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- Author or Editor: Robert Schrom x
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Abstract
Recent interest in interpreting polarimetric radar observations of ice and evaluating microphysical model output with these observations has highlighted the importance of accurately computing the scattering of microwave radiation by branched planar ice crystals. These particles are often represented as spheroids with uniform bulk density, reduced from that of solid ice to account for the complex, nonuniform structure of natural branched crystals. In this study, the potential errors that arise from this assumption are examined by comparing scattering calculations of branched planar crystals with those of homogeneous, reduced-density plate crystals and spheroids with the same mass, aspect ratio, and maximum dimension. The results show that this assumption leads to significant errors in backscatter cross sections at horizontal and vertical polarization, specific differential phase (K DP), and differential reflectivity (Z DR), with the largest Z DR errors for ice crystals with the most extreme aspect ratios (<0.01) and effective densities < 250 kg m−3. For example, the maximum errors in X-band Z DR are 4.5 dB for 5.6-mm branched planar crystals. However, substantial errors are present at all weather radar frequencies, with resonance scattering effects at Ka and W band amplifying the low-frequency errors. The implications of these results on the interpretation of polarimetric radar observations and forward modeling of the polarimetric radar variables from microphysical model output are discussed.
Abstract
Recent interest in interpreting polarimetric radar observations of ice and evaluating microphysical model output with these observations has highlighted the importance of accurately computing the scattering of microwave radiation by branched planar ice crystals. These particles are often represented as spheroids with uniform bulk density, reduced from that of solid ice to account for the complex, nonuniform structure of natural branched crystals. In this study, the potential errors that arise from this assumption are examined by comparing scattering calculations of branched planar crystals with those of homogeneous, reduced-density plate crystals and spheroids with the same mass, aspect ratio, and maximum dimension. The results show that this assumption leads to significant errors in backscatter cross sections at horizontal and vertical polarization, specific differential phase (K DP), and differential reflectivity (Z DR), with the largest Z DR errors for ice crystals with the most extreme aspect ratios (<0.01) and effective densities < 250 kg m−3. For example, the maximum errors in X-band Z DR are 4.5 dB for 5.6-mm branched planar crystals. However, substantial errors are present at all weather radar frequencies, with resonance scattering effects at Ka and W band amplifying the low-frequency errors. The implications of these results on the interpretation of polarimetric radar observations and forward modeling of the polarimetric radar variables from microphysical model output are discussed.
Abstract
To better connect radar observations to microphysical processes, the authors analyze concurrent polarimetric radar observations at vertical incidence and roughly side incidence during the Front Range Orographic Storms (FROST) project. Data from three events show signatures of riming, aggregation, and dendritic growth. Riming and the growth of graupel are suggested by negative differential reflectivity Z DR and vertically pointing Doppler velocity magnitude |V R | > 2.0 m s−1; aggregation is indicated by maxima in the downward-relative gradient of radar reflectivity at horizontal polarization Z H below the −15°C isotherm and positive downward-relative gradients in |V R | when averaged over time. A signature of positive downward-relative gradients in Z H , negative downward-relative gradients in |V R |, and maxima in Z DR is observed near −15°C during all three events. This signature may be indicative of dendritic growth; preexisting, thick platelike crystals fall faster and grow slower than dendrites, allowing for |V R | to shift toward the slower-falling, rapidly growing dendrites. To test this hypothesis, simplified calculations of the Z H and |V R | gradients are performed for a range of terminal fall speeds of dendrites and isometric crystals. The authors prescribe linear profiles of Z H for the dendrites and isometric crystals, with the resulting profiles and gradients of |V R | determined from a range of particle fall speeds. Both the observed Z H and |V R | gradients are reproduced by the calculations for a large range of fall speeds. However, more observational data are needed to fully constrain these calculations and reject or support explanations for this signature.
