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Abstract
Four years of data from three radar wind profilers and collocated anemometers are used to examine the airflow regimes near Juneau, Alaska. Wind direction probability density functions and wind rose histograms show the dominant wind speeds and directions from a long time series of observations. Analysis of diurnal variation separates mountain–valley flow events from synoptically driven events. Flow constrained by the Gastineau Channel dominates the winds near downtown Juneau, and the wind profilers document the rotation of this flow as it merges with the synoptic flow above the surrounding mountains. Strong flows from the northeast over the Taku Glacier, locally known as “Taku flows,” are also documented. These flows are less frequent but can cause strong wind storms at the surface. In addition, local flow effects are seen, including winds turning in response to terrain influence, drainage flows in creek valleys, and cross-valley flows. This analysis also demonstrates that radar wind profilers, using recently developed data-processing algorithms [in this case the National Center for Atmospheric Research (NCAR) Improved Moments Algorithm–NCAR Winds and Confidence Algorithm (NIMA–NWCA)], can provide valuable data even at low altitudes near complex terrain and sources of ground clutter.
Abstract
Four years of data from three radar wind profilers and collocated anemometers are used to examine the airflow regimes near Juneau, Alaska. Wind direction probability density functions and wind rose histograms show the dominant wind speeds and directions from a long time series of observations. Analysis of diurnal variation separates mountain–valley flow events from synoptically driven events. Flow constrained by the Gastineau Channel dominates the winds near downtown Juneau, and the wind profilers document the rotation of this flow as it merges with the synoptic flow above the surrounding mountains. Strong flows from the northeast over the Taku Glacier, locally known as “Taku flows,” are also documented. These flows are less frequent but can cause strong wind storms at the surface. In addition, local flow effects are seen, including winds turning in response to terrain influence, drainage flows in creek valleys, and cross-valley flows. This analysis also demonstrates that radar wind profilers, using recently developed data-processing algorithms [in this case the National Center for Atmospheric Research (NCAR) Improved Moments Algorithm–NCAR Winds and Confidence Algorithm (NIMA–NWCA)], can provide valuable data even at low altitudes near complex terrain and sources of ground clutter.
Abstract
It has long been realized that turbulent mixing of vertical refractive-index gradients is responsible for most clear-air echoes seen by UHF radars. The assumptions that the turbulence is isotropic and homogeneous then let us predict a scale dependence, and therefore wavelength dependence, for the strength of these Bragg scattered echoes. This dependence, λ−1/3, is quite different from the wavelength dependence of Rayleigh scatter from hydrometeors, λ−4. Three sensitive collocated clear-air radars were used in coordinated experiments to test the predicted λ−1/3 dependence. The radars have well-separated wavelengths allowing us to probe atmospheric turbulence at three Bragg scales of 34, 11.5, and 1.5 cm, and recent modifications made to the radars enabled us to collect measurements closely matched in space and time. Results from measurements taken at many altitudes show that the λ−1/3 dependence is frequently observed. However, many measurements deviate from the prediction and the measured power law is more accurately described as a distribution centered about λ−1/3. Possible explanations for this include temporal variability and anisotropy of the turbulence as well as measurement limitations. Observations of clouds and precipitation at the three wavelengths are also presented. The wavelength dependence of these measurements is explained by combined returns from Rayleigh backscatter from the hydrometeors and Bragg scatter from the clear air.
