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- Author or Editor: Stephen A. Cohn x
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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
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
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.