Abstract
To better connect radar observations to microphysical processes, the authors analyze concurrent polarimetric radar observations at vertical incidence and roughly side incidence during the Front Range Orographic Storms (FROST) project. Data from three events show signatures of riming, aggregation, and dendritic growth. Riming and the growth of graupel are suggested by negative differential reflectivity Z DR and vertically pointing Doppler velocity magnitude |V R | > 2.0 m s−1; aggregation is indicated by maxima in the downward-relative gradient of radar reflectivity at horizontal polarization Z H below the −15°C isotherm and positive downward-relative gradients in |V R | when averaged over time. A signature of positive downward-relative gradients in Z H , negative downward-relative gradients in |V R |, and maxima in Z DR is observed near −15°C during all three events. This signature may be indicative of dendritic growth; preexisting, thick platelike crystals fall faster and grow slower than dendrites, allowing for |V R | to shift toward the slower-falling, rapidly growing dendrites. To test this hypothesis, simplified calculations of the Z H and |V R | gradients are performed for a range of terminal fall speeds of dendrites and isometric crystals. The authors prescribe linear profiles of Z H for the dendrites and isometric crystals, with the resulting profiles and gradients of |V R | determined from a range of particle fall speeds. Both the observed Z H and |V R | gradients are reproduced by the calculations for a large range of fall speeds. However, more observational data are needed to fully constrain these calculations and reject or support explanations for this signature.
Abstract
Polarimetric radar measurements provide information about ice particle growth and offer the potential to evaluate and better constrain ice microphysical models. To achieve these goals, one must map the ice particle physical properties (e.g., those predicted by a microphysical model) to electromagnetic scattering properties using a radar forward model. Simplified methods of calculating these scattering properties using homogeneous, reduced-density spheroids produce large errors in the polarimetric radar measurements, particularly for low-aspect-ratio branched planar crystals. To overcome these errors, an empirical method is introduced to more faithfully represent branched planar crystal scattering using scattering calculations for a large number of detailed shapes. Additionally, estimates of the uncertainty in the scattering properties, owing to ambiguity in the crystal shape given a set of bulk physical properties, are also incorporated in the forward model. To demonstrate the utility of the forward model developed herein, the radar variables are simulated from microphysical model output for an Arctic cloud case. The simulated radar variables from the empirical forward model are more consistent with the observations compared to those from the homogeneous, reduced-density-spheroid model, and have relatively low uncertainty.
Abstract
Polarimetric radar measurements provide information about ice particle growth and offer the potential to evaluate and better constrain ice microphysical models. To achieve these goals, one must map the ice particle physical properties (e.g., those predicted by a microphysical model) to electromagnetic scattering properties using a radar forward model. Simplified methods of calculating these scattering properties using homogeneous, reduced-density spheroids produce large errors in the polarimetric radar measurements, particularly for low-aspect-ratio branched planar crystals. To overcome these errors, an empirical method is introduced to more faithfully represent branched planar crystal scattering using scattering calculations for a large number of detailed shapes. Additionally, estimates of the uncertainty in the scattering properties, owing to ambiguity in the crystal shape given a set of bulk physical properties, are also incorporated in the forward model. To demonstrate the utility of the forward model developed herein, the radar variables are simulated from microphysical model output for an Arctic cloud case. The simulated radar variables from the empirical forward model are more consistent with the observations compared to those from the homogeneous, reduced-density-spheroid model, and have relatively low uncertainty.
Abstract
X-band polarimetric radar observations of winter storms in northeastern Colorado on 20–21 February, 9 March, and 9 April 2013 are examined. These observations were taken by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU-CHILL) radar during the Front Range Orographic Storms (FROST) project. The polarimetric radar moments of reflectivity factor at horizontal polarization Z H , differential reflectivity Z DR, and specific differential phase K DP exhibited a range of signatures at different times near the −15°C temperature level favored for dendritic ice crystal growth. In general, K DP was enhanced in these regions with Z DR decreasing and Z H increasing toward the ground, suggestive of aggregation (or riming). The largest Z DR values (~3.5–5.5 dB) were observed during periods of significant low-level upslope flow. Convective features observed when the upslope flow was weaker had the highest K DP (>1.5° km−1) and Z H (>20 dBZ) values. Electromagnetic scattering calculations using the generalized multiparticle Mie method were used to determine whether these radar signatures were consistent with dendrites. Particle size distributions (PSDs) of dendrites were retrieved for a variety of cases using these scattering calculations and the radar observations. The PSDs derived using stratiform precipitation observations were found to be reasonably consistent with previous PSD observations. PSDs derived where riming may have occurred likely had errors and deviated significantly from these previous PSD observations. These results suggest that this polarimetric radar signature may therefore be useful in identifying regions of rapidly collecting dendrites, after considering the effects of riming on the radar variables.
