WRF-simulated low-level jets over Iowa: characterization and sensitivity studies

Abstract

Abstract. Output from 6 months of high-resolution simulations with the Weather Research and Forecasting (WRF) model are analyzed to characterize local low-level jets (LLJs) over Iowa for winter and spring in the contemporary climate. Low-level jets affect rotor plane aerodynamic loading, turbine structural loading and turbine performance, and thus accurate characterization and identification are pertinent. Analyses using a detection algorithm wherein the wind speed above and below the jet maximum must be below 80 % of the jet wind speed within a vertical window of approximately 20–530 m a.g.l. (above ground level) indicate the presence of an LLJ in at least one of the 14 700 4 km×4 km grid cells over Iowa on 98 % of nights. Nocturnal LLJs are most frequently associated with stable stratification and low turbulent kinetic energy (TKE) and hence are more frequent during the winter months. The spatiotemporal mean LLJ maximum (jet core) wind speed is 9.55 m s−1, and the mean height is 182 m. Locations of high LLJ frequency and duration across the state are seasonally varying, with a mean duration of 3.5 h. The highest frequency occurs in the topographically complex northwest of the state in winter and in the flatter northeast of the state in spring. Sensitivity of LLJ characteristics to the (i) LLJ definition and (ii) vertical resolution at which the WRF output is sampled is examined. LLJ definitions commonly used in the literature are considered in the first sensitivity analysis. These sensitivity analyses indicate that LLJ characteristics are highly variable with definition. Use of different definitions identifies both different frequencies of LLJs and different LLJ events. Further, when the model output is down-sampled to lower vertical resolution, the mean jet core wind speed height decreases, but spatial distributions of regions of high frequency and duration are conserved. Implementation of a polynomial interpolation to extrapolate down-sampled output to full-resolution results in reduced sensitivity of LLJ characteristics to down-sampling.

Classification and properties of coastal wind profiles with negative gradients – an observational study

“…The lidar measurements used in this study were performed on eight height levels, most levels separated by a distance of 50 m. As LLJ cores can be of varying vertical extent, it is likely that this vertical resolution might not capture all LLJs. We refer to Nunalee and Basu (2014) and Aird et al (2021) for discussion on the sensitivity of detecting LLJs to the height resolution.…”

“…As the definitions of LLJs vary in the literature, we follow the most recent recommendation by Aird et al (2021), applying both a fixed and a relative criterion for LLJ classification. A profile was classified as a strong LLJ if there was a well-pronounced local maximum in the wind profile where the core speed was both at least 20% and at least 2 m s −1 stronger than the weakest wind speed in the lidar profile both above and below the jet core.…”

Occurrence of Low-Level Jets over the Eastern U.S. Coastal Zone at Heights Relevant to Wind Energy

“…LLJ frequency and characteristics are sensitive to the definition employed [50,51]. Use of only an absolute wind speed threshold for detection (i.e., maximum wind speed at the jet core exceeds that above and below the jet by X ms −1 ) results in higher speed LLJs, whereas the use of only a relative (or variable) wind speed threshold (i.e., jet core wind speed exceeds wind speeds above and below by X%) results in lower speed LLJs [52].…”

“…PBLH is analyzed hourly and is as defined within the MYNN scheme as the lowest height at which modeled turbulent kinetic energy (TKE) < 1.0 × 10 −6 m 2 s −2 [51].…”

Classification and properties of non-idealized coastal wind profiles – an observational study

“…All lidar wind profiles were classified into one of the following six classes: idealized, negative, LLM, transition, weak LLJ or strong LLJ. As the definitions of LLJs vary in the literature, we follow the most recent recommendation by Aird et al (2021), applying both a fixed and a relative criterion for LLJ classification. Using hourly model data of wind profiles up to approximately 530 m height over Iowa (USA) during a period of 6 months (December 2007 to May 2008), Aird et al (2021) concluded that defining LLJs based on only a fixed criterion compared to only using a relative criterion identified different LLJs 40 % of the time.…”