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Larry K. Berg, Rob K. Newsom, and David D. Turner

Abstract

One year of coherent Doppler lidar data collected at the U.S. Department of Energy’s Atmospheric Radiation Measurement site in Oklahoma was analyzed to provide profiles of vertical velocity variance, skewness, and kurtosis for cases of cloud-free convective boundary layers. The variance was normalized by the Deardorff convective velocity scale, which was successful when the boundary layer depth was stationary but failed in situations in which the layer was changing rapidly. In this study, the data are sorted according to time of day, season, wind direction, surface shear stress, degree of instability, and wind shear across the boundary layer top. The normalized variance was found to have its peak value near a normalized height of 0.25. The magnitude of the variance changes with season, shear stress, degree of instability, and wind shear across the boundary layer top. The skewness was largest in the top half of the boundary layer (with the exception of wintertime conditions). The skewness was also found to be a function of the season, shear stress, and wind shear across the boundary layer top. Like skewness, the vertical profile of kurtosis followed a consistent pattern, with peak values near the boundary layer top. The normalized altitude of the peak values of kurtosis was found to be higher when there was a large amount of wind shear at the boundary layer top.

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Rob K. Newsom, David Ligon, Ron Calhoun, Rob Heap, Edward Cregan, and Marko Princevac

Abstract

Dual-Doppler lidar observations are used to assess the accuracy of single-Doppler retrievals of microscale wind and temperature fields in a shear-driven convective boundary layer. The retrieval algorithm, which is based on four-dimensional variational data assimilation, is applied by using dual- and single-Doppler lidar data that are acquired during the Joint Urban 2003 field experiment. The velocity field that was retrieved using single-Doppler data is compared directly with radial velocities that were measured by a second noncollocated lidar. Dual-Doppler retrievals are also performed and then compared with the single-Doppler retrieval. The linear correlation coefficient and rms deviation between the single-Doppler retrieval and the observations from the second lidar are found to be 0.94 and 1.2 m s−1, respectively. The high correlation is mainly the result of good agreement in the mean vertical structure as observed by the two lidars. Comparisons between the single- and dual-Doppler retrieval indicate that the single-Doppler retrieval underestimates the magnitude of fluctuations in the crossbeam direction. Vertical profiles of horizontally averaged correlations between the single- and dual-Doppler retrievals also show a marginal correlation (0.4–0.8) between one of the horizontal velocity components. Again, this suggests that the retrieval algorithm has difficulty estimating the crossbeam component from single-Doppler data.

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Jerome D. Fast, Rob K. Newsom, K. Jerry Allwine, Qin Xu, Pengfei Zhang, Jeffrey Copeland, and Juanzhen Sun

Abstract

Two entirely different methods for retrieving 3D fields of horizontal winds from Next Generation Weather Radar (NEXRAD) radial velocities have been evaluated using radar wind profiler measurements to determine whether routine wind retrievals would be useful for atmospheric dispersion model applications. The first method uses a physical algorithm based on four-dimensional variational data assimilation, and the second simpler method uses a statistical technique based on an analytic formulation of the background error covariance. Both methods can be run in near–real time, but the simpler method was executed about 2.5 times as fast as the four-dimensional variational method. The observed multiday and diurnal variations in wind speed and direction were reproduced by both methods below ∼1.5 km above the ground in the vicinity of Oklahoma City, Oklahoma, during July 2003. However, wind retrievals overestimated the strength of the nighttime low-level jet by as much as 65%. The wind speeds and directions obtained from both methods were usually similar when compared with profiler measurements, and neither method outperformed the other statistically. Within a dispersion model framework, the 3D wind fields and transport patterns were often better represented when the wind retrievals were included along with operational data. Despite uncertainties in the wind speed and direction obtained from the wind retrievals that are higher than those from remote sensing radar wind profilers, the inclusion of the wind retrievals is likely to produce more realistic temporal variations in the winds aloft than would be obtained by interpolation using the available radiosondes, especially during rapidly changing synoptic- and mesoscale conditions.

