scholarly journals Classification of Rainfall Types Using Parsivel Disdrometer and S-Band Polarimetric Radar in Central Korea

2020 ◽  
Vol 12 (4) ◽  
pp. 642 ◽  
Author(s):  
Jui Le Loh ◽  
Dong-In Lee ◽  
Mi-Young Kang ◽  
Cheol-Hwan You
Keyword(s):  
Vertical Profile ◽  
Drop Size ◽  
Classification Scheme ◽  
Radar Data ◽  
Number Concentration ◽  
Polarimetric Radar ◽  
Retrieval Method ◽  
Median Volume ◽  
Better Than

Tools to identify and classify stratiform and convective rains at various times of the 12 days from June 2015 to March 2016 in Jincheon, Korea, were developed by using a Parsivel disdrometer and S-band polarimetric (S-POL) radar data. Stratiform and convective rains were identified using three different methods (vertical profile of reflectivity (VPR), the method proposed by Bringi et al. (BR03), and a combination of the two (BR03-VPR)) by using a Parsivel disdrometer for its applications to radar as a reference. BR03-VPR exhibits a better classification scheme than the VPR and BR03 methods. The rain types were compared using the drop size distribution (DSD) retrieved from polarimetric variables and reflectivity only. By using the DSD variables, a new convective/stratiform classification line of the log-normalized droplet number concentration ( log 10 N w ) − median volume diameter ( D 0 ) was derived for this area to classify the rainfall types using DSD variables retrieved from the polarimetric radar. For the radar variables, the method by Steiner et al. (SHY95) was found to be the best method, with 0.00% misclassification of the stratiform rains. For the convective rains, the DSD retrieval method performed better. However, for both stratiform and convective rains, the fuzzy method performed better than the SHY95 and DSD retrieval methods.

Observed Bulk Hook Echo Drop Size Distribution Evolution in Supercell Tornadogenesis and Tornadogenesis Failure

2021 ◽  
Author(s):  
Kristofer S. Tuftedal ◽  
Michael M. French ◽  
Darrel M. Kingfield ◽  
Jeffrey C. Snyder
Keyword(s):  
Drop Size ◽  
Thermodynamic Characteristics ◽  
Radar Data ◽  
Number Concentration ◽  
Size Distributions ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Near Surface ◽  
Microphysical Processes ◽  
Drop Size Distributions

AbstractThe time preceding supercell tornadogenesis and tornadogenesis “failure” has been studied extensively to identify differing attributes related to tornado production or lack thereof. Studies from the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) found that air in the rear-flank downdraft (RFD) regions of non- and weakly tornadic supercells had different near-surface thermodynamic characteristics than that in strongly tornadic supercells. Subsequently, it was proposed that microphysical processes are likely to have an impact on the resulting thermodynamics of the near-surface RFD region. One way to view proxies to microphysical features, namely drop size distributions (DSDs), is through use of polarimetric radar data. Studies from the second VORTEX used data from dual-polarization radars to provide evidence of different DSDs in the hook echoes of tornadic and non-tornadic supercells. However, radar-based studies during these projects were limited to a small number of cases preventing result generalizations. This study compiles 68 tornadic and 62 non-tornadic supercells using Weather Surveillance Radar–1988 Doppler (WSR-88D) data to analyze changes in polarimetric radar variables leading up to, and at, tornadogenesis and tornadogenesis failure. Case types generally did not show notable hook echo differences in variables between sets, but did show spatial hook echo quadrant DSD differences. Consistent with past studies, differential radar reflectivity factor (ZDR) generally decreased leading up to tornadogenesis and tornadogenesis failure; in both sets, estimated total number concentration increased during the same times. Relationships between DSDs and the near-storm environment, and implications of results for nowcasting tornadogenesis, also are discussed.

