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Abstract
Meteorological instruments which are designed to measure the size spectra of hydrometeors such as hailstones or raindrops or instruments designed to measure cumulative precipitation such as raingages can produce errors if they are not installed and maintained with their sensing surfaces level. Errors from this source have generally been assumed to be negligible or ignored completely in most studies of rainfall and hailfall apparently because levelness is often taken for granted. This paper examines the effects on spectra and integrated precipitation measurements when the wind is blowing and the measuring instrument is out of level. Examples of out-of-level instruments are given based on National Hail Research Experiment hailpad data, data from the Illinois State Water Survey raindrop camera, and government-operated and volunteer-observer raingages. Raingages, for example, that are out of level by 2° in the presence of winds of 10 m s−1 can produce errors on the order of 9%.
Abstract
Meteorological instruments which are designed to measure the size spectra of hydrometeors such as hailstones or raindrops or instruments designed to measure cumulative precipitation such as raingages can produce errors if they are not installed and maintained with their sensing surfaces level. Errors from this source have generally been assumed to be negligible or ignored completely in most studies of rainfall and hailfall apparently because levelness is often taken for granted. This paper examines the effects on spectra and integrated precipitation measurements when the wind is blowing and the measuring instrument is out of level. Examples of out-of-level instruments are given based on National Hail Research Experiment hailpad data, data from the Illinois State Water Survey raindrop camera, and government-operated and volunteer-observer raingages. Raingages, for example, that are out of level by 2° in the presence of winds of 10 m s−1 can produce errors on the order of 9%.
Abstract
Modern Doppler weather radars use clutter filtering to reduce the strength of ground targets and enhance the detection of meteorological echoes. WSR-88D [NEXRAD (Next Generation Radar) and TDWR (Terminal Doppler Weather Radar)] radars, for example, will automatically reject up to 50 dB of signal from stationary targets. Clutter rejection operates on the assumption that the targets to be rejected are really stationary. This assumption is correct for all true ground targets. Unfortunately, these targets are detected by radars using moving antennas. The design of these antennas usually places the feedhorn of the antenna some distance from the center of azimuthal rotation of the antenna. The WSR-88D, for example, has the feedhorn 4.78 m from the center of azimuthal rotation. One consequence of this design feature is that there is a relative motion between the feedhorn and stationary ground targets, which introduces a radial velocity that depends upon the distance from the feedhorn to axis of rotation, the azimuthal rotation rate, and the relative angle between the mainlobe direction and the target. Consequently, stationary targets can produce sidelobe echoes that have velocities. For example, data from the Lincoln Laboratory S-band FL2 radar show antenna rotation-produced moving sidelobe echo velocities as fast as 3 m s−1; the UND C-band radar produced erroneous velocities as fast as 0.8 m s−1. WSR-88D radars will generate erroneous velocities as large as 2.5 m s−1 for a 30° s−1 scan rate.
Abstract
Modern Doppler weather radars use clutter filtering to reduce the strength of ground targets and enhance the detection of meteorological echoes. WSR-88D [NEXRAD (Next Generation Radar) and TDWR (Terminal Doppler Weather Radar)] radars, for example, will automatically reject up to 50 dB of signal from stationary targets. Clutter rejection operates on the assumption that the targets to be rejected are really stationary. This assumption is correct for all true ground targets. Unfortunately, these targets are detected by radars using moving antennas. The design of these antennas usually places the feedhorn of the antenna some distance from the center of azimuthal rotation of the antenna. The WSR-88D, for example, has the feedhorn 4.78 m from the center of azimuthal rotation. One consequence of this design feature is that there is a relative motion between the feedhorn and stationary ground targets, which introduces a radial velocity that depends upon the distance from the feedhorn to axis of rotation, the azimuthal rotation rate, and the relative angle between the mainlobe direction and the target. Consequently, stationary targets can produce sidelobe echoes that have velocities. For example, data from the Lincoln Laboratory S-band FL2 radar show antenna rotation-produced moving sidelobe echo velocities as fast as 3 m s−1; the UND C-band radar produced erroneous velocities as fast as 0.8 m s−1. WSR-88D radars will generate erroneous velocities as large as 2.5 m s−1 for a 30° s−1 scan rate.
