Performance of the Hail Differential Reflectivity (HDR) Polarimetric Radar Hail Indicator

2007 ◽  
Vol 46 (8) ◽  
pp. 1290-1301 ◽  
Author(s):  
Tracy K. Depue ◽  
Patrick C. Kennedy ◽  
Steven A. Rutledge
Keyword(s):  
Elevation Angle ◽  
Maximum Diameter ◽  
State University ◽  
Polarimetric Radar ◽  
Colorado State University ◽  
Surface Wetness ◽  
Critical Success ◽  
State Water ◽  
Linear Depolarization Ratio ◽  
Differential Reflectivity

Abstract A series of poststorm surveys were conducted in the wake of hailstorms observed by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU-CHILL) S-Band polarimetric radar. Information on hail characteristics (maximum diameter, building damage, apparent hailstone density, etc.) was solicited from the general-public storm observers that were contacted during the surveys; the locations of their observations were determined using GPS equipment. Low-elevation angle radar measurements of reflectivity, differential reflectivity ZDR, and linear depolarization ratio (LDR) were interpolated to the ground-observer locations. Relationships between the hail differential reflectivity parameter HDR and the observer-reported hail characteristics were examined. It was found that HDR thresholds of 21 and 30 dB were reasonably successful (critical success index values of ∼0.77) in respectively identifying regions where large (>19 mm in diameter) and structurally damaging hail were observed. The LDR characteristics in the observed hail areas were also examined. Because of sensitivities to variations in the hailstone bulk ice density, degree of surface wetness, and shape irregularities, the basic correlation between LDR magnitude and hail diameter was poor. However, when the reported hail diameters exceeded ∼25 mm, LDR levels below ∼−24 dB were uncommon.

scholarly journals Studies of the Polarimetric Covariance Matrix. Part II: Modeling and Polarization Errors

2003 ◽  
Vol 20 (7) ◽  
pp. 1011-1022 ◽  
Author(s):  
J. C. Hubbert ◽  
V. N. Bringi
Keyword(s):  
Covariance Matrix ◽  
Matrix Model ◽  
Model System ◽  
State University ◽  
Intense Rainfall ◽  
Colorado State University ◽  
Model Predictions ◽  
State Water ◽  
Propagation Matrix ◽  
Linear Depolarization Ratio

Abstract A polarimetric radar covariance matrix model is described to study the behavior of the co-to-cross covariances in precipitation. The 2 × 2 propagation matrix with attenuation, differential attenuation, and differential phase is coupled to the backscatter matrix leading to a propagation-modified covariance matrix model. System polarization errors are included in this model as well. This model is used to study the behavior of the magnitude and phase of the co-to-cross covariances and the linear depolarization ratio (LDR) in rainfall. It is shown that the model predictions are consistent with data collected with the Colorado State University (CSU)–University of Chicago–Illinois State Water Survey (CHILL) radar in intense rainfall. A method is also given for estimating the system polarization errors from covariance matrix data collected in intense rainfall.

scholarly journals Polarimetric Spectral Filter for Adaptive Clutter and Noise Suppression

2009 ◽  
Vol 26 (2) ◽  
pp. 215-228 ◽  
Author(s):  
Dmitri N. Moisseev ◽  
V. Chandrasekar
Keyword(s):  
Noise Suppression ◽  
State University ◽  
Spectral Filter ◽  
Colorado State University ◽  
Ground Clutter ◽  
Observation Volume ◽  
Spectral Decompositions ◽  
Different Types ◽  
State Water ◽  
Differential Reflectivity

Abstract In this paper, spectral decompositions of differential reflectivity, differential phase, and copolar correlation coefficient are used to discriminate between weather and nonweather signals in the spectral domain. This approach gives a greater flexibility for discrimination between different types of scattering sources present in a radar observation volume. A spectral filter, which removes nonweather signals, is defined based on this method. The performance of this filter is demonstrated on the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) observations. It is shown that the resulting filter parameters are adaptively defined for each range sample and do not require an assumption on spectral properties of ground clutter.