Abstract
It has long been realized that turbulent mixing of vertical refractive-index gradients is responsible for most clear-air echoes seen by UHF radars. The assumptions that the turbulence is isotropic and homogeneous then let us predict a scale dependence, and therefore wavelength dependence, for the strength of these Bragg scattered echoes. This dependence, λ−1/3, is quite different from the wavelength dependence of Rayleigh scatter from hydrometeors, λ−4. Three sensitive collocated clear-air radars were used in coordinated experiments to test the predicted λ−1/3 dependence. The radars have well-separated wavelengths allowing us to probe atmospheric turbulence at three Bragg scales of 34, 11.5, and 1.5 cm, and recent modifications made to the radars enabled us to collect measurements closely matched in space and time. Results from measurements taken at many altitudes show that the λ−1/3 dependence is frequently observed. However, many measurements deviate from the prediction and the measured power law is more accurately described as a distribution centered about λ−1/3. Possible explanations for this include temporal variability and anisotropy of the turbulence as well as measurement limitations. Observations of clouds and precipitation at the three wavelengths are also presented. The wavelength dependence of these measurements is explained by combined returns from Rayleigh backscatter from the hydrometeors and Bragg scatter from the clear air.
Abstract
Two independent radar methods for estimating the turbulent eddy dissipation rate ε are applied to a common dataset, and the results are compared. The first method estimates ε from backscattered power and relies on the effects of turbulent mixing of atmospheric refractive index gradients. It requires additional measurements of temperature and humidity from a balloon sounding. The second makes use of broadening of the backscattered Doppler spectrum by turbulent motions. The turbulent eddy dissipation rate ε is a measure of the energy cascade through scales of inertial subrange turbulence. Data were collected with the Millstone Hill UHF radar in Westford, Massachusetts, and with Cross-chain Loran Atmospheric Sounding System thermodynamic soundings launched from Hanscom Field about 25 km away. Encouraging similarities are found both in the magnitude and shape of the measured profiles, though differences are also found. Some differences may be explained by characteristics of the measurement techniques. The relative strengths and weaknesses of the methods, and limitations imposed by the radar, will be discussed.
Abstract
Two independent radar methods for estimating the turbulent eddy dissipation rate ε are applied to a common dataset, and the results are compared. The first method estimates ε from backscattered power and relies on the effects of turbulent mixing of atmospheric refractive index gradients. It requires additional measurements of temperature and humidity from a balloon sounding. The second makes use of broadening of the backscattered Doppler spectrum by turbulent motions. The turbulent eddy dissipation rate ε is a measure of the energy cascade through scales of inertial subrange turbulence. Data were collected with the Millstone Hill UHF radar in Westford, Massachusetts, and with Cross-chain Loran Atmospheric Sounding System thermodynamic soundings launched from Hanscom Field about 25 km away. Encouraging similarities are found both in the magnitude and shape of the measured profiles, though differences are also found. Some differences may be explained by characteristics of the measurement techniques. The relative strengths and weaknesses of the methods, and limitations imposed by the radar, will be discussed.
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.
Abstract
The authors examine measurements of boundary layer height z i and entrainment zone thickness observed with two lidars and with a radar wind profiler during the Flatland96 Lidars in Flat Terrain experiment. Lidar backscatter is proportional to aerosol content (and some molecular scatter) in the boundary layer, and wind profiler backscatter depends on the refractive index structure (moisture gradients and turbulence strength). Although these backscatter mechanisms are very different, good agreement is found in measurements of z i at 1-h resolution. When the dataset is limited to daytime convective conditions (times between 1000 and 1700 LT), correlation coefficients between the profiler and each lidar are 0.87 and 0.95. Correlation between the two lidars is 0.99. Comparisons of entrainment zone thickness show less agreement, with correlation coefficients of about 0.6 between the profiler and lidars and 0.8 between the two lidars. The lidar measurements of z i make use of coefficients of a Haar continuous wavelet transform of the backscatter profile. The wind profiler measurements use a standard technique. The wavelet transform technique is shown to provide consistent results with lidar data at 1-s time resolution.