Abstract
Microbursts continue to pose a serious problem to the aviation industry. Fortunately, Doppler weather radars are capable of detecting microbursts quite successfully. This study gives the results of a comparison of 84 microbursts detected by a pair of C-band Doppler radars near Orlando during the summer of 1991. The study shows that microbursts were detectable at nearly the same locations (average positional difference of 1 km) and times (average time of detection differed by only 23 s) by both radars. The differential wind velocity detected by each of the radars was also quite similar (average difference of only 0.01 m s−1) as were the radar reflectivity factors (average difference was 1 dB). The conclusion from this is that a C-band radar located anywhere near an airport should be fully capable of detecting hazardous wet-microburst events. Attenuation of the C-band signals was never strong enough to make microbursts undetectable. Bemuse all events were wet microbursts (average reflectivity was 47 dBZ) and the maximum reflectivity difference seen for any microburst was only 10 dB, all events would have been much stronger than the minimum detectable reflectivity at the relatively short ranges used in this study. Attenuation might, however, be a problem for the detection of weak gust fronts.
Abstract
Microbursts continue to pose a serious problem to the aviation industry. Fortunately, Doppler weather radars are capable of detecting microbursts quite successfully. This study gives the results of a comparison of 84 microbursts detected by a pair of C-band Doppler radars near Orlando during the summer of 1991. The study shows that microbursts were detectable at nearly the same locations (average positional difference of 1 km) and times (average time of detection differed by only 23 s) by both radars. The differential wind velocity detected by each of the radars was also quite similar (average difference of only 0.01 m s−1) as were the radar reflectivity factors (average difference was 1 dB). The conclusion from this is that a C-band radar located anywhere near an airport should be fully capable of detecting hazardous wet-microburst events. Attenuation of the C-band signals was never strong enough to make microbursts undetectable. Bemuse all events were wet microbursts (average reflectivity was 47 dBZ) and the maximum reflectivity difference seen for any microburst was only 10 dB, all events would have been much stronger than the minimum detectable reflectivity at the relatively short ranges used in this study. Attenuation might, however, be a problem for the detection of weak gust fronts.
Abstract
Microburst rotation can be determined by measuring the difference in azimuths between the maximum approaching and maximum receding velocity centers on a Doppler radar. Nonrotating microbursts would have these centers exactly along the same radial from the radar. Microbursts rotating clockwise would have the approaching center clockwise of the receding center, and vise versa. In the fist part of this study the authors develop the relationships between the uniform wind, source strength, and rotational strength using potential flow theory and apply this to simulating real microbursts. In the second part the authors give observations of microburst rotation based on measurements of 908 microbursts made near Orlando, Florida, during 1992. While most microbursts had little rotation, 55.4% rotated cyclonically. The average tangential velocity of the rotational component was 1.1 m s−1; 5% had rotations equal to or greater than 2.5 m s−1. This may have significant implications for aviation. Finally, microburst strength measurements are compared with velocity shear and F factors for the 908 microbursts.
Abstract
Microburst rotation can be determined by measuring the difference in azimuths between the maximum approaching and maximum receding velocity centers on a Doppler radar. Nonrotating microbursts would have these centers exactly along the same radial from the radar. Microbursts rotating clockwise would have the approaching center clockwise of the receding center, and vise versa. In the fist part of this study the authors develop the relationships between the uniform wind, source strength, and rotational strength using potential flow theory and apply this to simulating real microbursts. In the second part the authors give observations of microburst rotation based on measurements of 908 microbursts made near Orlando, Florida, during 1992. While most microbursts had little rotation, 55.4% rotated cyclonically. The average tangential velocity of the rotational component was 1.1 m s−1; 5% had rotations equal to or greater than 2.5 m s−1. This may have significant implications for aviation. Finally, microburst strength measurements are compared with velocity shear and F factors for the 908 microbursts.
Abstract
The detection of hail with a dual-wavelength radar system can succeed only when the two essentially independent radars used are correctly calibrated, when attenuation is correctly handled, and when the radars sample the same volume in space. The primary point of this paper is to examine the effects of mismatched antenna beam patterns on dual-wavelength processing. We examine and develop techniques to handle calibration problems, range differences, scan-rate-dependent pointing errors, and attenuation. We also develop a technique (using a ground target) to determine the antenna beam patterns of the two antennas and use these to simulate numerically the effects of mismatched antenna beam patterns on dual-wavelength hail signals. With the National Center for Atmospheric Research CP-2 (10.7 cm wavelength) and M33 (3.2 cm wavelength) dual-wavelength radar system, mismatched beam patterns produce numerous erroneous hail signals of large amplitude. Beam patterns must be well matched to avoid producing erroneous hail signals. Mismatched beam patterns may have contributed to erroneous interpretations in several studies using dual-wavelength data.
