Application of Spectral Polarimetry to a Hailstorm at Low Elevation Angle

2019 ◽  
Vol 36 (4) ◽  
pp. 567-583 ◽  
Author(s):  
Yadong Wang ◽  
Tian-You Yu ◽  
Alexander V. Ryzhkov ◽  
Matthew R. Kumjian
Keyword(s):  
Elevation Angle ◽  
Radar Data ◽  
Vertical Shear ◽  
Spectral Signatures ◽  
University Of Oklahoma ◽  
Microphysical Properties ◽  
Size Sorting ◽  
Shear Turbulence ◽  
Radar Measurements ◽  
Differential Reflectivity

AbstractSpectral polarimetry has the potential to be used to study microphysical properties in relation to the dynamics within a radar resolution volume by combining Doppler and polarimetric measurements. The past studies of spectral polarimetry have focused on using radar measurements from higher elevation angles, where both the size sorting from the hydrometeors’ terminal velocities and polarimetric characteristics are maintained. In this work, spectral polarimetry is applied to data from the 0° elevation angle, where polarimetric properties are maximized. Radar data collected by the C-band University of Oklahoma Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) during a hailstorm event on 24 April 2011 are used in the analysis. The slope of the spectral differential reflectivity exhibits interesting variations across the hail core, which suggests the presence of size sorting of hydrometeors caused by vertical shear in a turbulent environment. A nearby S-band polarimetric Weather Surveillance Radar-1988 Doppler (KOUN) is also used to provide insights into this hailstorm. Moreover, a flexible numerical simulation is developed for this study, in which different types of hydrometeors such as rain and melting hail can be considered individually or as a combination under different sheared and turbulent conditions. The impacts of particle size distribution, shear, turbulence, attenuation, and mixture of rain and melting hail on polarimetric spectral signatures are investigated with the simulated Doppler spectra and spectral differential reflectivity.

scholarly journals A Variational Approach for Retrieving Raindrop Size Distribution from Polarimetric Radar Measurements in the Presence of Attenuation

2013 ◽  
Vol 52 (1) ◽  
pp. 169-185 ◽  
Author(s):  
Qing Cao ◽  
Guifu Zhang ◽  
Ming Xue
Keyword(s):  
Variational Approach ◽  
Simulated Data ◽  
Real Data ◽  
Radar Data ◽  
Polarimetric Radar ◽  
University Of Oklahoma ◽  
Raindrop Size Distribution ◽  
Radar Measurements ◽  
Raindrop Size ◽  
Differential Reflectivity

AbstractThis study presents a two-dimensional variational approach to retrieving raindrop size distributions (DSDs) from polarimetric radar data in the presence of attenuation. A two-parameter DSD model, the constrained-gamma model, is used to represent rain DSDs. Three polarimetric radar measurements—reflectivity ZH, differential reflectivity ZDR, and specific differential phase KDP—are optimally used to correct for the attenuation and retrieve DSDs by taking into account measurement error effects. Retrieval results with simulated data demonstrate that the proposed algorithm performs well. Applications to real data collected by the X-band Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radars and the C-band University of Oklahoma–Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) also demonstrate the efficacy of this approach.

scholarly journals Characterization of S-Band Dual-Polarized Radar Data for the Convective Rain Melting Layer Detection in A Tropical Region

2018 ◽  
Vol 10 (11) ◽  
pp. 1740 ◽  
Author(s):  
Feng Yuan ◽  
Yee Lee ◽  
Yu Meng ◽  
Jin Ong
Keyword(s):  
Radar Data ◽  
Tropical Region ◽  
Rain Event ◽  
Melting Layer ◽  
Convective Rain ◽  
Radar Measurements ◽  
Dual Polarized ◽  
Rain Events ◽  
Differential Reflectivity

In the tropical region, convective rain is a dominant rain event. However, very little information is known about the convective rain melting layer. In this paper, S-band dual-polarized radar data is studied in order to identify both the stratiform and convective rain melting layers in the tropical region, with a focus on the convective events. By studying and analyzing the above-mentioned two types of rain events, amongst three radar measurements of reflectivity ( Z ), differential reflectivity ( Z DR ), and cross correlation coefficient ( ρ HV ), the latter one is the best indicator for convective rain melting layer detection. From two years (2014 and 2015) of radar and radiosonde observations, 13 convective rain melting layers are identified with available 0 °C isothermal heights which are derived from radiosonde vertical profiles. By comparing the melting layer top heights with the corresponding 0 °C isothermal heights, it is found that for convective rain events, the threshold to detect melting layer should be modified to ρ HV = 0.95 for the tropical region. The melting layer top and bottom heights are then estimated using the proposed threshold, and it is observed from this study that the thickness of convective rain melting layer is around 2 times that of stratiform rain melting layer which is detected by using the conventional ρ HV = 0.97 .

