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- Author or Editor: B. L. Weber x
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Abstract
Comparisons of horizontal wind component measurements from a rawinsonde and a UHF wind profiler radar, obtained twice daily over a period of nearly 2 years (from mid-January 1984 through October 1985), showed differences with a standard deviation of about 2.5 m s−1, mainly due to meteorological variability in the winds.
Abstract
Comparisons of horizontal wind component measurements from a rawinsonde and a UHF wind profiler radar, obtained twice daily over a period of nearly 2 years (from mid-January 1984 through October 1985), showed differences with a standard deviation of about 2.5 m s−1, mainly due to meteorological variability in the winds.
Abstract
The distribution of backscattered power was computed for three wind profilers in the Colorado network that operated at 50,405, and 915 MHz. Since the backscattered power is a function of fluctuations in the refractivity index, this power distribution also gives the relative distribution of C 2 n . Similar distributions were found for all three frequencies in the lower troposphere where the atmosphere is often well mixed. But near and above the tropopause the distributions for the three frequencies different, probably because they responded to different processes in the atmosphere.
Abstract
The distribution of backscattered power was computed for three wind profilers in the Colorado network that operated at 50,405, and 915 MHz. Since the backscattered power is a function of fluctuations in the refractivity index, this power distribution also gives the relative distribution of C 2 n . Similar distributions were found for all three frequencies in the lower troposphere where the atmosphere is often well mixed. But near and above the tropopause the distributions for the three frequencies different, probably because they responded to different processes in the atmosphere.
Abstract
A new method for estimating winds and radio acoustic sounding system temperatures from radar Doppler measurements for the new NOAA wind profilers is described. This method emphasizes the quality of 6-min measurements prior to the computation of hourly averages. Compared with the older method currently being used, this new method provides measurements exhibiting better consistency and more complete coverage over height and time. Furthermore, it corrects aliased measurements.
Abstract
A new method for estimating winds and radio acoustic sounding system temperatures from radar Doppler measurements for the new NOAA wind profilers is described. This method emphasizes the quality of 6-min measurements prior to the computation of hourly averages. Compared with the older method currently being used, this new method provides measurements exhibiting better consistency and more complete coverage over height and time. Furthermore, it corrects aliased measurements.
Abstract
Vertical velocities were observed during the month of June 1990 with two side-by-side wind profilers at Platteville, Colorado. Many of the observations reveal strong wave motion, probably mountain lee waves, that sometimes caused vertical velocity changes of several meters per second in less than an hour. It is demonstrated that, under these conditions, hourly averages cannot always be used to accurately account for the effects of vertical motion on the profiler measurements. It is also shown that it is impossible to accurately remove the effects of vertical motion from the horizontal wind component estimates when the horizontal scale of vertical-motion variability is comparable to the horizontal separation distance between antenna beams. The Radio Acoustic Sounding System (RASS) temperature measurements, however, are not affected by the small spatial scales because those measurements are made on the same vertical antenna beam as the vertical velocity measurements. Nevertheless, it is important that these temperature measurements be made simultaneously with vertical velocity measurements so that valid vertical velocity corrections can be made.
Abstract
Vertical velocities were observed during the month of June 1990 with two side-by-side wind profilers at Platteville, Colorado. Many of the observations reveal strong wave motion, probably mountain lee waves, that sometimes caused vertical velocity changes of several meters per second in less than an hour. It is demonstrated that, under these conditions, hourly averages cannot always be used to accurately account for the effects of vertical motion on the profiler measurements. It is also shown that it is impossible to accurately remove the effects of vertical motion from the horizontal wind component estimates when the horizontal scale of vertical-motion variability is comparable to the horizontal separation distance between antenna beams. The Radio Acoustic Sounding System (RASS) temperature measurements, however, are not affected by the small spatial scales because those measurements are made on the same vertical antenna beam as the vertical velocity measurements. Nevertheless, it is important that these temperature measurements be made simultaneously with vertical velocity measurements so that valid vertical velocity corrections can be made.
Abstract
The maximum height performance of the 50, 405 nd 915 MHz Colorado wind profiles is computed from the wind profiler database. Results show that even though the 50 MHz profiler has the largest seasonal variation in the maximum height coverage, it also has the greatest height coverage. In addition, it also has a greater increase in height for the same increase in sensitivity. On the basis of thew measurements we predict the height coverage of the 405 MHz wind profiler for the proposed wind profiler network.
Abstract
The maximum height performance of the 50, 405 nd 915 MHz Colorado wind profiles is computed from the wind profiler database. Results show that even though the 50 MHz profiler has the largest seasonal variation in the maximum height coverage, it also has the greatest height coverage. In addition, it also has a greater increase in height for the same increase in sensitivity. On the basis of thew measurements we predict the height coverage of the 405 MHz wind profiler for the proposed wind profiler network.