Abstract
The detection of hail with a dual-wavelength radar system can succeed only when the two essentially independent radars used are correctly calibrated, when attenuation is correctly handled, and when the radars sample the same volume in space. The primary point of this paper is to examine the effects of mismatched antenna beam patterns on dual-wavelength processing. We examine and develop techniques to handle calibration problems, range differences, scan-rate-dependent pointing errors, and attenuation. We also develop a technique (using a ground target) to determine the antenna beam patterns of the two antennas and use these to simulate numerically the effects of mismatched antenna beam patterns on dual-wavelength hail signals. With the National Center for Atmospheric Research CP-2 (10.7 cm wavelength) and M33 (3.2 cm wavelength) dual-wavelength radar system, mismatched beam patterns produce numerous erroneous hail signals of large amplitude. Beam patterns must be well matched to avoid producing erroneous hail signals. Mismatched beam patterns may have contributed to erroneous interpretations in several studies using dual-wavelength data.
Abstract
In using a dual-wavelength radar system to detect hail, erroneous positive hail signals can result because of the stronger attenuation of the shorter wavelength radar beam. We present a simple technique to correct for attenuation in dual-wavelength analyses. The technique makes use of an attenuation-reflectivity relationship of the form, A = CZx p , where Zx is the S-band reflectivity, C is a coefficient which is determined on a ray-by-ray basis, and p is the exponent, which is assumed to be a constant. In situations where rays of radar data contain a mixture of rain and hail, the attenuation-correction scheme can erroneously apportion more of the attenuation to hail regions rather than to rain regions. The scheme is modified to account for such situations.
Abstract
In using a dual-wavelength radar system to detect hail, erroneous positive hail signals can result because of the stronger attenuation of the shorter wavelength radar beam. We present a simple technique to correct for attenuation in dual-wavelength analyses. The technique makes use of an attenuation-reflectivity relationship of the form, A = CZx p , where Zx is the S-band reflectivity, C is a coefficient which is determined on a ray-by-ray basis, and p is the exponent, which is assumed to be a constant. In situations where rays of radar data contain a mixture of rain and hail, the attenuation-correction scheme can erroneously apportion more of the attenuation to hail regions rather than to rain regions. The scheme is modified to account for such situations.
Abstract
Radar data collected during the seeding experiment of the National Hail Research Experiment are used in a search for possible effects of seeding. Two types of variables, denoted by P and Q, are defined as daily integrals of reflectivity and areas of reflectivity above a given threshold. These and other radar variables are examined for correlation with hailfall at the ground and for seeding effect. Though several variables are closely associated with the occurrence of hail in the network, according to the present sample, none is highly correlated with the amount of hail. A method for measuring hailfall by radar recently used in Switzerland with apparently good results was not successful when applied to the Colorado area.
Ten radar variables were tested for seeding effect by comparing their values on seed and control days. Both the Student's t-test and the Wilcoxon-Mann-Whitney test were employed and gave comparable results. No variables tested showed a difference between seed and control days that was significant at the 10% level. An examination of regressions developed between two adjacent areas (one of which was expected to be much more strongly affected by seeding than the other) also failed to detect a statistically significant difference between seed and control days.
Abstract
Radar data collected during the seeding experiment of the National Hail Research Experiment are used in a search for possible effects of seeding. Two types of variables, denoted by P and Q, are defined as daily integrals of reflectivity and areas of reflectivity above a given threshold. These and other radar variables are examined for correlation with hailfall at the ground and for seeding effect. Though several variables are closely associated with the occurrence of hail in the network, according to the present sample, none is highly correlated with the amount of hail. A method for measuring hailfall by radar recently used in Switzerland with apparently good results was not successful when applied to the Colorado area.
Ten radar variables were tested for seeding effect by comparing their values on seed and control days. Both the Student's t-test and the Wilcoxon-Mann-Whitney test were employed and gave comparable results. No variables tested showed a difference between seed and control days that was significant at the 10% level. An examination of regressions developed between two adjacent areas (one of which was expected to be much more strongly affected by seeding than the other) also failed to detect a statistically significant difference between seed and control days.
