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Abstract
Realistic aggregate snowflake models and experimental snowflake size distribution parameters are used to derive X-band power-law relations between the equivalent radar reflectivity factor Ze and the liquid equivalent snowfall precipitation rate S (Ze = ASB ). There is significant variability in coefficients of these relations caused by uncertainties in the snowflake bulk densities (as defined by the mass–size relationships), fall velocities, and particle size distribution parameters. The variability in snowflake parameters results in differing Ze –S relations that provide more than a factor of 2 difference in precipitation rate and liquid equivalent accumulation estimates for typical reflectivity values observed in snowfall (∼20–30 dBZ). Characteristic values of the exponent B in the derived for dry snowfall relations were generally in the range 1.3–1.55 (when Ze is in mm6 m−3 and S is in mm h−1). The coefficient A exhibited stronger variability and varied in the range from about 30 (for aircraft-based size distributions and smaller density particles) to about 140 (for surface-based size distributions). The non-Rayleigh scattering effects at X band result in diminishing of both A and B, as compared to the relations for longer wavelength radars. The snowflake shape and orientation also influences its backscatter properties, but to a lesser extent compared to the particle bulk density. The derived relations were primarily obtained for snowfall consisting of dry aggregate snowflakes. They were applied to the X-band radar measurements during observations of wintertime storms. For approximately collocated measurements, the in situ estimates of snowfall accumulations were generally within the range of radar-derived values when the coefficient A was around 100–120.
Abstract
Realistic aggregate snowflake models and experimental snowflake size distribution parameters are used to derive X-band power-law relations between the equivalent radar reflectivity factor Ze and the liquid equivalent snowfall precipitation rate S (Ze = ASB ). There is significant variability in coefficients of these relations caused by uncertainties in the snowflake bulk densities (as defined by the mass–size relationships), fall velocities, and particle size distribution parameters. The variability in snowflake parameters results in differing Ze –S relations that provide more than a factor of 2 difference in precipitation rate and liquid equivalent accumulation estimates for typical reflectivity values observed in snowfall (∼20–30 dBZ). Characteristic values of the exponent B in the derived for dry snowfall relations were generally in the range 1.3–1.55 (when Ze is in mm6 m−3 and S is in mm h−1). The coefficient A exhibited stronger variability and varied in the range from about 30 (for aircraft-based size distributions and smaller density particles) to about 140 (for surface-based size distributions). The non-Rayleigh scattering effects at X band result in diminishing of both A and B, as compared to the relations for longer wavelength radars. The snowflake shape and orientation also influences its backscatter properties, but to a lesser extent compared to the particle bulk density. The derived relations were primarily obtained for snowfall consisting of dry aggregate snowflakes. They were applied to the X-band radar measurements during observations of wintertime storms. For approximately collocated measurements, the in situ estimates of snowfall accumulations were generally within the range of radar-derived values when the coefficient A was around 100–120.
Abstract
An attenuation-based method to retrieve vertical profiles of rainfall rate from vertically pointing Ka-band radar measurements has been refined and adjusted for use with the U.S. Department of Energy’s cloud radars deployed at multiple Atmospheric Radiation Program (ARM) test bed sites. This method takes advantage of the linear relationship between the rainfall rate and the attenuation coefficient, and can account for a priori information about the vertical profile of nonattenuated reflectivity. The retrieval method is applied to a wide variety of rainfall events observed at different ARM sites ranging from stratiform events with low-to-moderate rainfall rates (∼5 mm h−1) to heavy convective rains with rainfall rates approaching 100 mm h−1. The Ka-band attenuation-based retrieval results expressed in both instantaneous rainfall rates and in rainfall accumulations are compared to available surface data and measurements of a scanning C-band precipitation polarimetric radar located near the Darwin, Australia, ARM test bed site. The Ka-band retrievals are found to be in good agreement with the C-band radar estimates, which are based both on conventional radar reflectivity approaches and on polarimetric differential phase shift measurements. Typically, the C-band–Ka-band radar estimate differences are within the expected retrieval uncertainties. The magnitude of the Ka-band rainfall-rate estimate error depends on the retrieval resolution, rain intensity, and uncertainties in the profiles of nonattenuated reflectivity. It is shown that reasonable retrieval accuracies (∼15%–40%) can be achieved for a large dynamic range of observed rainfall rates (4–100 mm h−1) and the effective vertical resolution of about 1 km. The potential enhancements of the Ka-band attenuation-based method by including a priori information on vertical profiles of nonattenuated reflectivity and increasing the height range of the retrievals by using Ka-band polarization measurements are also discussed. The addition of the precipitation products to the suite of ARM hydrometeor retrievals can enhance the overall characterization of the vertical atmospheric column.
