Statistics-based optimization of the polarimetric radar hydrometeor classification algorithm and its application for a squall line in South China

2018 ◽  
Vol 35 (3) ◽  
pp. 296-316 ◽  
Author(s):  
Chong Wu ◽  
Liping Liu ◽  
Ming Wei ◽  
Baozhu Xi ◽  
Minghui Yu
Keyword(s):  
South China ◽  
Classification Algorithm ◽  
Squall Line ◽  
Polarimetric Radar

An investigation into microphysical structure of a squall line in South China observed with a polarimetric radar and a disdrometer

2019 ◽  
Vol 226 ◽  
pp. 171-180 ◽  
Author(s):  
Hong Wang ◽  
Fanyou Kong ◽  
Naigeng Wu ◽  
Hongping Lan ◽  
Jinfang Yin
Keyword(s):  
South China ◽  
Squall Line ◽  
Polarimetric Radar

Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

2014 ◽  
Vol 53 (8) ◽  
pp. 2017-2033 ◽  
Author(s):  
Vivek N. Mahale ◽  
Guifu Zhang ◽  
Ming Xue
Keyword(s):  
Fuzzy Logic ◽  
Standard Deviation ◽  
Radar Data ◽  
Classification Algorithm ◽  
Radar Reflectivity ◽  
Membership Functions ◽  
Polarimetric Radar ◽  
Radar Echo ◽  
Three Body ◽  
Reflectivity Factor

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

scholarly journals Analysis of Dual-Polarimetric Radar Variables and Quantitative Precipitation Estimators for Landfall Typhoons and Squall Lines Based on Disdrometer Data in Southern China

Atmosphere ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 30 ◽  
Author(s):  
Yonghua Zhang ◽  
Liping Liu ◽  
Shuoben Bi ◽  
Zhifang Wu ◽  
Ping Shen ◽  
...  
Keyword(s):  
Drop Size ◽  
Southern China ◽  
Average Diameter ◽  
Squall Line ◽  
Polarimetric Radar ◽  
Quantitative Precipitation Estimation ◽  
Squall Lines ◽  
Fitting Method ◽  
Precipitation Estimation ◽  
Differential Reflectivity

Typhoon rainstorms often cause disasters in southern China. Quantitative precipitation estimation (QPE) with the use of polarimetric radar can improve the accuracy of precipitation estimation and enhance typhoon defense ability. On the basis of the observed drop size distribution (DSD) of raindrops, a comparison is conducted among the DSD parameters and the polarimetric radar observation retrieved from DSD in five typhoon and three squall line events that occurred in southern China from 2016 to 2017. A new piecewise fitting method (PFM) is used to develop the QPE estimators for landfall typhoons and squall lines. The performance of QPE is evaluated by two fitting methods for two precipitation types using DSD data collected. Findings indicate that the number concentration of raindrops in typhoon precipitation is large and the average diameter is small, while the raindrops in squall line rain have opposite characteristics. The differential reflectivity (ZDR) and specific differential phase (KDP) in these two precipitation types increase slowly with the reflectivity factor (ZH), whereas the two precipitation types have different ZDR and KDP in the same ZH. Thus, it is critical to fit the rainfall estimator for different precipitation types. Enhanced estimation can be obtained using the estimators for specific precipitation types, whether the estimators are derived from the conventional fitting method (CFM) or PFM, and the estimators fitted using the PFM can produce better results. The estimators for the developed polarimetric radar can be used in operational QPE and quantitative precipitation foresting, and they can improve disaster defense against typhoons and heavy rains.

scholarly journals Cloud Ice Crystal Classification Using a 95-GHz Polarimetric Radar

2004 ◽  
Vol 21 (11) ◽  
pp. 1679-1688 ◽  
Author(s):  
K. Aydin ◽  
J. Singh
Keyword(s):  
Fuzzy Logic ◽  
Air Temperature ◽  
Classification Algorithm ◽  
Ice Crystals ◽  
Polarimetric Radar ◽  
Ice Crystal ◽  
Radar Measurements ◽  
Particle Images ◽  
Cloud Ice ◽  
Range Of Values

Abstract Two algorithms are presented for ice crystal classification using 95-GHz polarimetric radar observables and air temperature (T). Both are based on a fuzzy logic scheme. Ice crystals are classified as columnar crystals (CC), planar crystals (PC), mixtures of PC and small- to medium-sized aggregates and/or lightly to moderately rimed PC (PSAR), medium- to large-sized aggregates of PC, or densely rimed PC, or graupel-like snow or small lumpy graupel (PLARG), and graupel larger than about 2 mm (G). The 1D algorithm makes use of Zh, Zdr, LDRhv, and T, while the 2D algorithm incorporates the three radar observables in pairs, (Zdr, Zh), (LDRhv, Zh), and (Zdr, LDRhv), plus the temperature T. The range of values for each observable or pair of observables is derived from extensive modeling studies conducted earlier. The algorithms are tested using side-looking radar measurements from an aircraft, which was also equipped with particle probes producing simultaneous and nearly collocated shadow images of cloud ice crystals. The classification results from both algorithms agreed very well with the particle images. The two algorithms were in agreement by 89% in one case and 97% in the remaining three cases considered here. The most effective observable in the 1D algorithm was Zdr, and in the 2D algorithm the pair (Zdr, Zh). LDRhv had negligible effect in the 1D classification algorithm for the cases considered here. The temperature T was mainly effective in separating columnar crystals from the rest. The advantage of the 2D algorithm over the 1D algorithm was that it significantly reduced the dependence on T in two out of the four cases.