Microphysical and Polarimetric Radar Modeling of Hydrometeor Refreezing

2021 ◽  
Author(s):  
Dana M. Tobin ◽  
Matthew R. Kumjian
Keyword(s):  
Polarimetric Radar ◽  
Horizontal Polarization ◽  
Radar Signature ◽  
Liquid Portion ◽  
Linear Depolarization Ratio ◽  
Bin Microphysics ◽  
Ice Shell ◽  
Differential Reflectivity ◽  
The Impact ◽  
Polar Correlation

AbstractA unique polarimetric radar signature indicative of hydrometeor refreezing during ice pellet events has been documented in several recent studies, yet the underlying microphysical causes remain unknown. The signature is characterized by enhancements in differential reflectivity (ZDR), specific differential phase (KDP), and linear depolarization ratio (LDR), and a reduction in co-polar correlation coefficient (ρhv) within a layer of decreasing radar reflectivity factor at horizontal polarization (ZH). In previous studies, the leading hypothesis for the observed radar signature is the preferential refreezing of small drops. Here, a simplified, one-dimensional, explicit bin microphysics model is developed to simulate the refreezing of fully melted hydrometeors, and coupled with a polarimetric radar forward operator to quantify the impact of preferential refreezing on simulated radar signatures. The modeling results demonstrate that preferential refreezing is insufficient by itself to produce the observed signatures. In contrast, simulations considering an ice shell growing asymmetrically around a freezing particle (i.e., emulating a thicker ice shell on the bottom of a falling particle) produce realistic ZDR enhancements, and also closely replicate observed features in ZH, KDP, LDR, and ρhv. Simulations that assume no increase in particle wobbling with freezing produce an even greater ZDR enhancement, but this comes at the expense of reducing the LDR enhancement. It is suggested that the polarimetric refreezing signature is instead strongly related to both the distribution of the unfrozen liquid portion within a freezing particle, and the orientation of this liquid with respect to the horizontal.

scholarly journals Clutter Suppression for Staggered PRT Waveforms

2008 ◽  
Vol 25 (12) ◽  
pp. 2209-2218 ◽  
Author(s):  
Dmitri N. Moisseev ◽  
Cuong M. Nguyen ◽  
V. Chandrasekar
Keyword(s):  
Time Domain ◽  
State Of The Art ◽  
Radar Signal ◽  
Clutter Suppression ◽  
State University ◽  
Spectral Moments ◽  
Colorado State University ◽  
Current State ◽  
Repetition Time ◽  
State Water

Abstract This paper presents a clutter suppression methodology for staggered pulse repetition time (PRT) observations. It is shown that spectral moments of precipitation echoes can be accurately estimated even in cases where clutter-to-signal ratios are high by using a parametric time domain method (PTDM). Based on radar signal simulations, the accuracy of the proposed method is evaluated for various observation conditions. The performance of PTDM is demonstrated by the implementation of the staggered PRT at the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL). Based on this study, it is found that the accuracy of the retrieval is comparable to the current state of the art methods applied to the uniformly sampled observations and that the estimated velocity is unbiased for the complete Nyquist range.

A New Dual-Polarization Radar Rainfall Algorithm: Application in Colorado Precipitation Events

2011 ◽  
Vol 28 (3) ◽  
pp. 352-364 ◽  
Author(s):  
R. Cifelli ◽  
V. Chandrasekar ◽  
S. Lim ◽  
P. C. Kennedy ◽  
Y. Wang ◽  
...  
Keyword(s):  
Estimation Algorithm ◽  
State University ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Colorado State University ◽  
Differential Phase ◽  
Precipitation Estimation ◽  
The One ◽  
Differential Reflectivity

Abstract The efficacy of dual-polarization radar for quantitative precipitation estimation (QPE) has been demonstrated in a number of previous studies. Specifically, rainfall retrievals using combinations of reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp) have advantages over traditional Z–R methods because more information about the drop size distribution (DSD) and hydrometeor type are available. In addition, dual-polarization-based rain-rate estimators can better account for the presence of ice in the sampling volume. An important issue in dual-polarization rainfall estimation is determining which method to employ for a given set of polarimetric observables. For example, under what circumstances does differential phase information provide superior rain estimates relative to methods using reflectivity and differential reflectivity? At Colorado State University (CSU), an optimization algorithm has been developed and used for a number of years to estimate rainfall based on thresholds of Zh, Zdr, and Kdp. Although the algorithm has demonstrated robust performance in both tropical and midlatitude environments, results have shown that the retrieval is sensitive to the selection of the fixed thresholds. In this study, a new rainfall algorithm is developed using hydrometeor identification (HID) to guide the choice of the particular rainfall estimation algorithm. A separate HID algorithm has been developed primarily to guide the rainfall application with the hydrometeor classes, namely, all rain, mixed precipitation, and all ice. Both the data collected from the S-band Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar and a network of rain gauges are used to evaluate the performance of the new algorithm in mixed rain and hail in Colorado. The evaluation is also performed using an algorithm similar to the one developed for the Joint Polarization Experiment (JPOLE). Results show that the new CSU HID-based algorithm provides good performance for the Colorado case studies presented here.