Abstract
An attenuation-based method to retrieve vertical profiles of rainfall rate from vertically pointing Ka-band radar measurements has been refined and adjusted for use with the U.S. Department of Energy’s cloud radars deployed at multiple Atmospheric Radiation Program (ARM) test bed sites. This method takes advantage of the linear relationship between the rainfall rate and the attenuation coefficient, and can account for a priori information about the vertical profile of nonattenuated reflectivity. The retrieval method is applied to a wide variety of rainfall events observed at different ARM sites ranging from stratiform events with low-to-moderate rainfall rates (∼5 mm h−1) to heavy convective rains with rainfall rates approaching 100 mm h−1. The Ka-band attenuation-based retrieval results expressed in both instantaneous rainfall rates and in rainfall accumulations are compared to available surface data and measurements of a scanning C-band precipitation polarimetric radar located near the Darwin, Australia, ARM test bed site. The Ka-band retrievals are found to be in good agreement with the C-band radar estimates, which are based both on conventional radar reflectivity approaches and on polarimetric differential phase shift measurements. Typically, the C-band–Ka-band radar estimate differences are within the expected retrieval uncertainties. The magnitude of the Ka-band rainfall-rate estimate error depends on the retrieval resolution, rain intensity, and uncertainties in the profiles of nonattenuated reflectivity. It is shown that reasonable retrieval accuracies (∼15%–40%) can be achieved for a large dynamic range of observed rainfall rates (4–100 mm h−1) and the effective vertical resolution of about 1 km. The potential enhancements of the Ka-band attenuation-based method by including a priori information on vertical profiles of nonattenuated reflectivity and increasing the height range of the retrievals by using Ka-band polarization measurements are also discussed. The addition of the precipitation products to the suite of ARM hydrometeor retrievals can enhance the overall characterization of the vertical atmospheric column.
Abstract
A remote sensing method is proposed for the retrievals of vertical profiles of ice cloud microphysical parameters from ground-based measurements of radar reflectivity and Doppler velocity with a vertically pointed cloud radar. This method relates time-averaged Doppler velocities (which are used as a proxy for the reflectivity-weighted particle fall velocities) to particle characteristic sizes such as median or mean. With estimated profiles of particle characteristic size, profiles of cloud ice water content (IWC) are then calculated using reflectivity measurements. The method accounts for the intrinsic correlation between particle sizes and parameters of the fall velocity–size relations. It also accounts for changes of particle bulk density with size. The range of applicability of this method encompasses ice-phase clouds and also mixed-phase clouds that contain liquid drops, which are small compared to ice particles, so the radar signals are dominated by these larger particles. It is, however, limited to the observational situations without strong up- and downdrafts, so the residual of mean vertical air motions is small enough compared to the reflectivity-weighted cloud particle fall velocities. The Doppler-velocity reflectivity method was applied to the data obtained with an 8.6-mm wavelength radar when observing Arctic clouds. Typical retrieval uncertainties are about 35%–40% for particle characteristic size and 60%–70% for IWC, though in some cases IWC uncertainties can be as high as factor of 2 (i.e., −50%, +100%). Comparisons with in situ data for one observational case yielded 25% and 55% differences in retrieved and in situ estimates of characteristic size and IWC, respectively. The results of the microphysical retrievals obtained from the remote sensing method developed here were compared with data obtained from the multisensor technique that utilizes combined radar–IR radiometer measurements. For pure ice-phase layers unobstructed by liquid clouds (i.e., conditions where the multisensor approach is applicable), the relative standard deviations between the results of both remote sensing approaches were about 27% for mean particle size and 38% for IWC, with relative biases of only 5% and 20%, respectively.
