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Abstract
C-band dual-linear polarization radar observables are simulated from disdrometer measurements in rainfall and from gamma-model raindrop-size distributions. It is shown that rainfall is clustered in certain sections of planes formed by selected pairs of radar observables, such as (Z H , Z DR), (Z H , Z DP), (Z H , K DP), and (ρ, Z DR). Measurements lying outside these rainfall cluster regions can be interpreted as being from ice- and mixed-phase hydrometeors. Relationships for estimating rainfall rate R from radar measurements of Z H , K DP, and (Z H , Z DR) are derived, and the latter is shown to produce the best results. An important problem for C-band measurements in the presence of rainfall is attenuation. For this purpose, relationships are derived for estimating specific attenuation and specific differential attenuation from the specific differential phase. The backscattering differential phase shift, which is a potential source of error in estimating K DP, is shown to be very sensitive to the drop-size distribution. The effects of raindrop temperature and canting on the derived relationships are discussed. Finally, S-band relations similar to those obtained at C band are also presented.
Abstract
C-band dual-linear polarization radar observables are simulated from disdrometer measurements in rainfall and from gamma-model raindrop-size distributions. It is shown that rainfall is clustered in certain sections of planes formed by selected pairs of radar observables, such as (Z H , Z DR), (Z H , Z DP), (Z H , K DP), and (ρ, Z DR). Measurements lying outside these rainfall cluster regions can be interpreted as being from ice- and mixed-phase hydrometeors. Relationships for estimating rainfall rate R from radar measurements of Z H , K DP, and (Z H , Z DR) are derived, and the latter is shown to produce the best results. An important problem for C-band measurements in the presence of rainfall is attenuation. For this purpose, relationships are derived for estimating specific attenuation and specific differential attenuation from the specific differential phase. The backscattering differential phase shift, which is a potential source of error in estimating K DP, is shown to be very sensitive to the drop-size distribution. The effects of raindrop temperature and canting on the derived relationships are discussed. Finally, S-band relations similar to those obtained at C band are also presented.
Abstract
Two algorithms are presented for ice crystal classification using 95-GHz polarimetric radar observables and air temperature (T). Both are based on a fuzzy logic scheme. Ice crystals are classified as columnar crystals (CC), planar crystals (PC), mixtures of PC and small- to medium-sized aggregates and/or lightly to moderately rimed PC (PSAR), medium- to large-sized aggregates of PC, or densely rimed PC, or graupel-like snow or small lumpy graupel (PLARG), and graupel larger than about 2 mm (G). The 1D algorithm makes use of Z h , Z dr, LDRhv, and T, while the 2D algorithm incorporates the three radar observables in pairs, (Z dr, Z h ), (LDRhv, Z h ), and (Z dr, LDRhv), plus the temperature T. The range of values for each observable or pair of observables is derived from extensive modeling studies conducted earlier. The algorithms are tested using side-looking radar measurements from an aircraft, which was also equipped with particle probes producing simultaneous and nearly collocated shadow images of cloud ice crystals. The classification results from both algorithms agreed very well with the particle images. The two algorithms were in agreement by 89% in one case and 97% in the remaining three cases considered here. The most effective observable in the 1D algorithm was Z dr, and in the 2D algorithm the pair (Z dr, Z h ). LDRhv had negligible effect in the 1D classification algorithm for the cases considered here. The temperature T was mainly effective in separating columnar crystals from the rest. The advantage of the 2D algorithm over the 1D algorithm was that it significantly reduced the dependence on T in two out of the four cases.