Abstract
The authors examine measurements of boundary layer height z i and entrainment zone thickness observed with two lidars and with a radar wind profiler during the Flatland96 Lidars in Flat Terrain experiment. Lidar backscatter is proportional to aerosol content (and some molecular scatter) in the boundary layer, and wind profiler backscatter depends on the refractive index structure (moisture gradients and turbulence strength). Although these backscatter mechanisms are very different, good agreement is found in measurements of z i at 1-h resolution. When the dataset is limited to daytime convective conditions (times between 1000 and 1700 LT), correlation coefficients between the profiler and each lidar are 0.87 and 0.95. Correlation between the two lidars is 0.99. Comparisons of entrainment zone thickness show less agreement, with correlation coefficients of about 0.6 between the profiler and lidars and 0.8 between the two lidars. The lidar measurements of z i make use of coefficients of a Haar continuous wavelet transform of the backscatter profile. The wind profiler measurements use a standard technique. The wavelet transform technique is shown to provide consistent results with lidar data at 1-s time resolution.
Abstract
A network of three boundary layer radar wind profilers is used to study characteristics of mountain waves and rotors and to explore the utility of such a network. The data employed were collected as part of the Terrain-Induced Rotor Experiment (T-REX), which took place in Owens Valley, California, in early 2006. The wind profilers provide a continuous time––height representation of wave and rotor structure. During intensive observing period 3 (IOP 3), the profiler network was positioned in an L-shaped configuration, capturing key features of the mountain waves and rotor, including the boundary layer vortex sheet (or shear layer), turbulence within this shear layer, the classical lower turbulence zone (LTZ), and wave motion above the LTZ. Observed features were found to be in good agreement with recent high-resolution numerical simulations. Using the wind profiler with superior time resolution (Multiple Antenna Profiler Radar), a series of updraft––downdraft couplets were observed beneath the first downwind wave crest. These are interpreted as signatures of subrotors. Such detailed observations of subrotors are rare, even though subrotors are believed to be a common feature of rotor circulations in Owens Valley. During IOP 6, the network was repositioned to form a line across the valley. A simple algorithm was used to determine the amplitude, wavelength, and phase of the primary wave over the valley and to observe their changes over time and height. In the IOP-6 case, the wavelength increased over time, the phase indicated an eastward-shifting wave crest, and the amplitude increased with height and also varied over time.
Abstract
A network of three boundary layer radar wind profilers is used to study characteristics of mountain waves and rotors and to explore the utility of such a network. The data employed were collected as part of the Terrain-Induced Rotor Experiment (T-REX), which took place in Owens Valley, California, in early 2006. The wind profilers provide a continuous time––height representation of wave and rotor structure. During intensive observing period 3 (IOP 3), the profiler network was positioned in an L-shaped configuration, capturing key features of the mountain waves and rotor, including the boundary layer vortex sheet (or shear layer), turbulence within this shear layer, the classical lower turbulence zone (LTZ), and wave motion above the LTZ. Observed features were found to be in good agreement with recent high-resolution numerical simulations. Using the wind profiler with superior time resolution (Multiple Antenna Profiler Radar), a series of updraft––downdraft couplets were observed beneath the first downwind wave crest. These are interpreted as signatures of subrotors. Such detailed observations of subrotors are rare, even though subrotors are believed to be a common feature of rotor circulations in Owens Valley. During IOP 6, the network was repositioned to form a line across the valley. A simple algorithm was used to determine the amplitude, wavelength, and phase of the primary wave over the valley and to observe their changes over time and height. In the IOP-6 case, the wavelength increased over time, the phase indicated an eastward-shifting wave crest, and the amplitude increased with height and also varied over time.
In October 1993, the King Air research aircraft of the University of Wyoming visited McGill University in Montreal, Canada. As part of a National Science Foundation–sponsored educational initiative, graduate students in McGill's Department of Atmospheric and Oceanic Sciences planned and executed research flights using the King Air. This article describes the project, student preparations, and research flights, and considers the benefits of the initiative. Some thoughts are offered on what was learned and on ways to improve such a project in the future.
In October 1993, the King Air research aircraft of the University of Wyoming visited McGill University in Montreal, Canada. As part of a National Science Foundation–sponsored educational initiative, graduate students in McGill's Department of Atmospheric and Oceanic Sciences planned and executed research flights using the King Air. This article describes the project, student preparations, and research flights, and considers the benefits of the initiative. Some thoughts are offered on what was learned and on ways to improve such a project in the future.
