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- Author or Editor: Richard G. Strauch x
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Abstract
When long-wavelength radars are used to observe the atmosphere, there are occasions when radar return from a volume of cloud is unexpectedly large relative to that predicted by the classical incoherent scatter from individual cloud droplets. The assumption of incoherence predicts the scattered power to be proportional to the inverse fourth power of the wavelength. The observed weaker wavelength dependence could result from Bragg-coherent scatter from the ensemble of droplets or it could result from an enhancement by the cloud of inhomogeneities in the dielectric constant of the gaseous medium within the cloud. Both mechanisms are discussed and compared with data acquired in a forward-scatter mode by two 3 cm wavelength radars of NOAA's Wave Propagation Laboratory. Observed differences between the in-cloud and out-of-cloud refractive index spectra are discussed and conclusions are suggested.
Abstract
When long-wavelength radars are used to observe the atmosphere, there are occasions when radar return from a volume of cloud is unexpectedly large relative to that predicted by the classical incoherent scatter from individual cloud droplets. The assumption of incoherence predicts the scattered power to be proportional to the inverse fourth power of the wavelength. The observed weaker wavelength dependence could result from Bragg-coherent scatter from the ensemble of droplets or it could result from an enhancement by the cloud of inhomogeneities in the dielectric constant of the gaseous medium within the cloud. Both mechanisms are discussed and compared with data acquired in a forward-scatter mode by two 3 cm wavelength radars of NOAA's Wave Propagation Laboratory. Observed differences between the in-cloud and out-of-cloud refractive index spectra are discussed and conclusions are suggested.
Abstract
It is well known that the presence of ground clutter may severely bias radar measurements of the Doppler shift, particularly with wind profilers undertaking boundary layer measurements. It is shown both qualitatively and quantitatively with simulated data that a simple detrending of the time series data is often sufficient to significantly reduce the clutter problem. Finite impulse response filters are also investigated. Improvements are seen when long records are filtered prior to spectral analysis of the time series. The results are not very sensitive to the width of the filter (within reason) as long as the filter width encompasses the clutter spectrum.
Abstract
It is well known that the presence of ground clutter may severely bias radar measurements of the Doppler shift, particularly with wind profilers undertaking boundary layer measurements. It is shown both qualitatively and quantitatively with simulated data that a simple detrending of the time series data is often sufficient to significantly reduce the clutter problem. Finite impulse response filters are also investigated. Improvements are seen when long records are filtered prior to spectral analysis of the time series. The results are not very sensitive to the width of the filter (within reason) as long as the filter width encompasses the clutter spectrum.
Abstract
A numerical model to simulate radar data is used for testing various estimators of the Doppler shift in Doppler radar echoes. The estimators are the pulse pair and poly-pulse pair algorithms in the correlation domain, a least-squares fitting to the spectral peak of the power spectra, and direct calculations of the moments from periodograms in the spectral domain. Two averaging schemes (a consensus average and a median filter) are also examined for data with poor signal-to-noise ratios. The data processing method used in Doppler radar wind profilers, which operate over a very wide range of signal to noise ratios, is examined in detail. It is shown that the direct moment calculation combined with a consensus averaging technique has the best overall performance for accuracy and the ability to use data with a very low signal-to-noise ratio.
Abstract
A numerical model to simulate radar data is used for testing various estimators of the Doppler shift in Doppler radar echoes. The estimators are the pulse pair and poly-pulse pair algorithms in the correlation domain, a least-squares fitting to the spectral peak of the power spectra, and direct calculations of the moments from periodograms in the spectral domain. Two averaging schemes (a consensus average and a median filter) are also examined for data with poor signal-to-noise ratios. The data processing method used in Doppler radar wind profilers, which operate over a very wide range of signal to noise ratios, is examined in detail. It is shown that the direct moment calculation combined with a consensus averaging technique has the best overall performance for accuracy and the ability to use data with a very low signal-to-noise ratio.
Abstract
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Abstract
Temperature measurements obtained using radiosondes and Radio Acoustic Sounding Systems (RASS) are compared to assess the utility of the RASS technique for meteorological studies. The agreement is generally excellent; rms temperature differences are about 1.0°C for comparisons during a variety of meteorological conditions. Observations taken under ideal circumstances indicate that a precision of about 0.2°C is achievable with the RASS technique. A processor being designed for RASS should allow routine temperature measurements approaching this precision.