On the Influence of Assumed Drop Size Distribution Form on Radar-Retrieved Thunderstorm Microphysics

2006 ◽  
Vol 45 (2) ◽  
pp. 259-268 ◽  
Author(s):  
Edward A. Brandes ◽  
Guifu Zhang ◽  
Juanzhen Sun
Keyword(s):  
Drop Size ◽  
Liquid Water Content ◽  
Polarimetric Radar ◽  
Stratiform Rain ◽  
Distribution Form ◽  
Median Volume ◽  
Strong Convection ◽  
Radar Measurements ◽  
Size Model ◽  
Drop Size Distributions

Abstract Polarimetric radar measurements are used to retrieve drop size distributions (DSD) in subtropical thunderstorms. Retrievals are made with the single-moment exponential drop size model of Marshall and Palmer driven by radar reflectivity measurements and with a two-parameter constrained-gamma drop size model that utilizes reflectivity and differential reflectivity. Results are compared with disdrometer observations. Retrievals with the constrained-gamma DSD model gave better representation of total drop concentration, liquid water content, and drop median volume diameter and better described their natural variability. The Marshall–Palmer DSD model, with a fixed intercept parameter, tended to underestimate the total drop concentration in storm cores and to overestimate significantly the concentration in stratiform regions. Rainwater contents in strong convection were underestimated by a factor of 2–3, and drop median volume diameters in stratiform rain were underestimated by 0.5 mm. To determine possible DSD model impacts on numerical forecasts, evaporation and accretion rates were computed using Kessler-type parameterizations. Rates based on the Marshall–Palmer DSD model were lower by a factor of 2–3 in strong convection and were higher by about a factor of 2 in stratiform rain than those based on the constrained-gamma model. The study demonstrates the potential of polarimetric radar measurements for improving the understanding of precipitation processes and microphysics parameterization in numerical forecast models.

Classification of Precipitation Types during Transitional Winter Weather Using the RUC Model and Polarimetric Radar Retrievals

2012 ◽  
Vol 51 (4) ◽  
pp. 763-779 ◽  
Author(s):  
Terry J. Schuur ◽  
Hyang-Suk Park ◽  
Alexander V. Ryzhkov ◽  
Heather D. Reeves
Keyword(s):  
Numerical Models ◽  
Radar Data ◽  
Storm Event ◽  
Model Output ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Precipitation Type ◽  
Microphysical Processes ◽  
Using Data

AbstractA new hydrometeor classification algorithm that combines thermodynamic output from the Rapid Update Cycle (RUC) model with polarimetric radar observations is introduced. The algorithm improves upon existing classification techniques that rely solely on polarimetric radar observations by using thermodynamic information to help to diagnose microphysical processes (such as melting or refreezing) that might occur aloft. This added information is especially important for transitional weather events for which past studies have shown radar-only techniques to be deficient. The algorithm first uses vertical profiles of wet-bulb temperature derived from the RUC model output to provide a background precipitation classification type. According to a set of empirical rules, polarimetric radar data are then used to refine precipitation-type categories when the observations are found to be inconsistent with the background classification. Using data from the polarimetric KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman, Oklahoma, the algorithm is tested on a transitional winter-storm event that produced a combination of rain, freezing rain, ice pellets, and snow as it passed over central Oklahoma on 30 November 2006. Examples are presented in which the presence of a radar bright band (suggesting an elevated warm layer) is observed immediately above a background classification of dry snow (suggesting the absence of an elevated warm layer in the model output). Overall, the results demonstrate the potential benefits of combining polarimetric radar data with thermodynamic information from numerical models, with model output providing widespread coverage and polarimetric radar data providing an observation-based modification of the derived precipitation type at closer ranges.

A Moment-Based Polarimetric Radar Forward Operator for Rain Microphysics

2019 ◽  
Vol 58 (1) ◽  
pp. 113-130 ◽  
Author(s):  
Matthew R. Kumjian ◽  
Charlotte P. Martinkus ◽  
Olivier P. Prat ◽  
Scott Collis ◽  
Marcus van Lier-Walqui ◽  
...  
Keyword(s):  
Drop Size ◽  
Functional Form ◽  
A Priori ◽  
Polarimetric Radar ◽  
Microphysics Scheme ◽  
One Dimensional ◽  
Mapping Model ◽  
The Relationship ◽  
Forward Operator ◽  
Better Than

AbstractThere is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model–observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of >200 million realistic DSDs from one-dimensional bin microphysical rain-shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of “truth” and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the sixth and ninth) perform better than the lower-order moments (zeroth and third) typically predicted by many bulk microphysics schemes.

