The building of utility-scale wind farms requires knowledge of the wind speed climatology at hub height (typically 80–100 m). As most wind speed measurements are taken at 10 m above ground level, efforts are being made to relate 10-m measurements to approximate hub-height wind speeds. One common extrapolation method is the power law, which uses a shear parameter to estimate the wind shear between a reference height and hub height. The shear parameter is dependent on atmospheric stability and should ideally be determined independently for different atmospheric stability regimes. In this paper, data from the Oklahoma Mesonet are used to classify atmospheric stability and to develop stability-dependent power law fits for a nearby tall tower. Shear exponents developed from one month of data are applied to data from different seasons to determine the robustness of the power law method. In addition, similarity theory-based methods are investigated as possible alternatives to the power law. Results indicate that the power law method performs better than similarity theory methods, particularly under stable conditions, and can easily be applied to wind speed data from different seasons. In addition, the importance of using co-located near-surface and hub-height wind speed measurements to develop extrapolation fits is highlighted.

The current tornado detection algorithm (TDA) used by the National Weather Service produces a large number of false detections, primarily because it calculates azimuthal shear in a manner that is adversely impacted by noisy velocity data and range-degraded velocity signatures. Coincident with the advent of new radar-derived products and ongoing research involving new weather radar systems, the National Severe Storms Laboratory is developing an improved TDA. A primary component of this algorithm is the local, linear least squares derivatives (LLSD) azimuthal shear field. The LLSD method incorporates rotational derivatives of the velocity field and is affected less strongly by noisy velocity data in comparison with traditional “peak to peak” azimuthal shear calculations. LLSD shear is generally less range dependent than peak-to-peak shear, although some range dependency is unavoidable. The relationship between range and the LLSD shear values of simulated circulations was examined to develop a range correction for LLSD shear. A linear regression and artificial neural networks (ANNs) were investigated as range-correction models. Both methods were used to produce fits for the simulated shear data, although the ANN excelled as it could capture the nonlinear nature of the data. The range-correction methods were applied to real radar data from tornadic and nontornadic events to measure the capacity of the corrected shear to discriminate between tornadic and nontornadic circulations. The findings presented herein suggest that both methods increased shear values during tornadic periods by nearly an order of magnitude, facilitating differentiation between tornadic and nontornadic scans in tornadic events.

The Weather Research and Forecasting (WRF) model is used to investigate choice of land surface model (LSM) on the near surface wind profile, including heights reached by multi-megawatt (MW) wind turbines. Simulations of wind profiles and surface energy fluxes were made using five LSMs of varying degrees of sophistication in dealing with soil–plant–atmosphere feedbacks for the Department of Energy (DOE) Southern Great Plains (SGP) Atmospheric Radiation Measurement Program (ARM) Central Facility in Oklahoma, USA. Surface flux and wind profile measurements were available for validation. WRF was run for three, two-week periods covering varying canopy and meteorological conditions. The LSMs predicted a wide range of energy flux and wind shear magnitudes even during the cool autumn period when we expected less variability. Simulations of energy fluxes varied in accuracy by model sophistication, whereby LSMs with very simple or no soil–plant–atmosphere feedbacks were the least accurate; however, the most complex models did not consistently produce more accurate results. Errors in wind shear were also sensitive to LSM choice and were partially related to energy flux accuracy. The variability of LSM performance was relatively high suggesting that LSM representation of energy fluxes in WRF remains a large source of model uncertainty for simulating wind turbine inflow conditions.

On 2 April 2010, a quasi-linear convective system (QLCS) moved eastward through Oklahoma during the early morning hours. Wind damage in Rush Springs, Oklahoma, approached (enhanced Fujita) EF1-scale intensity and was likely associated with a mesovortex along the leading edge of the QLCS. The evolution of the QLCS as it produced its first bow echo was captured by the National Weather Radar Testbed Phased Array Radar (NWRT PAR) in Norman, Oklahoma. The NWRT PAR is an S-band radar with an electronically steered beam, allowing for rapid volumetric updates (~1 min) and user-defined scanning strategies. The rapid temporal updates and dense vertical sampling of the PAR created a detailed depiction of the damaging wind mechanisms associated with the QLCS. Key features sampled by the PAR include microbursts, an intensifying midlevel jet, and rotation associated with the mesovortex. In this work, PAR data are analyzed and compared to data from nearby operational radars, highlighting the advantages of using high-temporal-resolution data to monitor storm evolution. The PAR sampled the events preceding the Rush Springs circulation in great detail. Based on PAR data, the midlevel jet in the QLCS strengthened as it approached Rush Springs, creating an area of strong midlevel convergence where it impinged on the system-relative front-to-rear flow. As this convergence extended to the lower levels of the storm, a preexisting azimuthal shear maximum increased in magnitude and vertical extent, and EF1-scale damage occurred in Rush Springs. The depiction of these events in the PAR data demonstrates the complex and rapidly changing nature of QLCSs.