Abstract
A remote sensing method is proposed for the retrievals of vertical profiles of ice cloud microphysical parameters from ground-based measurements of radar reflectivity and Doppler velocity with a vertically pointed cloud radar. This method relates time-averaged Doppler velocities (which are used as a proxy for the reflectivity-weighted particle fall velocities) to particle characteristic sizes such as median or mean. With estimated profiles of particle characteristic size, profiles of cloud ice water content (IWC) are then calculated using reflectivity measurements. The method accounts for the intrinsic correlation between particle sizes and parameters of the fall velocity–size relations. It also accounts for changes of particle bulk density with size. The range of applicability of this method encompasses ice-phase clouds and also mixed-phase clouds that contain liquid drops, which are small compared to ice particles, so the radar signals are dominated by these larger particles. It is, however, limited to the observational situations without strong up- and downdrafts, so the residual of mean vertical air motions is small enough compared to the reflectivity-weighted cloud particle fall velocities. The Doppler-velocity reflectivity method was applied to the data obtained with an 8.6-mm wavelength radar when observing Arctic clouds. Typical retrieval uncertainties are about 35%–40% for particle characteristic size and 60%–70% for IWC, though in some cases IWC uncertainties can be as high as factor of 2 (i.e., −50%, +100%). Comparisons with in situ data for one observational case yielded 25% and 55% differences in retrieved and in situ estimates of characteristic size and IWC, respectively. The results of the microphysical retrievals obtained from the remote sensing method developed here were compared with data obtained from the multisensor technique that utilizes combined radar–IR radiometer measurements. For pure ice-phase layers unobstructed by liquid clouds (i.e., conditions where the multisensor approach is applicable), the relative standard deviations between the results of both remote sensing approaches were about 27% for mean particle size and 38% for IWC, with relative biases of only 5% and 20%, respectively.
Abstract
Microphysical data and radar reflectivities (Z e , −15 < Z e < 10 dB) measured from flights during the NASA Tropical Clouds, Convection, Chemistry and Climate field program are used to relate Z e at X and W band to measured ice water content (IWC). Because nearly collocated Z e and IWC were each directly measured, Z e –IWC relationships could be developed directly. Using the particle size distributions and ice particle masses evaluated based on the direct IWC measurements, reflectivity–snowfall rate (Z e –S) relationships were also derived. For −15 < Z e < 10 dB, the relationships herein yield larger IWC and S than given by the retrievals and earlier relationships. The sensitivity of radar reflectivity to particle size distribution and size-dependent mass, shape, and orientation introduces significant uncertainties in retrieved quantities since these factors vary substantially globally. To partially circumvent these uncertainties, a W-band Z e –S relationship is developed by relating four years of global CloudSat reflectivity observations measured immediately above the melting layer to retrieved rain rates at the base of the melting layer. The supporting assumptions are that the water mass flux is constant through the melting layer, that the air temperature is nearly 0°C, and that the retrieved rain rates are well constrained. Where Z e > 10 dB, this Z e –S relationship conforms well to earlier relationships, but for Z e < 10 dB it yields higher IWC and S. Because not all retrieval algorithms estimate either or both IWC and S, the authors use a large aircraft-derived dataset to relate IWC and S. The IWC can then be estimated from S and vice versa.