Abstract
Two algorithms are presented for ice crystal classification using 95-GHz polarimetric radar observables and air temperature (T). Both are based on a fuzzy logic scheme. Ice crystals are classified as columnar crystals (CC), planar crystals (PC), mixtures of PC and small- to medium-sized aggregates and/or lightly to moderately rimed PC (PSAR), medium- to large-sized aggregates of PC, or densely rimed PC, or graupel-like snow or small lumpy graupel (PLARG), and graupel larger than about 2 mm (G). The 1D algorithm makes use of Z h , Z dr, LDRhv, and T, while the 2D algorithm incorporates the three radar observables in pairs, (Z dr, Z h ), (LDRhv, Z h ), and (Z dr, LDRhv), plus the temperature T. The range of values for each observable or pair of observables is derived from extensive modeling studies conducted earlier. The algorithms are tested using side-looking radar measurements from an aircraft, which was also equipped with particle probes producing simultaneous and nearly collocated shadow images of cloud ice crystals. The classification results from both algorithms agreed very well with the particle images. The two algorithms were in agreement by 89% in one case and 97% in the remaining three cases considered here. The most effective observable in the 1D algorithm was Z dr, and in the 2D algorithm the pair (Z dr, Z h ). LDRhv had negligible effect in the 1D classification algorithm for the cases considered here. The temperature T was mainly effective in separating columnar crystals from the rest. The advantage of the 2D algorithm over the 1D algorithm was that it significantly reduced the dependence on T in two out of the four cases.
Abstract
Conical graupel is modeled using sphere-cone-oblate spheroidal shapes for the purpose of computing their backscattering properties at a wavelength of 10 cm in terms of the radar polarimetric observables, reflectivity factors, differential reflectivities and circular depolarization ratios. A shape distribution based on in situ measurements is used together with gamma (m=0, 2) size distributions in the computations; both wet and dry graupel are considered. Significant differences in the radar observables between the wet and dry cases are noted and the effects of canting on the radar observables are considered. The implications of these results on the differentiation of hydrometeor phase with radar are discussed.
Abstract
Conical graupel is modeled using sphere-cone-oblate spheroidal shapes for the purpose of computing their backscattering properties at a wavelength of 10 cm in terms of the radar polarimetric observables, reflectivity factors, differential reflectivities and circular depolarization ratios. A shape distribution based on in situ measurements is used together with gamma (m=0, 2) size distributions in the computations; both wet and dry graupel are considered. Significant differences in the radar observables between the wet and dry cases are noted and the effects of canting on the radar observables are considered. The implications of these results on the differentiation of hydrometeor phase with radar are discussed.
Abstract
Empirical relationships for estimating rainfall rate, liquid water content and median volume diameter from radar measurements of reflectivity factor and differential reflectivity are derived from a disdrometer record of a highly variable, heavy rainfall event in central Illinois. Comparisons with relationships representing exponential and gamma model drop-size distributions are made. Simulations, employing these and Z-R relationships for rainfall estimation, are performed. Statistical measures are tabulated for comparing results. These show an excellent agreement between the disdrometer- and radar-derived rainfall parameters when the latter are obtained from the empirical relationships.
Abstract
Empirical relationships for estimating rainfall rate, liquid water content and median volume diameter from radar measurements of reflectivity factor and differential reflectivity are derived from a disdrometer record of a highly variable, heavy rainfall event in central Illinois. Comparisons with relationships representing exponential and gamma model drop-size distributions are made. Simulations, employing these and Z-R relationships for rainfall estimation, are performed. Statistical measures are tabulated for comparing results. These show an excellent agreement between the disdrometer- and radar-derived rainfall parameters when the latter are obtained from the empirical relationships.
Abstract
A technique for the remote sensing of hail with an S-band dual linear polarization radar is described. The method employs a new hail signal HDR , which is derived from disdrometer measurements of raindrop size distributions. Experimental measurements, made in Colorado with the National Center for Atmospheric Research's (NCAR) CP-2 radar system, are used to demonstrate the technique in two major hailstorms.
Abstract
A technique for the remote sensing of hail with an S-band dual linear polarization radar is described. The method employs a new hail signal HDR , which is derived from disdrometer measurements of raindrop size distributions. Experimental measurements, made in Colorado with the National Center for Atmospheric Research's (NCAR) CP-2 radar system, are used to demonstrate the technique in two major hailstorms.