scholarly journals Toward a Polarimetric Radar Classification Scheme for Coalescence-Dominant Precipitation: Application to Complex Terrain

2017 ◽  
Vol 18 (12) ◽  
pp. 3199-3215 ◽  
Author(s):  
Leonardo Porcacchia ◽  
P. E. Kirstetter ◽  
J. J. Gourley ◽  
V. Maggioni ◽  
B. L. Cheong ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Classification Scheme ◽  
Flash Flood ◽  
Radar Data ◽  
High Elevation ◽  
Rain Gauge ◽  
Polarimetric Radar ◽  
Quantitative Precipitation Estimation ◽  
X Band ◽  
Precipitation Estimation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory’s X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data.

scholarly journals Microphysical Retrievals from Dual-Polarization Radar Measurements at X Band

2008 ◽  
Vol 25 (5) ◽  
pp. 729-741 ◽  
Author(s):  
Eugenio Gorgucci ◽  
V. Chandrasekar ◽  
Luca Baldini
Keyword(s):  
Size Distribution ◽  
Attenuation Correction ◽  
Drop Size ◽  
Radar Data ◽  
Drop Size Distribution ◽  
Polarimetric Radar ◽  
Axis Ratio ◽  
X Band ◽  
Radar Measurements ◽  
Self Consistency

Abstract The recent advances in attenuation correction methodology are based on the use of a constraint represented by the total amount of the attenuation encountered along the path shared over each range bin in the path. This technique is improved by using the inner self-consistency of radar measurements. The full self-consistency methodology provides an optimization procedure for obtaining the best estimate of specific and cumulative attenuation and specific and cumulative differential attenuation. The main goal of the study is to examine drop size distribution (DSD) retrieval from X-band radar measurements after attenuation correction. A new technique for estimating the slope of a linear axis ratio model from polarimetric radar measurements at attenuated frequencies is envisioned. A new set of improved algorithms immune to variability in the raindrop shape–size relation are presented for the estimation of the governing parameters characterizing a gamma raindrop size distribution. Simulations based on the use of profiles of gamma drop size distribution parameters obtained from S-band observations are used for quantitative analysis. Radar data collected by the NOAA/Earth System Research Laboratory (ESRL) X-band polarimetric radar are used to provide examples of the DSD parameter retrievals using attenuation-corrected radar measurements. Retrievals agree fairly well with disdrometer data. The radar data are also used to observe the prevailing shape of raindrops directly from the radar measurements. A significant result is that oblateness of drops is bounded between the two shape models of Pruppacher and Beard, and Beard and Chuang, the former representing the upper boundary and the latter the lower boundary.

scholarly journals Radar Measurement of Human Polarimetric Micro-Doppler

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
David Tahmoush ◽  
Jerry Silvious
Keyword(s):  
Phase Difference ◽  
Radar Data ◽  
The Body ◽  
Polarimetric Radar ◽  
Radar Measurement ◽  
Planar Surfaces ◽  
The Subject ◽  
Arm Motion

We use polarimetric micro-Doppler for the detection of arm motion, especially for the classification of whether someone has their arms swinging and is thus unloaded. The arm is often bent at the elbow, providing a surface somewhat similar to a dihedral. This is distinct from the more planar surfaces of the body which allows us to isolate the signals of the arm (and knee). The dihedral produces a double bounce that can be seen in polarimetric radar data by measuring the phase difference between HH and VV. This measurement can then be used to determine whether the subject is unloaded.

scholarly journals Retrieval of lower-order moments of the drop size distribution using CSU-CHILL X-band polarimetric radar: a case study

2020 ◽  
Vol 13 (9) ◽  
pp. 4727-4750
Author(s):  
Viswanathan Bringi ◽  
Kumar Vijay Mishra ◽  
Merhala Thurai ◽  
Patrick C. Kennedy ◽  
Timothy H. Raupach
Keyword(s):  
Size Distribution ◽  
Lower Order ◽  
Drop Size ◽  
Radar Data ◽  
Drop Size Distribution ◽  
Polarimetric Radar ◽  
X Band ◽  
Double Moment ◽  
Differential Reflectivity