Abstract
Temperature measurements obtained using radiosondes and Radio Acoustic Sounding Systems (RASS) are compared to assess the utility of the RASS technique for meteorological studies. The agreement is generally excellent; rms temperature differences are about 1.0°C for comparisons during a variety of meteorological conditions. Observations taken under ideal circumstances indicate that a precision of about 0.2°C is achievable with the RASS technique. A processor being designed for RASS should allow routine temperature measurements approaching this precision.
Abstract
Variance in horizontal and vertical winds are predicted when these components are computed from dual-Doppler velocity measurements combined with terminal velocity estimates and the continuity equation. Errors in horizontal wind magnitude and direction are shown to be functions of wind direction and speed as well as spatial location. Vertical wind could be estimated with errors less than a few meters per second up to altitudes near 14 km over a region 4d × 4d, where 2d is the radar separation. Vertical wind variance at high altitudes is related to accumulation of errors due to the integration of the continuity equation. The cause of wind variance is assumed to be uncertainty in mean Doppler velocity estimates produced by spectrum broadening mechanisms (e.g., shear, turbulence). Two interpolation methods, used to estimate Doppler velocity at common grid locations, are compared and their contribution to Doppler velocity variance reduction is calculated. Terminal velocity variance has been related to uncertainties in drop-size distributions and reflectivity estimate variance. The methods derived herein are applied to determine the errors in wind speeds calculated from dual-Doppler data.
Abstract
Variance in horizontal and vertical winds are predicted when these components are computed from dual-Doppler velocity measurements combined with terminal velocity estimates and the continuity equation. Errors in horizontal wind magnitude and direction are shown to be functions of wind direction and speed as well as spatial location. Vertical wind could be estimated with errors less than a few meters per second up to altitudes near 14 km over a region 4d × 4d, where 2d is the radar separation. Vertical wind variance at high altitudes is related to accumulation of errors due to the integration of the continuity equation. The cause of wind variance is assumed to be uncertainty in mean Doppler velocity estimates produced by spectrum broadening mechanisms (e.g., shear, turbulence). Two interpolation methods, used to estimate Doppler velocity at common grid locations, are compared and their contribution to Doppler velocity variance reduction is calculated. Terminal velocity variance has been related to uncertainties in drop-size distributions and reflectivity estimate variance. The methods derived herein are applied to determine the errors in wind speeds calculated from dual-Doppler data.
Abstract
The available range resolution of pulsed radar wind profilers is usually limited by bandwidth restrictions. Range imaging (RIM) has recently been developed as a means of mitigating these limitations by operating the wind profilers over a small set of distinct transmitter frequencies. A constrained optimization method can then be used to generate high-resolution maps of the reflectivity field as a function of range. This paper presents a description of how the RIM technique has been recently implemented on the Platteville 915-MHz tropospheric profiler, the first such implementation at UHF. Examples of data collected during a two-part experiment on 10 April 2001 using the Platteville 915-MHz tropospheric profiler are presented. In the first part, an intercomparison was made involving measurements from RIM and standard radar techniques. It is shown that available frequency bandwidth can be very effectively utilized through the RIM processing. In the second part of the experiment, RIM was applied to radar observations collected with a short (0.5 µs) transmit pulse. The resulting data include observations of a thin, persistent scattering layer attributed to a subsidence inversion and billows from a Kelvin–Helmholtz instability. Estimates of the width of the layer were found to be as small as 12 m.
Abstract
The available range resolution of pulsed radar wind profilers is usually limited by bandwidth restrictions. Range imaging (RIM) has recently been developed as a means of mitigating these limitations by operating the wind profilers over a small set of distinct transmitter frequencies. A constrained optimization method can then be used to generate high-resolution maps of the reflectivity field as a function of range. This paper presents a description of how the RIM technique has been recently implemented on the Platteville 915-MHz tropospheric profiler, the first such implementation at UHF. Examples of data collected during a two-part experiment on 10 April 2001 using the Platteville 915-MHz tropospheric profiler are presented. In the first part, an intercomparison was made involving measurements from RIM and standard radar techniques. It is shown that available frequency bandwidth can be very effectively utilized through the RIM processing. In the second part of the experiment, RIM was applied to radar observations collected with a short (0.5 µs) transmit pulse. The resulting data include observations of a thin, persistent scattering layer attributed to a subsidence inversion and billows from a Kelvin–Helmholtz instability. Estimates of the width of the layer were found to be as small as 12 m.