Abstract
X-band polarimetric radar observations of winter storms in northeastern Colorado on 20–21 February, 9 March, and 9 April 2013 are examined. These observations were taken by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU-CHILL) radar during the Front Range Orographic Storms (FROST) project. The polarimetric radar moments of reflectivity factor at horizontal polarization Z H , differential reflectivity Z DR, and specific differential phase K DP exhibited a range of signatures at different times near the −15°C temperature level favored for dendritic ice crystal growth. In general, K DP was enhanced in these regions with Z DR decreasing and Z H increasing toward the ground, suggestive of aggregation (or riming). The largest Z DR values (~3.5–5.5 dB) were observed during periods of significant low-level upslope flow. Convective features observed when the upslope flow was weaker had the highest K DP (>1.5° km−1) and Z H (>20 dBZ) values. Electromagnetic scattering calculations using the generalized multiparticle Mie method were used to determine whether these radar signatures were consistent with dendrites. Particle size distributions (PSDs) of dendrites were retrieved for a variety of cases using these scattering calculations and the radar observations. The PSDs derived using stratiform precipitation observations were found to be reasonably consistent with previous PSD observations. PSDs derived where riming may have occurred likely had errors and deviated significantly from these previous PSD observations. These results suggest that this polarimetric radar signature may therefore be useful in identifying regions of rapidly collecting dendrites, after considering the effects of riming on the radar variables.
Abstract
The scattering properties of aggregates are studied herein. Early aggregates (<7 monomers) of branched planar crystals and mature aggregates (up to 100 monomers) of columns are randomly generated with varying assumptions about the monomer attachment processes and the orientation behavior during collection. The resulting physical properties of the aggregates correspond well with prior in situ and retrieved sizes and shapes. Assumed azimuthally uniform orientations during collection and monomer pivoting upon attachment resulted in flatter and denser aggregates. The column aggregates had lower density and more spherical shapes than the branched planar crystal aggregates. The scattering properties were calculated using the discrete dipole approximation for a set of orientation angles and transformed to spectral coefficients representing modes of orientation angle variability. The zeroth- and second-order coefficients dominate this variability, with the zeroth-order coefficients representing the scattering properties for randomly oriented particles. The second-order coefficients for backscatter showed differences between horizontal and vertical polarization increasing with density, and these coefficients for specific differential phase increase with both mass and density. Similarly, coefficients for the copolar covariance decreased with density. Rapid changes in the contributions to the radar moments from the second-order coefficients from low to moderate density were observed, likely due to the increasing presence of horizontally aligned monomers in the aggregate structure. Differences in how differential reflectivity and correlation coefficient evolve with the orientation distribution parameters suggest that these measurements, along with specific differential phase and reflectivity, provide complementary information about aggregate sizes, shapes, and orientation distributions.
