An investigation of nocturnal intermittent turbulence during the Cooperative Atmosphere-Surface Exchange Study in 1999 (CASES-99) revealed three turbulence regimes at each observation height: 1) regime 1, a weak turbulence regime when the wind speed is less than a threshold value; 2) regime 2, a strong turbulence regime when the wind speed exceeds the threshold value; and 3) regime 3, a moderate turbulence regime when top-down turbulence sporadically bursts into the otherwise weak turbulence regime. For regime 1, the strength of small turbulence eddies is correlated with local shear and weakly related to local stratification. For regime 2, the turbulence strength increases systematically with wind speed as a result of turbulence generation by the bulk shear, which scales with the observation height. The threshold wind speed marks the transition above which the boundary layer approaches near-neutral conditions, where the turbulent mixing substantially reduces the stratification and temperature fluctuations. The preference of the turbulence regimes during CASES-99 is closely related to the existence and the strength of low-level jets. Because of the different roles of the bulk and local shear with regard to turbulence generation under different wind conditions, the relationship between turbulence strength and the local gradient Richardson number varies for the different turbulence regimes. Turbulence intermittency at any observation height was categorized in three ways: turbulence magnitude oscillations between regimes 1 and 2 as wind speed varies back and forth across its threshold value, episodic turbulence enhancements within regime 1 as a result of local instability, and downbursts of turbulence in regime 3.
Abstract. Recent increases in oil and natural gas (NG) production throughout the western US have come with scientific and public interest in emission rates, air quality and climate impacts related to this industry. This study uses a regionalscale air quality model (WRF-Chem) to simulate high ozone (O 3 ) episodes during the winter of 2013 over the Uinta Basin (UB) in northeastern Utah, which is densely populated by thousands of oil and NG wells. The high-resolution meteorological simulations are able qualitatively to reproduce the wintertime cold pool conditions that occurred in 2013, allowing the model to reproduce the observed multi-day buildup of atmospheric pollutants and the accompanying rapid photochemical ozone formation in the UB.Two different emission scenarios for the oil and NG sector were employed in this study. The first emission scenario (bottom-up) was based on the US Environmental Protection Agency (EPA) National Emission Inventory (NEI) (2011, version 1) for the oil and NG sector for the UB. The second emission scenario (top-down) was based on estimates of methane (CH 4 ) emissions derived from in situ aircraft measurements and a regression analysis for multiple species relative to CH 4 concentration measurements in the UB. Evaluation of the model results shows greater underestimates of CH 4 and other volatile organic compounds (VOCs) in the simulation with the NEI-2011 inventory than in the case when the top-down emission scenario was used. Unlike VOCs, the NEI-2011 inventory significantly overestimates the emissions of nitrogen oxides (NO x ), while the topdown emission scenario results in a moderate negative bias. The model simulation using the top-down emission case captures the buildup and afternoon peaks observed during high O 3 episodes. In contrast, the simulation using the bottomup inventory is not able to reproduce any of the observed high O 3 concentrations in the UB. Simple emission reduction scenarios show that O 3 production is VOC sensitive and NO x insensitive within the UB. The model results show a disproportionate contribution of aromatic VOCs to O 3 formation relative to all other VOC emissions. The model analysis reveals that the major factors driving high wintertime O 3 in the UB are shallow boundary layers with light winds, high emissions of VOCs from oil and NG operations compared to NO x emissions, enhancement of photolysis fluxes and reduction of O 3 loss from deposition due to snow cover.
In the nighttime stable boundary layer (SBL), shear and turbulence are generated in the layer between the maximum of the low-level jet (LLJ) and the earth's surface. Here, it is investigated whether gross properties of the LLJ-its height and speed-could be used to diagnose turbulence intensities in this subjet layer. Data on the height and speed of the LLJ maximum were available at high vertical and temporal resolution using the high-resolution Doppler lidar (HRDL). These data were used to estimate a subjet layer shear, which was computed as the ratio of the speed to the height of the jet maximum, and a jet Richardson number Ri J , averaged at 15min intervals for 10 nights when HRDL LLJ data were available for this study. The shear and Ri J values were compared with turbulence kinetic energy (TKE) values measured near the top of the 60-m tower at the Cooperative Atmosphere-Surface Exchange Study-1999 (CASES-99) main site. TKE values were small for Ri J greater than 0.4, but as Ri J decreased to less than ϳ0.4, TKE values increased, indicating that Ri J does have merit in estimating turbulence magnitudes. Another interesting finding was that shear values tended to cluster around a constant value of 0.1 s Ϫ1 for TKE values that were not too small, that is, for TKE greater than ϳ0.1 m 2 s Ϫ2 .