scholarly journals Retrieval of Reflectivity in a Networked Radar Environment

2008 ◽  
Vol 25 (10) ◽  
pp. 1755-1767 ◽  
Author(s):  
V. Chandrasekar ◽  
S. Lim
Keyword(s):  
Electromagnetic Waves ◽  
State University ◽  
Colorado State University ◽  
Adaptive Sensing ◽  
Specific Attenuation ◽  
Rain Medium ◽  
X Band ◽  
State Water ◽  
Networked Radar ◽  
Using Data

Abstract A system for reflectivity and attenuation retrieval for rain medium in a networked radar environment is described. Electromagnetic waves backscattered from a common volume in networked radar systems are attenuated differently along the different paths. A solution for the specific attenuation distribution is proposed by solving the integral equation for reflectivity and attenuation. The set of governing integral equations describing the backscatter and propagation of common resolution volume are solved simultaneously with constraints on total path attenuation. The proposed algorithm is evaluated based on simulated X-band radar observations synthesized from S-band measurements collected by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar. Retrieved reflectivity and specific attenuation using the proposed method show good agreement with simulated reflectivity and specific attenuation. Preliminary demonstration of the network-based retrieval using data from the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) IP-1 radar network are also presented.

scholarly journals Polarimetric Radar Characteristics of Melting Hail. Part III: Validation of the Algorithm for Hail Size Discrimination

2016 ◽  
Vol 55 (4) ◽  
pp. 829-848 ◽  
Author(s):  
Kiel L. Ortega ◽  
John M. Krause ◽  
Alexander V. Ryzhkov
Keyword(s):  
Radar Data ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Polarimetric Radar ◽  
Size Discrimination ◽  
Critical Success ◽  
Single Polarization ◽  
Differential Reflectivity

AbstractThis study is the third part of a paper series investigating the polarimetric radar properties of melting hail and application of those properties for operational polarimetric hail detection and determination of its size. The results of theoretical simulations in Part I were used to develop a hail size discrimination algorithm (HSDA) described in Part II. The HSDA uses radar reflectivity Z, differential reflectivity ZDR, and cross-correlation coefficient ρhv along with melting-level height within a fuzzy-logic scheme to distinguish among three hail size classes: small hail (with diameter D < 2.5 cm), large hail (2.5 < D < 5.0 cm), and giant hail (D > 5.0 cm). The HSDA validation is performed using radar data collected by numerous WSR-88D sites and more than 3000 surface hail reports obtained from the Severe Hazards Analysis and Verification Experiment (SHAVE). The original HSDA version was modified in the process of validation, and the modified algorithm demonstrates probability of detection of 0.594, false-alarm ratio of 0.136, and resulting critical success index (CSI) equal to 0.543. The HSDA outperformed the current operational single-polarization hail detection algorithm, which only provides a single hail size estimate per storm and is characterized by CSI equal to 0.324. It is shown that HSDA is particularly sensitive to the quality of ZDR measurements, which might be affected by possible radar miscalibration and anomalously high differential attenuation.

scholarly journals Continuous Monitoring of Differential Reflectivity Bias for C-Band Polarimetric Radar Using Online Solar Echoes in Volume Scans

2019 ◽  
Vol 11 (22) ◽  
pp. 2714
Author(s):  
Chu ◽  
Liu ◽  
Zhang ◽  
Kou ◽  
Li
Keyword(s):  
High Frequency ◽  
Continuous Monitoring ◽  
Information Science ◽  
Elevation Angle ◽  
Gaussian Model ◽  
Likelihood Estimation ◽  
Polarimetric Radar ◽  
Time Calibration ◽  
Calibration Methods ◽  
Differential Reflectivity