Abstract
Microphysical data and radar reflectivities (Z e , −15 < Z e < 10 dB) measured from flights during the NASA Tropical Clouds, Convection, Chemistry and Climate field program are used to relate Z e at X and W band to measured ice water content (IWC). Because nearly collocated Z e and IWC were each directly measured, Z e –IWC relationships could be developed directly. Using the particle size distributions and ice particle masses evaluated based on the direct IWC measurements, reflectivity–snowfall rate (Z e –S) relationships were also derived. For −15 < Z e < 10 dB, the relationships herein yield larger IWC and S than given by the retrievals and earlier relationships. The sensitivity of radar reflectivity to particle size distribution and size-dependent mass, shape, and orientation introduces significant uncertainties in retrieved quantities since these factors vary substantially globally. To partially circumvent these uncertainties, a W-band Z e –S relationship is developed by relating four years of global CloudSat reflectivity observations measured immediately above the melting layer to retrieved rain rates at the base of the melting layer. The supporting assumptions are that the water mass flux is constant through the melting layer, that the air temperature is nearly 0°C, and that the retrieved rain rates are well constrained. Where Z e > 10 dB, this Z e –S relationship conforms well to earlier relationships, but for Z e < 10 dB it yields higher IWC and S. Because not all retrieval algorithms estimate either or both IWC and S, the authors use a large aircraft-derived dataset to relate IWC and S. The IWC can then be estimated from S and vice versa.
Atmospheric Ice Particle Shape Estimates from Polarimetric Radar Measurements and In Situ ObservationsAbstract
A remote sensing approach to retrieve the degree of nonsphericity of ice hydrometeors using scanning polarimetric Ka-band radar measurements from a U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program cloud radar operated in an alternate transmission–simultaneous reception mode is introduced. Nonsphericity is characterized by aspect ratios representing the ratios of particle minor-to-major dimensions. The approach is based on the use of a circular depolarization ratio (CDR) proxy reconstructed from differential reflectivity Z DR and copolar correlation coefficient ρ hυ linear polarization measurements. Essentially combining information contained in Z DR and ρ hυ , CDR-based retrievals of aspect ratios are fairly insensitive to hydrometeor orientation if measurements are performed at elevation angles of around 40°–50°. The suggested approach is applied to data collected using the third ARM Mobile Facility (AMF3), deployed to Oliktok Point, Alaska. Aspect ratio retrievals were also performed using Z DR measurements that are more strongly (compared to CDR) influenced by hydrometeor orientation. The results of radar-based retrievals are compared with in situ measurements from the tethered balloon system (TBS)-based video ice particle sampler and the ground-based multiangle snowflake camera. The observed ice hydrometeors were predominantly irregular-shaped ice crystals and aggregates, with aspect ratios varying between approximately 0.3 and 0.8. The retrievals assume that particle bulk density influencing (besides the particle shape) observed polarimetric variables can be deduced from the estimates of particle characteristic size. Uncertainties of CDR-based aspect ratio retrievals are estimated at about 0.1–0.15. Given these uncertainties, radar-based retrievals generally agreed with in situ measurements. The advantages of using the CDR proxy compared to the linear depolarization ratio are discussed.
Abstract
A remote sensing approach to retrieve the degree of nonsphericity of ice hydrometeors using scanning polarimetric Ka-band radar measurements from a U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program cloud radar operated in an alternate transmission–simultaneous reception mode is introduced. Nonsphericity is characterized by aspect ratios representing the ratios of particle minor-to-major dimensions. The approach is based on the use of a circular depolarization ratio (CDR) proxy reconstructed from differential reflectivity Z DR and copolar correlation coefficient ρ hυ linear polarization measurements. Essentially combining information contained in Z DR and ρ hυ , CDR-based retrievals of aspect ratios are fairly insensitive to hydrometeor orientation if measurements are performed at elevation angles of around 40°–50°. The suggested approach is applied to data collected using the third ARM Mobile Facility (AMF3), deployed to Oliktok Point, Alaska. Aspect ratio retrievals were also performed using Z DR measurements that are more strongly (compared to CDR) influenced by hydrometeor orientation. The results of radar-based retrievals are compared with in situ measurements from the tethered balloon system (TBS)-based video ice particle sampler and the ground-based multiangle snowflake camera. The observed ice hydrometeors were predominantly irregular-shaped ice crystals and aggregates, with aspect ratios varying between approximately 0.3 and 0.8. The retrievals assume that particle bulk density influencing (besides the particle shape) observed polarimetric variables can be deduced from the estimates of particle characteristic size. Uncertainties of CDR-based aspect ratio retrievals are estimated at about 0.1–0.15. Given these uncertainties, radar-based retrievals generally agreed with in situ measurements. The advantages of using the CDR proxy compared to the linear depolarization ratio are discussed.
