including scan coverage, number of scans per volume, data resolution, and scan-cycle repeat interval, are discussed.
Abstract
Wind turbine wakes in the atmosphere are three-dimensional (3D) and time dependent. An important question is how best to measure atmospheric wake properties, both for characterizing these properties observationally and for verification of numerical, conceptual, and physical (e.g., wind tunnel) models of wakes. Here a scanning, pulsed, coherent Doppler lidar is used to sample a turbine wake using 3D volume scan patterns that envelop the wake and simultaneously measure the inflow profile. The volume data are analyzed for quantities of interest, such as peak velocity deficit, downwind variability of the deficit, and downwind extent of the wake, in a manner that preserves the measured data. For the case study presented here, in which the wake was well defined in the lidar data, peak deficits of up to 80% were measured 0.6–2 rotor diameters (D) downwind of the turbine, and the wakes extended more than 11D downwind. Temporal wake variability over periods of minutes and the effects of atmospheric gusts and lulls in the inflow are demonstrated in the analysis. Lidar scanning trade-offs important to ensuring that the wake quantities of interest are adequately sampled by the scan pattern, including scan coverage, number of scans per volume, data resolution, and scan-cycle repeat interval, are discussed.