Abstract
Radar wind profilers (RWPs) sense the mean and turbulent motion of the clear air through Doppler shifts induced along several (3–5) upward-looking beams. RWP signals, like all radars signals, are often contaminated. The contamination is clearly evident in the associated Doppler spectra, and automatic routines designed to extract meteorological quantities from these spectra often yield inaccurate results. Much of the observed contamination is due to an aliasing of higher frequency signals into the clear-air portion of the spectrum and a broadening of the spectral contaminants caused by the relatively short time series used to generate Doppler spectra. In the past, this source of contamination could not be avoided because of limitations on the size and speed of RWP processing computers. Today’s computers, however, are able to process larger amounts of data at greatly increased speeds. Here it is shown how standard and well-known spectral processing methods can be applied to significantly longer time series to reduce contamination in the radar spectra and thereby improve the accuracy and the reliability of meteorological products derived from RWP systems. In particular, spectral processing methods to identify and remove contamination that is often aliased into the clear-air portion of the spectrum are considered. Optimal techniques for combining multiple spectra to produce averaged spectra are also discussed.
Abstract
Radar wind profilers (RWPs) sense the mean and turbulent motion of the clear air through Doppler shifts induced along several (3–5) upward-looking beams. RWP signals, like all radars signals, are often contaminated. The contamination is clearly evident in the associated Doppler spectra, and automatic routines designed to extract meteorological quantities from these spectra often yield inaccurate results. Much of the observed contamination is due to an aliasing of higher frequency signals into the clear-air portion of the spectrum and a broadening of the spectral contaminants caused by the relatively short time series used to generate Doppler spectra. In the past, this source of contamination could not be avoided because of limitations on the size and speed of RWP processing computers. Today’s computers, however, are able to process larger amounts of data at greatly increased speeds. Here it is shown how standard and well-known spectral processing methods can be applied to significantly longer time series to reduce contamination in the radar spectra and thereby improve the accuracy and the reliability of meteorological products derived from RWP systems. In particular, spectral processing methods to identify and remove contamination that is often aliased into the clear-air portion of the spectrum are considered. Optimal techniques for combining multiple spectra to produce averaged spectra are also discussed.
Abstract
An algorithm to compute the magnitude of humidity gradient profiles from the measurements of the zeroth, first, and second moments of wind profiling radar (WPR) Doppler spectra was developed and tested. The algorithm extends the National Oceanic and Atmospheric Administration (NOAA)/Environmental Technology Laboratory (ETL) Advanced Signal Processing System (SPS), which provides quality control of radar data in the spectral domain for wind profile retrievals, to include the retrieval of humidity gradient profiles. The algorithm uses a recently developed approximate formula for correcting Doppler spectral widths for the spatial and temporal filtering effects. Data collected by a 3-beam 915-MHz WPR onboard the NOAA research vessel Ronald H. Brown (RHB) and a 5-beam 449-MHz WPR developed at the ETL were used in this study. The two datasets cover vastly different atmospheric conditions, with the 915-MHz shipborne system probing the tropical ocean atmosphere and the 449-MHz WPR probing continental winter upslope icing storm in the Colorado Front Range. Vaisala radiosonde measurements of humidity and temperature profiles on board the RHB and the standard National Weather Service (NWS) radiosonde measurements at Stapleton, Colorado, were used for comparisons. The cases chosen represent typical atmospheric conditions and not special atmospheric situations. Results show that using SPS-obtained measurements of the zeroth, first, and second spectral moments provide radar-obtained humidity gradient profiles up to 3 km AGL.
Abstract
An algorithm to compute the magnitude of humidity gradient profiles from the measurements of the zeroth, first, and second moments of wind profiling radar (WPR) Doppler spectra was developed and tested. The algorithm extends the National Oceanic and Atmospheric Administration (NOAA)/Environmental Technology Laboratory (ETL) Advanced Signal Processing System (SPS), which provides quality control of radar data in the spectral domain for wind profile retrievals, to include the retrieval of humidity gradient profiles. The algorithm uses a recently developed approximate formula for correcting Doppler spectral widths for the spatial and temporal filtering effects. Data collected by a 3-beam 915-MHz WPR onboard the NOAA research vessel Ronald H. Brown (RHB) and a 5-beam 449-MHz WPR developed at the ETL were used in this study. The two datasets cover vastly different atmospheric conditions, with the 915-MHz shipborne system probing the tropical ocean atmosphere and the 449-MHz WPR probing continental winter upslope icing storm in the Colorado Front Range. Vaisala radiosonde measurements of humidity and temperature profiles on board the RHB and the standard National Weather Service (NWS) radiosonde measurements at Stapleton, Colorado, were used for comparisons. The cases chosen represent typical atmospheric conditions and not special atmospheric situations. Results show that using SPS-obtained measurements of the zeroth, first, and second spectral moments provide radar-obtained humidity gradient profiles up to 3 km AGL.