Abstract
The scattering properties of aggregates are studied herein. Early aggregates (<7 monomers) of branched planar crystals and mature aggregates (up to 100 monomers) of columns are randomly generated with varying assumptions about the monomer attachment processes and the orientation behavior during collection. The resulting physical properties of the aggregates correspond well with prior in situ and retrieved sizes and shapes. Assumed azimuthally uniform orientations during collection and monomer pivoting upon attachment resulted in flatter and denser aggregates. The column aggregates had lower density and more spherical shapes than the branched planar crystal aggregates. The scattering properties were calculated using the discrete dipole approximation for a set of orientation angles and transformed to spectral coefficients representing modes of orientation angle variability. The zeroth- and second-order coefficients dominate this variability, with the zeroth-order coefficients representing the scattering properties for randomly oriented particles. The second-order coefficients for backscatter showed differences between horizontal and vertical polarization increasing with density, and these coefficients for specific differential phase increase with both mass and density. Similarly, coefficients for the copolar covariance decreased with density. Rapid changes in the contributions to the radar moments from the second-order coefficients from low to moderate density were observed, likely due to the increasing presence of horizontally aligned monomers in the aggregate structure. Differences in how differential reflectivity and correlation coefficient evolve with the orientation distribution parameters suggest that these measurements, along with specific differential phase and reflectivity, provide complementary information about aggregate sizes, shapes, and orientation distributions.
Abstract
An outstanding challenge in modeling the radiative properties of stratiform rain systems is an accurate representation of the mixed-phase hydrometeors present in the melting layer. The use of ice spheres coated with meltwater or mixed-dielectric spheroids have been used as rough approximations, but more realistic shapes are needed to improve the accuracy of the models. Recently, realistically structured synthetic snowflakes have been computationally generated, with radiative properties that were shown to be consistent with coincident airborne radar and microwave radiometer observations. However, melting such finely structured ice hydrometeors is a challenging problem, and most of the previous efforts have employed heuristic approaches. In the current work, physical laws governing the melting process are applied to the melting of synthetic snowflakes using a meshless-Lagrangian computational approach henceforth referred to as the Snow Meshless Lagrangian Technique (SnowMeLT). SnowMeLT is capable of scaling across large computing clusters, and a collection of synthetic aggregate snowflakes from NASA’s OpenSSP database with diameters ranging from 2 to 10.5 mm are melted as a demonstration of the method. To properly capture the flow of meltwater, the simulations are carried out at relatively high resolution (15 μm), and a new analytic approximation is developed to simulate heat transfer from the environment without the need to simulate the atmosphere explicitly.
Abstract
An outstanding challenge in modeling the radiative properties of stratiform rain systems is an accurate representation of the mixed-phase hydrometeors present in the melting layer. The use of ice spheres coated with meltwater or mixed-dielectric spheroids have been used as rough approximations, but more realistic shapes are needed to improve the accuracy of the models. Recently, realistically structured synthetic snowflakes have been computationally generated, with radiative properties that were shown to be consistent with coincident airborne radar and microwave radiometer observations. However, melting such finely structured ice hydrometeors is a challenging problem, and most of the previous efforts have employed heuristic approaches. In the current work, physical laws governing the melting process are applied to the melting of synthetic snowflakes using a meshless-Lagrangian computational approach henceforth referred to as the Snow Meshless Lagrangian Technique (SnowMeLT). SnowMeLT is capable of scaling across large computing clusters, and a collection of synthetic aggregate snowflakes from NASA’s OpenSSP database with diameters ranging from 2 to 10.5 mm are melted as a demonstration of the method. To properly capture the flow of meltwater, the simulations are carried out at relatively high resolution (15 μm), and a new analytic approximation is developed to simulate heat transfer from the environment without the need to simulate the atmosphere explicitly.
Abstract
The eastern Great Lakes (Erie and Ontario) are often affected by intense lake-effect snowfalls. Lake-effect storms that form parallel to the major axes of these lakes can strongly impact communities by depositing more than 100 cm of snowfall in less than 24 h. Long-lake-axis-parallel (LLAP) storms are significantly different in structure and dynamics compared to the much more studied wind-parallel roll storms that typically form over the western Great Lakes. A Doppler on Wheels (DOW) mobile radar sampled several of these storms at fine spatial and temporal resolutions (and close to the surface) during the winter of 2010–11 over and downwind of Lake Ontario to document and improve understanding of how these storms develop. Over 1100 observations of vortices were catalogued within the 16 December 2010 and 4–5 January 2011 events. The majority of these vortices were less than 1 km in diameter with a statistical modal difference in Doppler velocity (delta-V) value across the vortex of 11 m s−1. Vortices developed along boundaries, which formed within the bands, suggesting horizontal shear instability was the main cause. Other features noted in the DOW observations included bounded weak echo regions, anvils, and horizontal vortices, typically on the south side of west–east-oriented LLAP bands. The reflectivity and velocity structure of LLAP bands were found to be much more complex than previously thought, which may impact localized precipitation amounts and errors in forecast location/intensity.