Abstract
Boundary layer wind profilers are increasingly being used in applications that require high-quality, rapidly updated winds. An example of this type of application is an airport wind hazard warning system. Wind shear can be a hazard to flight operations and is also associated with the production of turbulence. A method for calculating wind and wind shear using a linear wind field assumption is presented. This method, applied to four- or five-beam profilers, allows for the explicit accounting of the measurable shear terms. An error analysis demonstrates why some shears are more readily estimated than others, and the expected magnitudes of the variance for the wind and wind shear estimates are given. A method for computing a quality control index, or confidence, for the calculated wind is also presented. This confidence calculation is based on an assessment of the validity of the assumptions made in the calculations. Confidence values can be used as a quality control metric for the calculated wind and can also be used in generating a confidence-weighted average wind value from the rapid update values. Results are presented that show that errors in the wind estimates are reduced after removing values with low confidence. The wind and confidence methods are implemented in the NCAR Wind and Confidence Algorithm (NWCA), and have been used with the NCAR Improved Moments Algorithm (NIMA) method for calculating moments and associated moment confidence from Doppler spectra. However, NWCA may be used with any moment algorithm that also computes a first moment confidence. For example, a very simple confidence algorithm can be defined in terms of the signal-to-noise ratio.
Abstract
Boundary layer wind profilers are increasingly being used in applications that require high-quality, rapidly updated winds. An example of this type of application is an airport wind hazard warning system. Wind shear can be a hazard to flight operations and is also associated with the production of turbulence. A method for calculating wind and wind shear using a linear wind field assumption is presented. This method, applied to four- or five-beam profilers, allows for the explicit accounting of the measurable shear terms. An error analysis demonstrates why some shears are more readily estimated than others, and the expected magnitudes of the variance for the wind and wind shear estimates are given. A method for computing a quality control index, or confidence, for the calculated wind is also presented. This confidence calculation is based on an assessment of the validity of the assumptions made in the calculations. Confidence values can be used as a quality control metric for the calculated wind and can also be used in generating a confidence-weighted average wind value from the rapid update values. Results are presented that show that errors in the wind estimates are reduced after removing values with low confidence. The wind and confidence methods are implemented in the NCAR Wind and Confidence Algorithm (NWCA), and have been used with the NCAR Improved Moments Algorithm (NIMA) method for calculating moments and associated moment confidence from Doppler spectra. However, NWCA may be used with any moment algorithm that also computes a first moment confidence. For example, a very simple confidence algorithm can be defined in terms of the signal-to-noise ratio.
The authors describe and present early results from the July–August 1996 Lidars in Flat Terrain (LIFT) experiment. LIFT was a boundary layer experiment that made use of recently developed Doppler, aerosol backscatter, and ozone lidars, along with radars and surface instrumentation, to study the structure and evolution of the convective boundary layer over the very flat terrain of central Illinois. Scientific goals include measurement of fluxes of heat, moisture, and momentum; vertical velocity statistics; study of entrainment and boundary layer height; and observation of organized coherent structures. The data collected will also be used to evaluate the performance of these new lidars and compare measurements of velocity and boundary layer height to those obtained from nearby radar wind profilers. LIFT was a companion to the Flatland96 experiment, described by Angevine et al.
The authors describe and present early results from the July–August 1996 Lidars in Flat Terrain (LIFT) experiment. LIFT was a boundary layer experiment that made use of recently developed Doppler, aerosol backscatter, and ozone lidars, along with radars and surface instrumentation, to study the structure and evolution of the convective boundary layer over the very flat terrain of central Illinois. Scientific goals include measurement of fluxes of heat, moisture, and momentum; vertical velocity statistics; study of entrainment and boundary layer height; and observation of organized coherent structures. The data collected will also be used to evaluate the performance of these new lidars and compare measurements of velocity and boundary layer height to those obtained from nearby radar wind profilers. LIFT was a companion to the Flatland96 experiment, described by Angevine et al.