Abstract. The lower-order moments of the drop size distribution (DSD) have generally been considered difficult to retrieve accurately from polarimetric radar data because these data are related to higher-order moments. For example, the 4.6th moment is associated with a specific differential phase and the 6th moment with reflectivity and ratio of high-order moments with differential reflectivity. Thus, conventionally, the emphasis has been to estimate rain rate (3.67th moment) or parameters of the exponential or gamma distribution for the DSD. Many double-moment “bulk” microphysical schemes predict the total number concentration (the 0th moment of the DSD, or M0) and the mixing ratio (or equivalently, the 3rd moment M3). Thus, it is difficult to compare the model outputs directly with polarimetric radar observations or, given the model outputs, forward model the radar observables. This article describes the use of double-moment normalization of DSDs and the resulting stable intrinsic shape that can be fitted by the generalized gamma (G-G) distribution. The two reference moments are M3 and M6, which are shown to be retrievable using the X-band radar reflectivity, differential reflectivity, and specific attenuation (from the iterative correction of measured reflectivity Zh using the total Φdp constraint, i.e., the iterative ZPHI method). Along with the climatological shape parameters of the G-G fit to the scaled/normalized DSDs, the lower-order moments are then retrieved more accurately than possible hitherto. The importance of measuring the complete DSD from 0.1 mm onwards is emphasized using, in our case, an optical array probe with 50 µm resolution collocated with a two-dimensional video disdrometer with about 170 µm resolution. This avoids small drop truncation and hence the accurate calculation of lower-order moments. A case study of a complex multi-cell storm which traversed an instrumented site near the CSU-CHILL radar is described for which the moments were retrieved from radar and compared with directly computed moments from the complete spectrum measurements using the aforementioned two disdrometers. Our detailed validation analysis of the radar-retrieved moments showed relative bias of the moments M0 through M2 was <15 % in magnitude, with Pearson’s correlation coefficient >0.9. Both radar measurement and parameterization errors were estimated rigorously. We show that the temporal variation of the radar-retrieved mass-weighted mean diameter with M0 resulted in coherent “time tracks” that can potentially lead to studies of precipitation evolution that have not been possible so far.

scholarly journals Evaluation of radiography as a screening method for detection and characterisation of congenital vertebral malformations in dogs

2018 ◽  
Vol 182 (20) ◽  
pp. 573-573 ◽  
Author(s):  
Josep Brocal ◽  
Steven De Decker ◽  
Roberto José-López ◽  
Julien Guevar ◽  
Maria Ortega ◽  
...  
Keyword(s):  
Selective Breeding ◽  
Classification Scheme ◽  
Screening Method ◽  
Neurological Deficits ◽  
Future Studies ◽  
Breeding Programmes ◽  
Midline Closure ◽  
Radiographic Classification ◽  
Better Than

Congenital vertebral malformations (CVM) are common in brachycephalic ‘screw-tailed’ dogs; they can be associated with neurological deficits and a genetic predisposition has been suggested. The purpose of this study was to evaluate radiography as a screening method for congenital thoracic vertebral malformations in brachycephalic ‘screw-tailed’ dogs by comparing it with CT. Forty-nine dogs that had both radiographic and CT evaluations of the thoracic vertebral column were included. Three observers retrospectively reviewed the images independently to detect CVMs. When identified, they were classified according to a previously published radiographic classification scheme. A CT consensus was then reached. All observers identified significantly more affected vertebrae when evaluating orthogonal radiographic views compared with lateral views alone; and more affected vertebrae with the CT consensus compared with orthogonal radiographic views. Given the high number of CVMs per dog, the number of dogs classified as being CVM free was not significantly different between CT and radiography. Significantly more midline closure defects were also identified with CT compared with radiography. Malformations classified as symmetrical or ventral hypoplasias on radiography were frequently classified as ventral and medial aplasias on CT images. Our results support that CT is better than radiography for the classification of CVMs and this will be important when further evidence of which are the most clinically relevant CVMs is identified. These findings are of particular importance for designing screening schemes of CVMs that could help selective breeding programmes based on phenotype and future studies.

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