Abstract
Multiparameter radar measurements were made during a heavy rainfall event accompanied by hail in Colorado. Rainfall rates R and accumulation Σ for this event were estimated using S-band specific differential phase K DP, reflectivity factor Z H , and X-band specific attenuation A H3. These estimates were compared with measurements from a ground-based rain gauge. Both R–K DP and R–A H3 relations were in good agreement with the rain gauge data, that is, less than 10% difference in the rainfall accumulations. The R–Z relation produced similar results only when Z H was truncated at 55 dBZ. This study demonstrates the potential of K DP for estimating rainfall rates in severe storms that may have rain-hail mixtures.
Abstract
Multiparameter radar measurements were made during a heavy rainfall event accompanied by hail in Colorado. Rainfall rates R and accumulation Σ for this event were estimated using S-band specific differential phase K DP, reflectivity factor Z H , and X-band specific attenuation A H3. These estimates were compared with measurements from a ground-based rain gauge. Both R–K DP and R–A H3 relations were in good agreement with the rain gauge data, that is, less than 10% difference in the rainfall accumulations. The R–Z relation produced similar results only when Z H was truncated at 55 dBZ. This study demonstrates the potential of K DP for estimating rainfall rates in severe storms that may have rain-hail mixtures.
Abstract
A differential reflectivity radar technique for observing hailstorms is demonstrated using measurements obtained during the 13 June 1984 Denver hailstorm. The hail regions of the storm are identified with the differential reflectivity hail signal. Histograms of ZH and ZDR are generated for different heights in the hail regions and the relative variation of these parameters is also determined. It is observed that due to melting, the mean values of ZH and ZDR increase with decreasing height below the 0°C level (which is around 2.6 km above ground level). Furthermore, at the lower levels ZDR varies between −1 and +2 dB and ZH is generally greater than 50 dBZ. The value of ZH peaks at around 60 dBZ or more when ZDR is in the range −0.5 to 0 dB and 1.5 to 2 dB at 1.5 km and 2 km below the 0°C level, respectively. These and other features of ZH and ZDR are interpreted in terms of the size, shape and fall behavior of the hailstones using the dual wavelength ratio and the linear depolarization ratio radar measurements together with results from Battering computations. The negative ZDR , regions in this storm are inferred to be most likely composed of melting hailstones with sizes predominantly in the 12 to 40 mm range, which fall with their larger dimensions aligned (on the average) vertically. The positive ZDR values greater than 1 dB are concluded to be due to melting hailstones with sizes less than 12 mm, which fall with their larger dimensions aligned (on the average) horizontally.
Abstract
A differential reflectivity radar technique for observing hailstorms is demonstrated using measurements obtained during the 13 June 1984 Denver hailstorm. The hail regions of the storm are identified with the differential reflectivity hail signal. Histograms of ZH and ZDR are generated for different heights in the hail regions and the relative variation of these parameters is also determined. It is observed that due to melting, the mean values of ZH and ZDR increase with decreasing height below the 0°C level (which is around 2.6 km above ground level). Furthermore, at the lower levels ZDR varies between −1 and +2 dB and ZH is generally greater than 50 dBZ. The value of ZH peaks at around 60 dBZ or more when ZDR is in the range −0.5 to 0 dB and 1.5 to 2 dB at 1.5 km and 2 km below the 0°C level, respectively. These and other features of ZH and ZDR are interpreted in terms of the size, shape and fall behavior of the hailstones using the dual wavelength ratio and the linear depolarization ratio radar measurements together with results from Battering computations. The negative ZDR , regions in this storm are inferred to be most likely composed of melting hailstones with sizes predominantly in the 12 to 40 mm range, which fall with their larger dimensions aligned (on the average) vertically. The positive ZDR values greater than 1 dB are concluded to be due to melting hailstones with sizes less than 12 mm, which fall with their larger dimensions aligned (on the average) horizontally.
Abstract
Dual linear polarization weather radars measure as primary observables the mean power P̄ H, and P̄ V, corresponding to returns at horizontal and vertical polarizations, respectively. Differential reflectivity Z DR is defined as the ratio between these two measurements. Under the assumption of an exponential drop-size distribution, characterized by the two parameters N 0 and D 0, it has been shown that Z DR may be used to estimate the median volume diameter D 0, following which the parameter N 0 and, therefore, other drop-size distribution-dependent quantities, may be determined from the horizontal reflectivity Z H.