scholarly journals Deep Learning for Polarimetric Radar Quantitative Precipitation Estimation during Landfalling Typhoons in South China

2021 ◽  
Vol 13 (16) ◽  
pp. 3157
Author(s):  
Yonghua Zhang ◽  
Shuoben Bi ◽  
Liping Liu ◽  
Haonan Chen ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
South China ◽  
Rainfall Intensity ◽  
Spatiotemporal Variability ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Radar Observations ◽  
Quantitative Precipitation Estimation ◽  
Hourly Rainfall ◽  
Precipitation Estimation

Heavy rain associated with landfalling typhoons often leads to disasters in South China, which can be reduced by improving the accuracy of radar quantitative precipitation estimation (QPE). At present, raindrop size distribution (DSD)-based nonlinear fitting (QPEDSD) and traditional neural networks are the main radar QPE algorithms. The former is not sufficient to represent the spatiotemporal variability of DSDs through the generalized Z–R or polarimetric radar rainfall relations that are established using statistical methods since such parametric methods do not consider the spatial distribution of radar observables, and the latter is limited by the number of network layers and availability of data for training the model. In this paper, we propose an alternative approach to dual-polarization radar QPE based on deep learning (QPENet). Three datasets of “dual-polarization radar observations—surface rainfall (DPO—SR)” were constructed using radar observations and corresponding measurements from automatic weather stations (AWS) and used for QPENetV1, QPENetV2, and QPENetV3. In particular, 13 × 13, 25 × 25, and 41 × 41 radar range bins surrounding each AWS location were used in constructing the datasets for QPENetV1, QPENetV2, and QPENetV3, respectively. For training the QPENet models, the radar data and AWS measurements from eleven landfalling typhoons in South China during 2017–2019 were used. For demonstration, an independent typhoon event was randomly selected (i.e., Merbok) to implement the three trained models to produce rainfall estimates. The evaluation results and comparison with traditional QPEDSD algorithms show that the QPENet model has a better performance than the traditional parametric relations. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h−1), the QPEDSD model shows a comparable performance to QPENet. Comparing the three versions of the QPENet model, QPENetV2 has the best overall performance. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h−1), QPENetV3 performs the best.

scholarly journals Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables

2008 ◽  
Vol 136 (6) ◽  
pp. 2228-2245 ◽  
Author(s):  
Youngsun Jung ◽  
Guifu Zhang ◽  
Ming Xue
Keyword(s):  
Rayleigh Scattering ◽  
Weather Prediction ◽  
Radar Data ◽  
Squall Line ◽  
Polarimetric Radar ◽  
Test Bed ◽  
Line System ◽  
Melting Snow ◽  
Water Fraction ◽  
Stratiform Region

Abstract A radar simulator for polarimetric radar variables, including reflectivities at horizontal and vertical polarizations, the differential reflectivity, and the specific differential phase, has been developed. This simulator serves as a test bed for developing and testing forward observation operators of polarimetric radar variables that are needed when directly assimilating these variables into storm-scale numerical weather prediction (NWP) models, using either variational or ensemble-based assimilation methods. The simulator takes as input the results of high-resolution NWP model simulations with ice microphysics and produces simulated polarimetric radar data that may also contain simulated errors. It is developed based on calculations of electromagnetic wave propagation and scattering at the S band of wavelength 10.7 cm in a hydrometeor-containing atmosphere. The T-matrix method is used for the scattering calculation of raindrops and the Rayleigh scattering approximation is applied to snow and hail particles. The polarimetric variables are expressed as functions of the hydrometeor mixing ratios as well as their corresponding drop size distribution parameters and densities. The presence of wet snow and wet hail in the melting layer is accounted for by using a new, relatively simple melting model that defines the water fraction in the melting snow or hail. The effect of varying density due to the melting snow or hail is also included. Vertical cross sections and profiles of the polarimetric variables for a simulated mature multicellular squall-line system and a supercell storm show that polarimetric signatures of the bright band in the stratiform region and those associated with deep convection are well captured by the simulator.

scholarly journals Characterization of Hydrometeors in Sahelian Convective Systems with an X-Band Radar and Comparison with In Situ Measurements. Part I: Sensitivity of Polarimetric Radar Particle Identification Retrieval and Case Study Evaluation

2016 ◽  
Vol 55 (2) ◽  
pp. 231-249 ◽  
Author(s):  
F. Cazenave ◽  
M. Gosset ◽  
M. Kacou ◽  
M. Alcoba ◽  
E. Fontaine ◽  
...  
Keyword(s):  
Relative Proportion ◽  
In Situ Measurements ◽  
Particle Identification ◽  
Squall Line ◽  
Polarimetric Radar ◽  
Dense Particle ◽  
Convective Systems ◽  
X Band ◽  
Study Evaluation

AbstractThe particle identification scheme developed by Dolan and Rutledge for X-band polarimetric radar is tested for the first time in Africa and compared with in situ measurements. The data were acquired during the Megha-Tropiques mission algorithm-validation campaign that occurred in Niger in 2010. The radar classification is compared with the in situ observations gathered by an instrumented aircraft for the 13 August 2010 squall-line case. An original approach has been developed for the radar–in situ comparison: it consists of simulating synthetic radar variables from the microphysical-probe information and comparing the two datasets in a common “radar space.” The consistency between the two types of observation is good considering the differences in sampling illustrated in the paper. The time evolution of the hydrometeor types and their relative proportion in the convective and stratiform regions are analyzed. The farther away from the convection one looks, the more aggregation dominates, riming diminishes, and hydrometeors are less dense. Particle identification based on the polarimetric radar will be applied to a 5-yr African dataset in the future.

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