Profiles of mean winds and turbulence were measured by the High Resolution Doppler lidar in the strong-wind stable boundary layer (SBL) with continuous turbulence. The turbulence quantity measured was the variance of the streamwise wind velocity component 2 u . This variance is a component of the turbulence kinetic energy (TKE), and it is shown to be numerically approximately equal to TKE for stable conditions-profiles of 2 u are therefore equivalent to profiles of TKE. Mean-wind profiles showed lowlevel jet (LLJ) structure for most of the profiles, which represented 10-min averages of mean and fluctuating quantities throughout each of the six nights studied. Heights were normalized by the height of the first LLJ maximum above the surface Z X , and the velocity scale used was the speed of the jet U X , which is shown to be superior to the friction velocity u * as a velocity scale. The major results were 1) the ratio of the maximum value of the streamwise standard deviation to the LLJ speed u /U X was found to be 0.05, and 2) the three most common 2 u profile shapes were determined by stability (or Richardson number Ri). The least stable profile shapes had the maximum 2 u at the surface decreasing to a minimum at the height of the LLJ; profiles that were somewhat more stable had constant 2 u through a portion of the subjet layer; and the most stable of the profiles had a maximum of 2 u aloft, although it is important to note that the Ri for even the most stable of the three profile categories averaged less than 0.20. The datasets used in this study were two nights from the Cooperative Atmosphere-Surface Exchange Study 1999 campaign (CASES-99) and four nights from the Lamar Low-Level Jet Project, a wind-energy experiment in southeast Colorado, during September 2003.
Because of the dense arrays at most wind farms, the region of disturbed flow downstream of an individual turbine leads to reduced power production and increased structural loading for its leeward counterparts. Currently, wind farm wake modeling, and hence turbine layout optimization, suffers from an unacceptable degree of uncertainty, largely because of a lack of adequate experimental data for model validation. Accordingly, nearly 100 h of wake measurements were collected with long-range Doppler lidar at the National Wind Technology Center at the National Renewable Energy Laboratory in the Turbine Wake and Inflow Characterization Study (TWICS). This study presents quantitative procedures for determining critical parameters from this extensive dataset—such as the velocity deficit, the size of the wake boundary, and the location of the wake centerline—and categorizes the results by ambient wind speed, turbulence, and atmospheric stability. Despite specific reference to lidar, the methodology is general and could be applied to extract wake characteristics from other remote sensor datasets, as well as computational simulation output. The observations indicate an initial velocity deficit of 50%−60% immediately behind the turbine, which gradually declines to 15%−25% at a downwind distance x of 6.5 rotor diameters (D). The wake expands with downstream distance, albeit less so in the vertical direction due to the presence of the ground: initially the same size as the rotor, the extent of the wake grows to 2.7D (1.2D) in the horizontal (vertical) at x = 6.5D. Moreover, the vertical location of the wake center shifts upward with downstream distance because of the tilt of the rotor.
The primary goal of the Second Wind Forecast Improvement Project (WFIP2) is to advance the state-of-the-art of wind energy forecasting in complex terrain. To achieve this goal, a comprehensive 18-month field measurement campaign was conducted in the region of the Columbia River basin. The observations were used to diagnose and quantify systematic forecast errors in the operational High-Resolution Rapid Refresh (HRRR) model during weather events of particular concern to wind energy forecasting. Examples of such events are cold pools, gap flows, thermal troughs/marine pushes, mountain waves, and topographic wakes. WFIP2 model development has focused on the boundary layer and surface-layer schemes, cloud–radiation interaction, the representation of drag associated with subgrid-scale topography, and the representation of wind farms in the HRRR. Additionally, refinements to numerical methods have helped to improve some of the common forecast error modes, especially the high wind speed biases associated with early erosion of mountain–valley cold pools. This study describes the model development and testing undertaken during WFIP2 and demonstrates forecast improvements. Specifically, WFIP2 found that mean absolute errors in rotor-layer wind speed forecasts could be reduced by 5%–20% in winter by improving the turbulent mixing lengths, horizontal diffusion, and gravity wave drag. The model improvements made in WFIP2 are also shown to be applicable to regions outside of complex terrain. Ongoing and future challenges in model development will also be discussed.
The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE)- and National Oceanic and Atmospheric Administration (NOAA)-funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign that took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 km × 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.
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