An Automated Python Algorithm to Quantify ZDR Arc and KDP-ZDR Separation Signatures in Supercells

2020 ◽  
Author(s):  
Matthew B. Wilson ◽  
Matthew S. Van Den Broeke
Keyword(s):  
Time Series ◽  
Recent Work ◽  
Radar Data ◽  
Dual Polarization ◽  
Low Level ◽  
Supercell Storms ◽  
Size Sorting ◽  
Supercell Thunderstorms ◽  
Differential Reflectivity

AbstractSupercell thunderstorms often have pronounced signatures of hydrometeor size sorting within their forward flank regions, including an arc-shaped region of high differential reflectivity (ZDR) along the inflow edge of the forward flank known as the ZDR arc and a clear horizontal separation between this area of high ZDP values and and an area of enhanced KDP values deeper into the storm core. Recent work has indicated that ZDR arc and KDP-ZDR separation signatures in supercell storms may be related to environmental storm-relative helicity and low-level shear. Thus, characteristics of these signatures may be helpful to indicate whether a given storm is likely to produce a tornado. Although ZDR arc and KDP-ZDR separation signatures are typically easy to qualitatively identify in dual-polarization radar fields, quantifying their characteristics can be time-consuming and makes research into these signatures and their potential operational applications challenging. To address this problem, this paper introduces an automated Python algorithm to objectively identify and track these signatures in Weather Surveillance Radar-1988 Doppler (WSR-88D) radar data and quantify their characteristics. This paper will discuss the development of the algorithm, demonstrate its performance through comparisons with manually-generated time series of ZDR arc and KDP-ZDR separation signature characteristics, and briefly explore potential uses of this algorithm in research and operations.

scholarly journals Can a Unique Model Describe the Raindrop Shape–Size Relation? A Clue from Polarimetric Radar Measurements

2009 ◽  
Vol 26 (9) ◽  
pp. 1829-1842 ◽  
Author(s):  
Eugenio Gorgucci ◽  
V. Chandrasekar ◽  
Luca Baldini
Keyword(s):  
Axial Ratio ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Drop Shape ◽  
Climatic Regions ◽  
Unique Shape ◽  
Initial Drop ◽  
Radar Measurements ◽  
Differential Reflectivity ◽  
Size Relation

Abstract A method is proposed to retrieve raindrop shape–size relations from the radar measurements of reflectivity factor Zh, differential reflectivity Zdr, and specific differential phase Kdp at S band. This procedure is obtained using a domain defined by the two variables Kdp/Zh and Zdr where the drop size distribution (DSD) variability is collapsed onto a line and any variation is essentially due to the drop shape variability. To obtain information on the raindrop shape–size relation underlying a set of radar observations, this domain is studied in conjunction with another domain describing the relation between the drop axial ratio (or shape) and its equivolumetric diameter. Using an initial drop shape and choosing a set of DSDs described by a normalized gamma model, polarimetric radar measurements are produced by simulation. An averaged curve of Kdp/Zh versus Zdr is obtained and compared with the same curve obtained from the radar data. By changing the initial axial ratio relation, a procedure of minimization between the two curves is developed to derive the underlying drop shape–size relation governing the radar measurements under consideration. Three sets of radar data collected in different climatic regions are analyzed to evaluate whether there is a unique shape–size relation.

scholarly journals Triple-frequency radar retrieval of microphysical properties of snow

2021 ◽  
Vol 14 (11) ◽  
pp. 7243-7254
Author(s):  
Kamil Mroz ◽  
Alessandro Battaglia ◽  
Cuong Nguyen ◽  
Andrew Heymsfield ◽  
Alain Protat ◽  
...  
Keyword(s):  
Water Content ◽  
Radar Data ◽  
Characteristic Size ◽  
In Situ Data ◽  
Microphysical Properties ◽  
Triple Frequency ◽  
Radar Measurements ◽  
Highly Correlated ◽  
Mean Square Errors

Abstract. An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underpin better estimates of the snow characteristic size and content within the radar volume. The algorithm is based on Bayes' rule with riming parameterised by the “fill-in” model. The radar reflectivities are simulated with a range of scattering models corresponding to realistic snowflake shapes. The algorithm is tested on multi-frequency radar data collected during the ESA-funded Radar Snow Experiment For Future Precipitation Mission. During this campaign, in situ microphysical probes were mounted on the same aeroplane as the radars. This nearly perfectly co-located dataset of the remote and in situ measurements gives an opportunity to derive a combined multi-instrument estimate of snow microphysical properties that is used for a rigorous validation of the radar retrieval. Results suggest that the triple-frequency retrieval performs well in estimating ice water content (IWC) and mean mass-weighted diameters obtaining root-mean-square errors of 0.13 and 0.15, respectively, for log 10IWC and log 10Dm. The retrieval of the degree of riming is more challenging, and only the algorithm that uses Doppler information obtains results that are highly correlated with the in situ data.