Abstract
The eastern Great Lakes (Erie and Ontario) are often affected by intense lake-effect snowfalls. Lake-effect storms that form parallel to the major axes of these lakes can strongly impact communities by depositing more than 100 cm of snowfall in less than 24 h. Long-lake-axis-parallel (LLAP) storms are significantly different in structure and dynamics compared to the much more studied wind-parallel roll storms that typically form over the western Great Lakes. A Doppler on Wheels (DOW) mobile radar sampled several of these storms at fine spatial and temporal resolutions (and close to the surface) during the winter of 2010–11 over and downwind of Lake Ontario to document and improve understanding of how these storms develop. Over 1100 observations of vortices were catalogued within the 16 December 2010 and 4–5 January 2011 events. The majority of these vortices were less than 1 km in diameter with a statistical modal difference in Doppler velocity (delta-V) value across the vortex of 11 m s−1. Vortices developed along boundaries, which formed within the bands, suggesting horizontal shear instability was the main cause. Other features noted in the DOW observations included bounded weak echo regions, anvils, and horizontal vortices, typically on the south side of west–east-oriented LLAP bands. The reflectivity and velocity structure of LLAP bands were found to be much more complex than previously thought, which may impact localized precipitation amounts and errors in forecast location/intensity.
Abstract
The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.
Abstract
The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.
Abstract
Severe (>2.5 cm) hail causes >$5 billion in damage annually in the United States. However, radar sizing of hail remains challenging. Typically, spheroids are used to represent hailstones in radar forward operators and to inform radar hail-sizing algorithms. However, natural hailstones can have irregular shapes and lobes; these details significantly influence the hailstone’s scattering properties. The high-resolution 3D structure of real hailstones was obtained using a laser scanner for hail collected during the 2016–17 Insurance Institute for Business and Home Safety (IBHS) Hail Field Study. Plaster casts of several record hailstones (e.g., Vivian, South Dakota, 2010) were also scanned. The S-band scattering properties of these hailstones were calculated with the discrete dipole approximation (DDA). For comparison, scattering properties of spheroidal approximations of each hailstone (with identical maximum and minimum dimensions and mass) were calculated with the T matrix. The polarimetric radar variables have errors when using spheroids, even for small hail. Spheroids generally have smaller variations in the polarimetric variables than the real hailstones. This increased variability is one reason why the correlation coefficient 
Abstract
Severe (>2.5 cm) hail causes >$5 billion in damage annually in the United States. However, radar sizing of hail remains challenging. Typically, spheroids are used to represent hailstones in radar forward operators and to inform radar hail-sizing algorithms. However, natural hailstones can have irregular shapes and lobes; these details significantly influence the hailstone’s scattering properties. The high-resolution 3D structure of real hailstones was obtained using a laser scanner for hail collected during the 2016–17 Insurance Institute for Business and Home Safety (IBHS) Hail Field Study. Plaster casts of several record hailstones (e.g., Vivian, South Dakota, 2010) were also scanned. The S-band scattering properties of these hailstones were calculated with the discrete dipole approximation (DDA). For comparison, scattering properties of spheroidal approximations of each hailstone (with identical maximum and minimum dimensions and mass) were calculated with the T matrix. The polarimetric radar variables have errors when using spheroids, even for small hail. Spheroids generally have smaller variations in the polarimetric variables than the real hailstones. This increased variability is one reason why the correlation coefficient