In this paper the effects of reflectivity gradients, due to the variation of the drop-size distribution within the radar scattering volume, on the radar observables (Z H, Z DR) and derived rainfall rates are examined for radar observations with a stationary antenna. The bias of the estimates, their standard errors, and the optimum receiver response are computed for power law and logarithmic receivers. Finally, for the special case of the square law receiver, the contrasting effects due to either similar or opposing signs of the gradients of the parameters N 0 and D 0 are evaluated.
Abstract
Dual linear polarization weather radars measure as primary observables the mean power P̄ H, and P̄ V, corresponding to returns at horizontal and vertical polarizations, respectively. Differential reflectivity Z DR is defined as the ratio between these two measurements. Under the assumption of an exponential drop-size distribution, characterized by the two parameters N 0 and D 0, it has been shown that Z DR may be used to estimate the median volume diameter D 0, following which the parameter N 0 and, therefore, other drop-size distribution-dependent quantities, may be determined from the horizontal reflectivity Z H.
In this paper the effects of reflectivity gradients, due to the variation of the drop-size distribution within the radar scattering volume, on the radar observables (Z H, Z DR) and derived rainfall rates are examined for radar observations with a stationary antenna. The bias of the estimates, their standard errors, and the optimum receiver response are computed for power law and logarithmic receivers. Finally, for the special case of the square law receiver, the contrasting effects due to either similar or opposing signs of the gradients of the parameters N 0 and D 0 are evaluated.
Abstract
The presence of undetected mixed-phase precipitation or superimposed intense clutter can cause serious errors in the estimation of rainfall rate and other parameters of precipitation occurring in the radar scattering volume. To reduce or avoid these errors it is necessary to distinguish between the rain echo, and that due to other types of precipitation, and between precipitation radar echoes and ground clutter. Multiparameter radar measurements may be exploited for this discrimination. In particular, it is demonstrated that dual-linear polarization measurements may play a major role in this process. Sample radar data are employed to illustrate several different tests to classify radar data: the results refer to comparisons of dual-polarized echoes due to precipitation (rain or mixed-phase event) and land with echoes from land alone. This is illustrated by example through the application of a series of tests on a clutter-contaminated dual-polarized dataset obtained during the May Polarization Experiments (MAYPOLE) 1984 field program in Colorado.
Abstract
The presence of undetected mixed-phase precipitation or superimposed intense clutter can cause serious errors in the estimation of rainfall rate and other parameters of precipitation occurring in the radar scattering volume. To reduce or avoid these errors it is necessary to distinguish between the rain echo, and that due to other types of precipitation, and between precipitation radar echoes and ground clutter. Multiparameter radar measurements may be exploited for this discrimination. In particular, it is demonstrated that dual-linear polarization measurements may play a major role in this process. Sample radar data are employed to illustrate several different tests to classify radar data: the results refer to comparisons of dual-polarized echoes due to precipitation (rain or mixed-phase event) and land with echoes from land alone. This is illustrated by example through the application of a series of tests on a clutter-contaminated dual-polarized dataset obtained during the May Polarization Experiments (MAYPOLE) 1984 field program in Colorado.
Abstract
Bistatic dual-polarization radar parameters at S- and C-band frequencies are simulated for rain and hail. The goal is to determine their potential for discriminating the two precipitation types and for estimating the parameters of an exponential size distribution for hail. Raindrops and hailstones are modeled as oblate spheroids with canting distributions representing their fall behavior. Three hailstone composition models are used to illustrate the effects of melting. Most of the bistatic radar parameters are significantly affected by the amount of liquid water in the hailstones, which may prove useful in determining the melting level from the vertical profiles of these parameters. For single-polarized transmission, such as vertical (v) or horizontal (h) polarization, the four bistatic radar parameters of interest are effective reflectivity factor (Z v or Z h), bistatic-to-backscattering reflectivity ratio (BBRv or BBRh), linear depolarization ratio (LDRv or LDRh), and magnitude of the correlation coefficient between the co- and cross-polarized signals (ρ v or ρ h). If the transmission is dual polarized, then in addition to these two sets of parameters, the bistatic differential reflectivity (Z DR) and the magnitude of the copolarized correlation coefficient (ρ hv) will be available. For low elevation angles of the transmitter and receiver the parameters resulting from h-polarized transmission may be difficult to measure near the bistatic azimuth angle of 90° due to very low signal levels. This may not be an issue for precipitation involving large hailstones.