Storm-Relative Helicity Revealed from Polarimetric Radar Measurements

2009 ◽  
Vol 66 (3) ◽  
pp. 667-685 ◽  
Author(s):  
Matthew R. Kumjian ◽  
Alexander V. Ryzhkov
Keyword(s):  
Strong Positive Correlation ◽  
Convective Storms ◽  
Low Level ◽  
Initial Drop ◽  
Size Sorting ◽  
Radar Measurements ◽  
Microphysical Processes ◽  
Differential Sedimentation ◽  
The Right ◽  
Differential Reflectivity

Abstract The dual-polarization radar variables are especially sensitive to the microphysical processes of melting and size sorting of precipitation particles. In deep convective storms, polarimetric measurements of such processes can provide information about the airflow in and around the storm that may be used to elucidate storm behavior and evolution. Size sorting mechanisms include differential sedimentation, vertical transport, strong rotation, and wind shear. In particular, winds that veer with increasing height typical of supercell environments cause size sorting that is manifested as an enhancement of differential reflectivity (ZDR) along the right or inflow edge of the forward-flank downdraft precipitation echo, which has been called the ZDR arc signature. In some cases, this shear profile can be augmented by the storm inflow. It is argued that the magnitude of this enhancement is related to the low-level storm-relative environmental helicity (SRH) in the storm inflow. To test this hypothesis, a simple numerical model is constructed that calculates trajectories for raindrops based on their individual sizes, which allows size sorting to occur. The modeling results indicate a strong positive correlation between the maximum ZDR in the arc signature and the low-level SRH, regardless of the initial drop size distribution aloft. Additional observational evidence in support of the conceptual model is presented. Potential changes in the ZDR arc signature as the supercell evolves and the low-level mesocyclone occludes are described.

scholarly journals Aspects of Precipitation Development in Trade Wind Cumulus Revealed by Differential Reflectivity at S Band

2008 ◽  
Vol 65 (8) ◽  
pp. 2563-2580 ◽  
Author(s):  
Charles A. Knight ◽  
L. Jay Miller ◽  
Robert A. Rilling
Keyword(s):  
Elevation Angle ◽  
Single Pulse ◽  
Trade Wind ◽  
Similar Data ◽  
Cumulus Clouds ◽  
Entire Volume ◽  
Radar Echo ◽  
Size Sorting ◽  
Differential Reflectivity ◽  
Reflectivity Factor

Abstract Early radar echo development in trade wind cumulus clouds is studied using the equivalent reflectivity factor Ze combined with the differential reflectivity Zdr. The clouds studied are among the largest of trade wind cumulus, developing significantly positive values of Zdr and attaining at least about a 30-dBZ equivalent reflectivity factor. The measures used for analysis are values calculated for entire constant–elevation angle sweeps through the clouds and entire volume scans—not maximum single-pulse-volume values. The radar echo evolution follows fairly closely the Marshall–Palmer distribution with scatter toward higher values of Zdr especially in the earliest stages of echo intensification, where some of the scatter in the whole-sweep values is caused by size sorting. The data provide no evidence for an important role of ultragiant aerosols (UGA) in initiating coalescence. They are in strong contrast with similar data from a cloud over northern Alabama that do suggest a major role for UGA in producing several-mm-diameter raindrops that dominate its weak, early radar echo.

scholarly journals Cloud physical properties and empirical polarimetric measurements of rain signatures at C-Band

2008 ◽  
Vol 6 ◽  
pp. 315-318
Author(s):  
J. Steinert ◽  
M. Chandra
Keyword(s):  
Drop Size ◽  
Theoretical Calculations ◽  
Small Particles ◽  
Rayleigh Approximation ◽  
Scientific Discussion ◽  
Convective Clouds ◽  
Microphysical Properties ◽  
Radar Measurements ◽  
Scientific Results ◽  
Differential Reflectivity