Abstract
Correcting observed polarimetric radar variables for attenuation and differential attenuation effects in rain is important for meteorological applications involving measurements at attenuating frequencies such as those at X band. The results of estimating the coefficients in the correction-scheme relations from dual-wavelength polarimetric radar measurements of rainfall involving attenuating and nonattenuating frequencies are described. Such coefficients found directly from measurements are essentially free from different assumptions about drop shapes, drop size distributions, and/or relations between different radar variables that are typically used in many attenuation and differential attenuation correction schemes. Experimentally based estimates derived using dual-wavelength radar measurements conducted during a project in northern Colorado indicate values of the coefficients in the attenuation–differential phase quasi-linear relations at X band in the approximate range of 0.20–0.31 dB deg−1. The corresponding coefficients in the differential attenuation–differential phase relations are in the range of 0.052–0.065 dB deg−1.
Abstract
Correcting observed polarimetric radar variables for attenuation and differential attenuation effects in rain is important for meteorological applications involving measurements at attenuating frequencies such as those at X band. The results of estimating the coefficients in the correction-scheme relations from dual-wavelength polarimetric radar measurements of rainfall involving attenuating and nonattenuating frequencies are described. Such coefficients found directly from measurements are essentially free from different assumptions about drop shapes, drop size distributions, and/or relations between different radar variables that are typically used in many attenuation and differential attenuation correction schemes. Experimentally based estimates derived using dual-wavelength radar measurements conducted during a project in northern Colorado indicate values of the coefficients in the attenuation–differential phase quasi-linear relations at X band in the approximate range of 0.20–0.31 dB deg−1. The corresponding coefficients in the differential attenuation–differential phase relations are in the range of 0.052–0.065 dB deg−1.
Abstract
An operational suite of ground-based, remote sensing retrievals for producing cloud microphysical properties is described, assessed, and applied to 1 yr of observations in the Arctic. All measurements were made in support of the Surface Heat Budget of the Arctic (SHEBA) program and First International Satellite Cloud Climatology Project Regional Experiment (FIRE) Arctic Clouds Experiment (ACE) in 1997–98. Retrieval techniques and cloud-type classifications are based on measurements from a vertically pointing 35-GHz Doppler radar, microwave and infrared radiometers, and radiosondes. The retrieval methods are assessed using aircraft in situ measurements from a limited set of case studies and by intercomparison of multiple retrievals for the same parameters. In all-liquid clouds, retrieved droplet effective radii Re have an uncertainty of up to 32% and liquid water contents (LWC) have an uncertainty of 49%–72%. In all-ice clouds, ice particle mean sizes D mean can be retrieved with an uncertainty of 26%–46% while retrieved ice water contents (IWC) have an uncertainty of 62%–100%. In general, radar-only, regionally tuned empirical power-law retrievals were best suited among the tested retrieval algorithms for operational cloud monitoring at SHEBA because of their wide applicability, ease of use, and reasonable statistical accuracy. More complex multisensor techniques provided a moderate improvement in accuracy for specific case studies and were useful for deriving location-specific coefficients for the empirical retrievals but were not as frequently applicable as the single sensor methods because of various limitations. During the yearlong SHEBA program, all-liquid clouds were identified 19% of the time and were characterized by an annual average droplet Re of 6.5 μm, LWC of 0.10 g m−3, and liquid water path of 45 g m−2. All-ice clouds were identified 38% of the time with an annual average particle D mean of 73 μm, IWC of 0.014 g m−3, and ice water path of 30 g m−2.