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.
Abstract
Horizontal winds in the presence of precipitation were measured routinely with a UHF (405 MHz) Wind Profiler. The profiler had five beam-pointing positions so independent measurements of horizontal winds could be compared to determine relative accuracy and precision. Large precipitation fall speeds are shown to cause very large errors (on the order of tens of meters per second) in the horizontal wind estimates when those fall speeds are not properly included in the estimates. But when the precipitation fall speeds are properly included, the errors are much smaller (2–4 m s−1), approaching those of clear air (1 m s−1). The decrease in the precision in precipitation is attributed largely to horizontal nonuniformity in precipitation from one antenna beam to another. A 4- or 5-beam profiler can detect conditions of horizontal inhomogeneity by virtue of its ability to make independent measurements of the winds from horizontally separated scattering volumes.
Abstract
Horizontal winds in the presence of precipitation were measured routinely with a UHF (405 MHz) Wind Profiler. The profiler had five beam-pointing positions so independent measurements of horizontal winds could be compared to determine relative accuracy and precision. Large precipitation fall speeds are shown to cause very large errors (on the order of tens of meters per second) in the horizontal wind estimates when those fall speeds are not properly included in the estimates. But when the precipitation fall speeds are properly included, the errors are much smaller (2–4 m s−1), approaching those of clear air (1 m s−1). The decrease in the precision in precipitation is attributed largely to horizontal nonuniformity in precipitation from one antenna beam to another. A 4- or 5-beam profiler can detect conditions of horizontal inhomogeneity by virtue of its ability to make independent measurements of the winds from horizontally separated scattering volumes.
Abstract
The first wind profiler for a demonstration network of wind profilers recently passed the milestone of 300 h of continuous operation. The horizontal wind component measurements taken during that period are compared with the WPL Platteville wind profiler and the NWS Denver rawinsonde. The differences between the network and WPL wind profilers have standard deviations of 2.30 m s−1 and 2.16 m s−1 for the u- and v-components, respectively. However, the WPL wind profiler ignores vertical velocity, whereas the network radar measures it and removes its effects from the u- and v-component measurements. The differences between the network wind profiler and the NWS rawinsonde (separated spatially by about 50 km) have standard deviations of 3.65 m s−1 and 3.06 m s−1 for the u- and v-components, respectively. These results are similar to those found in earlier comparison studies. Finally, the new network wind profiler demonstrates excellent sensitivity, consistently reporting measurements at all heights msl from 2 to nearly 18 km with very few outages.
Abstract
The first wind profiler for a demonstration network of wind profilers recently passed the milestone of 300 h of continuous operation. The horizontal wind component measurements taken during that period are compared with the WPL Platteville wind profiler and the NWS Denver rawinsonde. The differences between the network and WPL wind profilers have standard deviations of 2.30 m s−1 and 2.16 m s−1 for the u- and v-components, respectively. However, the WPL wind profiler ignores vertical velocity, whereas the network radar measures it and removes its effects from the u- and v-component measurements. The differences between the network wind profiler and the NWS rawinsonde (separated spatially by about 50 km) have standard deviations of 3.65 m s−1 and 3.06 m s−1 for the u- and v-components, respectively. These results are similar to those found in earlier comparison studies. Finally, the new network wind profiler demonstrates excellent sensitivity, consistently reporting measurements at all heights msl from 2 to nearly 18 km with very few outages.
Abstract
Two independent wind profiles were measured every hour during February 1986 with a five-beam, UHF (405 MHz) wind Profiler at Platteville, Colorado. Our analysis of the horizontal wind components over all heights for the entire month gave a standard deviation of about 1.3 m s−1 for the measurement errors one can expect for three-beam Profilers in clear air. This study demonstrated that it is important to include the effects of large vertical motion (caused by gravity waves or precipitation in the horizontal wind component measurements. These vertical motions were large enough to raise the error in the horizontal wind components to 1.7 m s−1 in two-beam configurations where no corrections are made for the vertical motion.
Abstract
Two independent wind profiles were measured every hour during February 1986 with a five-beam, UHF (405 MHz) wind Profiler at Platteville, Colorado. Our analysis of the horizontal wind components over all heights for the entire month gave a standard deviation of about 1.3 m s−1 for the measurement errors one can expect for three-beam Profilers in clear air. This study demonstrated that it is important to include the effects of large vertical motion (caused by gravity waves or precipitation in the horizontal wind component measurements. These vertical motions were large enough to raise the error in the horizontal wind components to 1.7 m s−1 in two-beam configurations where no corrections are made for the vertical motion.