The measurement error of differential reflectivity (ZDR), especially systematic ZDR bias, is a fundamental issue for the application of polarimetric radar data. Several calibration methods have been proposed and applied to correct ZDR bias. However, recent studies have shown that ZDR bias is time-dependent and can be significantly different on two adjacent days. This means that the frequent monitoring of ZDR bias is necessary, which is difficult to achieve with existing methods. As radar sensitivity has gradually been enhanced, large amounts of online solar echoes have begun to be observed in volume-scan data. Online solar echoes have a high frequency, and a known theoretical value of ZDR (0 dB) could thus allow the continuous monitoring of ZDR bias. However, online solar echoes are also affected by low signal-to-noise ratio and precipitation attenuation for short-wavelength radar. In order to understand the variation of ZDR bias in a C-band polarimetric radar at the Nanjing University of Information Science and Technology (NUIST-CDP), we analyzed the characteristics of online solar echoes from this radar, including the daily frequency of occurrence, the distribution along the radial direction, precipitation attenuation, and fluctuation caused by noise. Then, an automatic method based on online solar echoes was proposed to monitor the daily ZDR bias of the NUIST-CDP. In the proposed method, a one-way differential attenuation correction for solar echoes and a maximum likelihood estimation using a Gaussian model were designed to estimate the optimal daily ZDR bias. The analysis of three months of data from the NUIST-CDP showed the following: (1) Online solar echoes occurred very frequently regardless of precipitation. Under the volume-scan mode, the average number of occurrences was 15 per day and the minimum number was seven. This high frequency could meet the requirements of continuous monitoring of the daily ZDR bias under precipitation and no-rain conditions. (2) The result from the proposed online solar method was significantly linearly correlated with that from the vertical pointing method (observation at an elevation angle of 90°), with a correlation coefficient of 0.61, suggesting that the proposed method is feasible. (3) The day-to-day variation in the ZDR bias was relatively large, and 32% of such variations exceeded 0.2 dB, meaning that a one-time calibration was not representative in time. Accordingly, continuous calibration will be necessary. (4) The ZDR bias was found to be largely influenced by the ambient temperature, with a large negative correlation between the ZDR bias and the temperature.

scholarly journals Freezing of Raindrops in Deep Convective Updrafts: A Microphysical and Polarimetric Model

2012 ◽  
Vol 69 (12) ◽  
pp. 3471-3490 ◽  
Author(s):  
Matthew R. Kumjian ◽  
Scott M. Ganson ◽  
Alexander V. Ryzhkov
Keyword(s):  
Electromagnetic Scattering ◽  
Drop Size ◽  
Sharp Decrease ◽  
Supercooled Liquid ◽  
Freezing Process ◽  
Polarimetric Radar ◽  
Linear Depolarization Ratio ◽  
Bin Microphysics ◽  
Differential Reflectivity ◽  
The Impact

Abstract Polarimetric radar observations of convective storms routinely reveal positive differential reflectivity ZDR extending above the 0°C level, indicative of the presence of supercooled liquid particles lofted by the storm’s updraft. The summit of such “ZDR columns” is marked by a zone of enhanced linear depolarization ratio LDR or decreased copolar cross-correlation coefficient ρhv and a sharp decrease in ZDR that together mark a particle freezing zone. To better understand the relation between changes in the storm updraft and the observed polarimetric variables, it is necessary to first understand the physics governing this freezing process and the impact of freezing on the polarimetric variables. A simplified, one-dimensional explicit bin microphysics model of stochastic drop nucleation by an immersed foreign particle and subsequent deterministic freezing is developed and coupled with an electromagnetic scattering model to explore the impact of the freezing process on the polarimetric radar variables. As expected, the height of the ZDR column is closely related to the updraft strength and initial drop size distribution. Additionally, the treatment of the stochastic nucleation process can also affect the depth of the freezing zone, underscoring the need to accurately depict this process in parameterizations. Representation of stochastic nucleation and deterministic freezing for each drop size bin yields better agreement between observations and the modeled vertical profiles of the surface reflectivity factor ZH and ZDR than bulk microphysics schemes. Further improvements in the representation of the LDR cap, the observed ZDR gradient in the freezing zone, and the magnitude of the ρhv minimum may require inclusion of accretion, which was not included in this model.

scholarly journals Characterization and Evaluation of Hybrid Polarization Observation of Precipitation

2006 ◽  
Vol 23 (4) ◽  
pp. 552-572 ◽  
Author(s):  
Yanting Wang ◽  
V. Chandrasekar ◽  
V. N. Bringi
Keyword(s):  
Measurement Accuracy ◽  
State University ◽  
Cross Polarization ◽  
Hybrid Mode ◽  
Vertical Polarization ◽  
Colorado State University ◽  
Mode Of Operation ◽  
State Of Polarization ◽  
State Water ◽  
Polarization States

Abstract Transmitting an arbitrary state of polarization while receiving horizontal–vertical polarization states is termed the hybrid polarization mode of operation. A theoretical model is developed for hybrid mode dual-polarization measurements in terms of the covariance matrix under linear horizontal–vertical polarization basis. The cross polarization encountered introduces biases in the copolar parameters estimated in the hybrid mode. Such biases are investigated for different precipitation types and propagation effects resulting from hydrometeor orientation and antenna properties. Polarimetric data measured by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar transmitting horizontal–vertical polarization states is alternately used to demonstrate the measurement accuracy that would be expected in different storm scenarios observed in the hybrid mode.

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