Abstract. Raindrops are one type of precipitation in stratiform and convective clouds. To get relationships for describing the raindrops two different methods were used. In the first way, the microphysical properties of the liquid hydrometeors were examined. For this the use of the Rayleigh approximation for small particles (raindrops at C-Band) and the drop size distribution by Ulbrich (Γ-DSD) lead to the calculation of the reflectivity at horizontal polarisation ZHH, the reflectivity at vertical polarisation ZVV and the differential reflectivity ZDR. In the second way, rain signatures were separated from polarimetric measurements. The database of these measurements consists of datasets measured by the dual polarimetric C-Band weather radar POLDIRAD (DLR, Oberpfaffenhofen). The aim of this study was then to combine and to compare the results from the real radar measurements against the theoretical calculations in the ZHH-ZDR plane. Based on these observations and calculations, scientific results for future practical use will be presented in form of empirical equations including ZHH-ZDR. Finally in form of scientific discussion, the ZHH-ZDR plane will be critically assessed for outstanding problems or issues.

scholarly journals Triple frequency radar retrieval of microphysical properties of snow

2021 ◽  
Author(s):  
Kamil Mroz ◽  
Alessandro Battaglia ◽  
Cuong Nguyen ◽  
Andrew Heymsfield ◽  
Alain Protat ◽  
...  
Keyword(s):  
Water Content ◽  
Radar Data ◽  
Characteristic Size ◽  
In Situ Data ◽  
Microphysical Properties ◽  
Triple Frequency ◽  
Radar Measurements ◽  
Highly Correlated ◽  
Ice Water

Abstract. An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underpin better estimates of the snow characteristic size and content within the radar volume. The algorithm is based on Bayes' rule with riming parameterized by the “fill-in” model. The radar reflectivities are simulated with a range of scattering models corresponding to realistic snowflake shapes. The algorithm is tested on multi-frequency radar data collected during the ESA-funded Radar Snow Experiment. During this campaign in-situ microphysical probes were mounted on the same airplane as the radars. This nearly perfectly collocated dataset of the remote and in-situ measurements gives an opportunity to derive a combined multi-instrument estimate of snow microphysical properties that is used for a rigorous validation of the radar retrieval. Results suggest that the triple-frequency retrieval performs well in estimating ice water content and mean-mass-weighted diameters obtaining root-mean-square-error of 0.13 and 0.15, respectively for log10 IWC and log10 Dm. The retrieval of the degree of riming is more challenging and only the algorithm that uses Doppler information obtains results that are highly correlated with the in-situ data.

scholarly journals Development of an Operational Convective Nowcasting Algorithm Using Raindrop Size Sorting Information from Polarimetric Radar Data

2018 ◽  
Vol 33 (5) ◽  
pp. 1477-1495 ◽  
Author(s):  
Darrel M. Kingfield ◽  
Joseph C. Picca
Keyword(s):  
Model Simulation ◽  
Algorithm Design ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Horizontal Polarization ◽  
Spatiotemporal Resolution ◽  
Raindrop Size Distribution ◽  
Size Sorting ◽  
Raindrop Size ◽  
Differential Reflectivity

Abstract Raindrop size sorting is a ubiquitous microphysical occurrence in precipitating systems. Owing to the greater terminal fall speed of larger particles, a raindrop’s fall trajectory can be sensitive to its size, and strong air currents (e.g., a convective updraft) can enhance this sensitivity. Indeed, observational and numerical model simulation studies have confirmed these effects on raindrop size distributions near convective updrafts. One striking example is the lofting of liquid drops and partially frozen hydrometeors above the environmental 0°C level, resulting in a small columnar region of positive differential reflectivity ZDR in polarimetric radar data, known as the ZDR column. This signature can serve as a proxy for updraft location and strength, offering operational forecasters a tool for monitoring convective trends. Beneath the 0°C level, where WSR-88D spatiotemporal resolution is highest, anomalously high ZDR collocated with lower reflectivity factor at horizontal polarization ZH is often observed within and beneath convective updrafts. Here, size sorting creates a deficit in small drops, while relatively large drops and melting hydrometeors are present in low concentrations. As such, this unique raindrop size distribution and its related polarimetric signature can indicate updraft location sooner and more frequently than the detection of a ZDR column. This paper introduces a novel algorithm that capitalizes on the improved radar coverage at lower levels and automates the detection of this size sorting signature. At the algorithm core, unique ZH–ZDR relationships are created for each radar elevation scan, and positive ZDR outliers (often indicative of size sorting) are identified. Algorithm design, examples, performance, strengths and limitations, and future development are discussed.

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