Abstract
An operational suite of ground-based, remote sensing retrievals for producing cloud microphysical properties is described, assessed, and applied to 1 yr of observations in the Arctic. All measurements were made in support of the Surface Heat Budget of the Arctic (SHEBA) program and First International Satellite Cloud Climatology Project Regional Experiment (FIRE) Arctic Clouds Experiment (ACE) in 1997–98. Retrieval techniques and cloud-type classifications are based on measurements from a vertically pointing 35-GHz Doppler radar, microwave and infrared radiometers, and radiosondes. The retrieval methods are assessed using aircraft in situ measurements from a limited set of case studies and by intercomparison of multiple retrievals for the same parameters. In all-liquid clouds, retrieved droplet effective radii Re have an uncertainty of up to 32% and liquid water contents (LWC) have an uncertainty of 49%–72%. In all-ice clouds, ice particle mean sizes D mean can be retrieved with an uncertainty of 26%–46% while retrieved ice water contents (IWC) have an uncertainty of 62%–100%. In general, radar-only, regionally tuned empirical power-law retrievals were best suited among the tested retrieval algorithms for operational cloud monitoring at SHEBA because of their wide applicability, ease of use, and reasonable statistical accuracy. More complex multisensor techniques provided a moderate improvement in accuracy for specific case studies and were useful for deriving location-specific coefficients for the empirical retrievals but were not as frequently applicable as the single sensor methods because of various limitations. During the yearlong SHEBA program, all-liquid clouds were identified 19% of the time and were characterized by an annual average droplet Re of 6.5 μm, LWC of 0.10 g m−3, and liquid water path of 45 g m−2. All-ice clouds were identified 38% of the time with an annual average particle D mean of 73 μm, IWC of 0.014 g m−3, and ice water path of 30 g m−2.
Abstract
This article describes polarimetric X-band radar-based quantitative precipitation estimations (QPE) under conditions of low freezing levels when, even at the lowest possible elevation angles, radar resolution volumes at longer ranges are in melting-layer or snow regions while it rains at the ground. A specifically adjusted vertical-profile-of-reflectivity (VPR) approach is introduced. The mean VPR is constructed based on the range–height indicator scans, and the effects of smoothing of brightband (BB) features with range are accounted for. A principal feature of the suggested QPE approach is the determination of the reflectivity BB boundaries and freezing-level heights on a beam-by-beam basis using the copolar correlation coefficient ρ hv, which is routinely available from the X-band radar measurements. It is shown that this coefficient provides a robust discrimination among the regions of rain, melting hydrometeors, and snow. The freezing-level estimates made using ρ hv were within 100–200 m from the corresponding estimates of the 0° isotherm heights from radiosonde soundings. The suggested VPR approach with the polarimetric determination of the reflectivity BB boundaries was used for QPE during the wintertime deployment of the NOAA X-band radar as part of the 2006 Hydrometeorological Test Bed (HMT-06) field experiment in the California Sierra Nevada foothills. It is shown that this approach noticeably improves radar-rainfall accumulation estimates. The use of the HMT-06 mean X-band reflectivity–rain-rate (Z eh–R) relation resulted in an approximately 65% relative standard deviation of radar estimates from the surface rain gauges if no VPR adjustments were made. Applying the VPR approach with polarimetric detection of the melting layer resulted in reduction of the corresponding relative standard deviation by about a factor of 2.
