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- Author or Editor: R. Kent Goodrich x
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Abstract
The accuracy of the radial wind velocity measured with a radar wind profiler will depend on turbulent variability and instrumental noise. Radial velocity estimates of a boundary layer wind profiler are compared with those estimated by a Doppler lidar over 2.3 h. The lidar resolution volume was much narrower than the profiler volume, but the samples were well matched in range and time. The wind profiler radial velocity was computed using two common algorithms [profiler online program (POP) and National Center for Atmospheric Research improved moments algorithm (NIMA)]. The squared correlation between radial velocities measured with the two instruments was R 2 = 0.99, and the standard deviation of the difference was about σ r = 0.20–0.23 m s−1 for radial velocities of greater than 1 m s−1 and σ r = 0.16–0.35 m s−1 for radial velocities of less than 1 m s−1. Small radial velocities may be treated differently in radar wind profiler processing because of ground-clutter mitigation strategies. A standard deviation of σ r = 0.23 m s−1 implies an error in horizontal winds from turbulence and noise of less than 1 m s−1 for a single cycle through the profiler beam directions and of less than 0.11–0.27 m s−1 for a 30-min average measurement, depending on the beam pointing sequence. The accuracy of a wind profiler horizontal wind measurement will also depend on assumptions of spatial and temporal inhomogeneity of the atmosphere, which are not considered in this comparison. The wind profiler radial velocities from the POP and NIMA are in good agreement. However, the analysis does show the need for improvements in wind profiler processing when radial velocity is close to zero.
Abstract
The accuracy of the radial wind velocity measured with a radar wind profiler will depend on turbulent variability and instrumental noise. Radial velocity estimates of a boundary layer wind profiler are compared with those estimated by a Doppler lidar over 2.3 h. The lidar resolution volume was much narrower than the profiler volume, but the samples were well matched in range and time. The wind profiler radial velocity was computed using two common algorithms [profiler online program (POP) and National Center for Atmospheric Research improved moments algorithm (NIMA)]. The squared correlation between radial velocities measured with the two instruments was R 2 = 0.99, and the standard deviation of the difference was about σ r = 0.20–0.23 m s−1 for radial velocities of greater than 1 m s−1 and σ r = 0.16–0.35 m s−1 for radial velocities of less than 1 m s−1. Small radial velocities may be treated differently in radar wind profiler processing because of ground-clutter mitigation strategies. A standard deviation of σ r = 0.23 m s−1 implies an error in horizontal winds from turbulence and noise of less than 1 m s−1 for a single cycle through the profiler beam directions and of less than 0.11–0.27 m s−1 for a 30-min average measurement, depending on the beam pointing sequence. The accuracy of a wind profiler horizontal wind measurement will also depend on assumptions of spatial and temporal inhomogeneity of the atmosphere, which are not considered in this comparison. The wind profiler radial velocities from the POP and NIMA are in good agreement. However, the analysis does show the need for improvements in wind profiler processing when radial velocity is close to zero.
Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar DataAbstract
An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.
Abstract
An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.
Abstract
The Juneau, Alaska, airport vicinity experiences frequent episodes of moderate and severe turbulence, which affect arriving and departing air traffic. The Federal Aviation Administration funded the National Center for Atmospheric Research to develop a warning system, consisting of carefully placed anemometers and wind profilers, along with data communications, an algorithm, and display, to warn pilots of potentially hazardous situations. The system uses regressions based on comparisons of research aircraft data with measurements from the ground-based sensors to estimate the turbulence intensity along selected flight paths. This paper describes the development of the turbulence warning system, from meteorological characteristics through sensor placement, algorithm construction and evaluation, and display design. The discussion includes how best estimates of winds were made in adverse meteorological and topographic conditions, how turbulence was calculated from aircraft conducting various flight maneuvers, how bad data were identified and removed from the system, how the regressors were selected, and the skill of the system.
Abstract
The Juneau, Alaska, airport vicinity experiences frequent episodes of moderate and severe turbulence, which affect arriving and departing air traffic. The Federal Aviation Administration funded the National Center for Atmospheric Research to develop a warning system, consisting of carefully placed anemometers and wind profilers, along with data communications, an algorithm, and display, to warn pilots of potentially hazardous situations. The system uses regressions based on comparisons of research aircraft data with measurements from the ground-based sensors to estimate the turbulence intensity along selected flight paths. This paper describes the development of the turbulence warning system, from meteorological characteristics through sensor placement, algorithm construction and evaluation, and display design. The discussion includes how best estimates of winds were made in adverse meteorological and topographic conditions, how turbulence was calculated from aircraft conducting various flight maneuvers, how bad data were identified and removed from the system, how the regressors were selected, and the skill of the system.