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Qing Yang, Larry K. Berg, Mikhail Pekour, Jerome D. Fast, Rob K. Newsom, Mark Stoelinga, and Catherine Finley

Abstract

One challenge with wind-power forecasts is the accurate prediction of rapid changes in wind speed (ramps). To evaluate the Weather Research and Forecasting (WRF) model's ability to predict such events, model simulations, conducted over an area of complex terrain in May 2011, are used. The sensitivity of the model's performance to the choice among three planetary boundary layer (PBL) schemes [Mellor–Yamada–Janjić (MYJ), University of Washington (UW), and Yonsei University (YSU)] is investigated. The simulated near-hub-height winds (62 m), vertical wind speed profiles, and ramps are evaluated against measurements obtained from tower-mounted anemometers, a Doppler sodar, and a radar wind profiler deployed during the Columbia Basin Wind Energy Study (CBWES). The predicted winds at near–hub height have nonnegligible biases in monthly mean under stable conditions. Under stable conditions, the simulation with the UW scheme better predicts upward ramps and the MYJ scheme is the most successful in simulating downward ramps. Under unstable conditions, simulations using the YSU and UW schemes show good performance in predicting upward ramps and downward ramps, with the YSU scheme being slightly better at predicting ramps with durations longer than 1 h. The largest differences in mean wind speed profiles among simulations using the three PBL schemes occur during upward ramps under stable conditions, which were frequently associated with low-level jets. The UW scheme has the best overall performance in ramp prediction over the CBWES site when evaluated using prediction accuracy and capture-rate statistics, but no single PBL parameterization is clearly superior to the others when all atmospheric conditions are considered.

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Rob K. Newsom, Larry K. Berg, Mikhail Pekour, Jerome Fast, Qin Xu, Pengfei Zhang, Qing Yang, William J. Shaw, and Julia Flaherty

Abstract

The accuracy of winds derived from Next Generation Weather Radar (NEXRAD) level-II data is assessed by comparison with independent observations from 915-MHz radar wind profilers. The evaluation is carried out at two locations with very different terrain characteristics. One site is located in an area of complex terrain within the State Line Wind Energy Center in northeastern Oregon. The other site is located in an area of flat terrain on the east-central Florida coast. The National Severe Storm Laboratory’s two-dimensional variational data assimilation (2DVar) algorithm is used to retrieve wind fields from the KPDT (Pendleton, Oregon) and KMLB (Melbourne, Florida) NEXRAD radars. Wind speed correlations at most observation height levels fell in the range from 0.7 to 0.8, indicating that the retrieved winds followed temporal fluctuations in the profiler-observed winds reasonably well. The retrieved winds, however, consistently exhibited slow biases in the range of 1–2 m s−1. Wind speed difference distributions were broad, with standard deviations in the range from 3 to 4 m s−1. Results from the Florida site showed little change in the wind speed correlations and difference standard deviations with altitude between about 300 and 1400 m AGL. Over this same height range, results from the Oregon site showed a monotonic increase in the wind speed correlation and a monotonic decrease in the wind speed difference standard deviation with increasing altitude. The poorest overall agreement occurred at the lowest observable level (~300 m AGL) at the Oregon site, where the effects of the complex terrain were greatest.

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Yansen Wang, Cheryl L. Klipp, Dennis M. Garvey, David A. Ligon, Chatt C. Williamson, Sam S. Chang, Rob K. Newsom, and Ronald Calhoun

Abstract

Boundary layer wind data observed by a Doppler lidar and sonic anemometers during the mornings of three intensive observational periods (IOP2, IOP3, and IOP7) of the Joint Urban 2003 (JU2003) field experiment are analyzed to extract the mean and turbulent characteristics of airflow over Oklahoma City, Oklahoma. A strong nocturnal low-level jet (LLJ) dominated the flow in the boundary layer over the measurement domain from midnight to the morning hours. Lidar scans through the LLJ taken after sunrise indicate that the LLJ elevation shows a gradual increase of 25–100 m over the urban area relative to that over the upstream suburban area. The mean wind speed beneath the jet over the urban area is about 10%–15% slower than that over the suburban area. Sonic anemometer observations combined with Doppler lidar observations in the urban and suburban areas are also analyzed to investigate the boundary layer turbulence production in the LLJ-dominated atmospheric boundary layer. The turbulence kinetic energy was higher over the urban domain mainly because of the shear production of building surfaces and building wakes. Direct transport of turbulent momentum flux from the LLJ to the urban street level was very small because of the relatively high elevation of the jet. However, since the LLJ dominated the mean wind in the boundary layer, the turbulence kinetic energy in the urban domain is correlated directly with the LLJ maximum speed and inversely with its height. The results indicate that the jet Richardson number is a reasonably good indicator for turbulent kinetic energy over the urban domain in the LLJ-dominated atmospheric boundary layer.

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