Abstract
This article describes polarimetric X-band radar-based quantitative precipitation estimations (QPE) under conditions of low freezing levels when, even at the lowest possible elevation angles, radar resolution volumes at longer ranges are in melting-layer or snow regions while it rains at the ground. A specifically adjusted vertical-profile-of-reflectivity (VPR) approach is introduced. The mean VPR is constructed based on the range–height indicator scans, and the effects of smoothing of brightband (BB) features with range are accounted for. A principal feature of the suggested QPE approach is the determination of the reflectivity BB boundaries and freezing-level heights on a beam-by-beam basis using the copolar correlation coefficient ρ hv, which is routinely available from the X-band radar measurements. It is shown that this coefficient provides a robust discrimination among the regions of rain, melting hydrometeors, and snow. The freezing-level estimates made using ρ hv were within 100–200 m from the corresponding estimates of the 0° isotherm heights from radiosonde soundings. The suggested VPR approach with the polarimetric determination of the reflectivity BB boundaries was used for QPE during the wintertime deployment of the NOAA X-band radar as part of the 2006 Hydrometeorological Test Bed (HMT-06) field experiment in the California Sierra Nevada foothills. It is shown that this approach noticeably improves radar-rainfall accumulation estimates. The use of the HMT-06 mean X-band reflectivity–rain-rate (Z eh–R) relation resulted in an approximately 65% relative standard deviation of radar estimates from the surface rain gauges if no VPR adjustments were made. Applying the VPR approach with polarimetric detection of the melting layer resulted in reduction of the corresponding relative standard deviation by about a factor of 2.
Abstract
Scanning polarimetric measurements from the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) systems are evaluated for the retrievals of snow-level (SL) heights, which are located below the 0°C isotherm and represent the altitude within the melting layer (ML) where snow changes to rain. The evaluations are conducted by intercomparisons of the SL estimates obtained from the Beale Air Force Base WSR-88D unit (KBBX) during a wet season 6-month period (from October 2012 to March 2013) and robust SL height measurements h SL from a high-resolution vertically pointing Doppler snow-level profiler deployed near Oroville, California. It is shown that a mean value height measurement h L3 between the estimates of the ML top and bottom, which can be derived from the WSR-88D level-III (L3) ML products, provides relatively unbiased estimates of SL heights with a standard deviation of about 165 m. There is little azimuthal variability in derived values of h L3, which is, in part, due to the use of higher radar beam tilts and azimuthal smoothing of the level-III ML products. Height estimates h rho based on detection of the ML minima of the copolar cross-correlation coefficient ρ hv calculated from the WSR-88D level-II products are slightly better correlated with profiler-derived SL heights, though they are biased low by about 113 m with respect to h SL. If this bias is accounted for, the standard deviation of the ρ hv minima–based SL estimates is generally less than 100 m. Overall, the results of this study indicate that, at least for closer radar ranges (up to ~13–15 km), the operational radar polarimetric data can provide snow-level estimates with a quality similar to those from the dedicated snow-level radar profilers.
Abstract
Scanning polarimetric measurements from the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) systems are evaluated for the retrievals of snow-level (SL) heights, which are located below the 0°C isotherm and represent the altitude within the melting layer (ML) where snow changes to rain. The evaluations are conducted by intercomparisons of the SL estimates obtained from the Beale Air Force Base WSR-88D unit (KBBX) during a wet season 6-month period (from October 2012 to March 2013) and robust SL height measurements h SL from a high-resolution vertically pointing Doppler snow-level profiler deployed near Oroville, California. It is shown that a mean value height measurement h L3 between the estimates of the ML top and bottom, which can be derived from the WSR-88D level-III (L3) ML products, provides relatively unbiased estimates of SL heights with a standard deviation of about 165 m. There is little azimuthal variability in derived values of h L3, which is, in part, due to the use of higher radar beam tilts and azimuthal smoothing of the level-III ML products. Height estimates h rho based on detection of the ML minima of the copolar cross-correlation coefficient ρ hv calculated from the WSR-88D level-II products are slightly better correlated with profiler-derived SL heights, though they are biased low by about 113 m with respect to h SL. If this bias is accounted for, the standard deviation of the ρ hv minima–based SL estimates is generally less than 100 m. Overall, the results of this study indicate that, at least for closer radar ranges (up to ~13–15 km), the operational radar polarimetric data can provide snow-level estimates with a quality similar to those from the dedicated snow-level radar profilers.