When parameter pairs such as (LDRv, ρ v) and (BBRv, Z v) are plotted, it is observed that rain and hail tend to cluster in different regions on these planes. This indicates a potential for using bistatic radar parameters for differentiating rain from hail. Similar pairs are possible for h-polarization. Various other combinations of these parameters lead to similar results. The use of more than one pair of parameters and/or several bistatic receiver locations should enhance the level of confidence in the discrimination process. It should also be noted that in some cases there are regions on these planes where rain and hail overlap and discrimination may not always be possible.
Other than Z v and Z h, all of the bistatic radar parameters mentioned above are in the form of ratios. As a result, given an exponential size distribution, N 0 exp(−3.67D/D 0), they depend only on the median volume diameter D 0 and not on N 0. Assuming that the amount of liquid water and ice in the composition of the hailstones are known, the ratio parameters may be used for estimating D 0. However, among these parameters only BBRv and BBRh are negligibly affected by variations in the axial ratio and the mean orientation of hailstones, making them preferable for D 0 estimation. Once D 0 is obtained, N 0 may be estimated using Z v or Z h.
Abstract
Bistatic dual-polarization radar parameters at S- and C-band frequencies are simulated for rain and hail. The goal is to determine their potential for discriminating the two precipitation types and for estimating the parameters of an exponential size distribution for hail. Raindrops and hailstones are modeled as oblate spheroids with canting distributions representing their fall behavior. Three hailstone composition models are used to illustrate the effects of melting. Most of the bistatic radar parameters are significantly affected by the amount of liquid water in the hailstones, which may prove useful in determining the melting level from the vertical profiles of these parameters. For single-polarized transmission, such as vertical (v) or horizontal (h) polarization, the four bistatic radar parameters of interest are effective reflectivity factor (Z v or Z h), bistatic-to-backscattering reflectivity ratio (BBRv or BBRh), linear depolarization ratio (LDRv or LDRh), and magnitude of the correlation coefficient between the co- and cross-polarized signals (ρ v or ρ h). If the transmission is dual polarized, then in addition to these two sets of parameters, the bistatic differential reflectivity (Z DR) and the magnitude of the copolarized correlation coefficient (ρ hv) will be available. For low elevation angles of the transmitter and receiver the parameters resulting from h-polarized transmission may be difficult to measure near the bistatic azimuth angle of 90° due to very low signal levels. This may not be an issue for precipitation involving large hailstones.
When parameter pairs such as (LDRv, ρ v) and (BBRv, Z v) are plotted, it is observed that rain and hail tend to cluster in different regions on these planes. This indicates a potential for using bistatic radar parameters for differentiating rain from hail. Similar pairs are possible for h-polarization. Various other combinations of these parameters lead to similar results. The use of more than one pair of parameters and/or several bistatic receiver locations should enhance the level of confidence in the discrimination process. It should also be noted that in some cases there are regions on these planes where rain and hail overlap and discrimination may not always be possible.
Other than Z v and Z h, all of the bistatic radar parameters mentioned above are in the form of ratios. As a result, given an exponential size distribution, N 0 exp(−3.67D/D 0), they depend only on the median volume diameter D 0 and not on N 0. Assuming that the amount of liquid water and ice in the composition of the hailstones are known, the ratio parameters may be used for estimating D 0. However, among these parameters only BBRv and BBRh are negligibly affected by variations in the axial ratio and the mean orientation of hailstones, making them preferable for D 0 estimation. Once D 0 is obtained, N 0 may be estimated using Z v or Z h.
