Towards an optimal polarimetric radar rainfall methodology: Demonstration during a water-logging disaster in eastern China

2021 ◽  
Author(s):  
Yabin Gou ◽  
Hong Zhu ◽  
Ming Yang ◽  
Haonan Chen ◽  
Jieying He
Keyword(s):  
Eastern China ◽  
Polarimetric Radar ◽  
Radar Rainfall ◽  
Water Logging

scholarly journals Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China

2019 ◽  
Vol 11 (20) ◽  
pp. 2335 ◽  
Author(s):  
Yabin Gou ◽  
Haonan Chen ◽  
Jiafeng Zheng
Keyword(s):  
Extreme Precipitation ◽  
Rapid Development ◽  
Eastern China ◽  
Rain Gauge ◽  
Extreme Precipitation Event ◽  
Precipitation Event ◽  
Distribution Data ◽  
Lake Area ◽  
Polarimetric Radar ◽  
Radar Rainfall

Polarimetric radar provides more choices and advantages for quantitative precipitation estimation (QPE) than single-polarization radar. Utilizing the C-band polarimetric radar in Hangzhou, China, six radar QPE estimators based on the horizontal reflectivity (ZH), specific attenuation (AH), specific differential phase (KDP), and double parameters that further integrate the differential reflectivity (ZDR), namely, R(ZH, ZDR), R(KDP, ZDR), and R(AH, ZDR), are investigated for an extreme precipitation event that occurred in Eastern China on 1 June 2016. These radar QPE estimators are respectively evaluated and compared with a local rain gauge network and drop size distribution data observed by two disdrometers. The results show that (i) although R(AH, ZDR) underestimates in the light rain scenario, it performs the best among all radar QPE estimators according to the normalized mean error; (ii) the optimal radar rainfall relationship and consistency between radar measurements aloft and their surface counterparts are both required to obtain accurate rainfall estimates close to the ground. The contamination from melting layer on AH and KDP can make R(AH), R(AH, ZDR), R(KDP), and R(KDP, ZDR) less effective than R(ZH) and R(ZH,ZDR). Instead, adjustments of the α coefficient can partly reduce such impact and hence render a superior AH–based rainfall estimator; (iii) each radar QPE estimator may outperform others during some time intervals featured by particular rainfall characteristics, but they all tend to underestimate rainfall if radar fails to capture the rapid development of rainstorms.

Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China

2020 ◽  
Author(s):  
Yabin Gou ◽  
Haonan Chen ◽  
Juan Zhou
Keyword(s):  
Extreme Precipitation ◽  
Eastern China ◽  
Rain Gauge ◽  
Extreme Precipitation Event ◽  
Precipitation Event ◽  
Lake Area ◽  
Polarimetric Radar ◽  
Radar Rainfall ◽  
Light Rain ◽  
Advantages And Disadvantages

<p>Polarimetric radar provides more choices and advantages for quantitative precipitation estimation (QPE). Utilizing the C-band polarimetric (CPOL) radar in Hangzhou, China, six radar QPE estimators based on the horizontal reflectivity (<em>Z</em><sub>H</sub>), the specific attenuation (<em>A</em><sub>H</sub>), the specific differential phase (<em>K</em><sub>DP</sub>), and their corresponding double-parameters that further integrate the differential reflectivity (<em>Z</em><sub>DR</sub>), namely <em>R</em>(<em>Z</em><sub>H</sub>, <em>Z</em><sub>DR</sub>), <em>R</em>(<em>K</em><sub>DP</sub>, <em>Z</em><sub>DR</sub>) and <em>R</em>(<em>A</em><sub>H</sub>, <em>Z</em><sub>DR</sub>), are investigated for an extreme precipitation event occurred in Eastern China on 1 June 2016. These radar QPE estimators are respectively evaluated and compared with a local rain gauge network and drop size distribution (DSD) data observed by two disdrometers. The results show that (i) Each radar QPE estimator has its own advantages and disadvantages depending on the specific rainfall patterns, and it can outperform other estimators at a certain period of time; (ii) although <em>R</em>(<em>A</em><sub>H</sub>, <em>Z</em><sub>DR</sub>) underestimates in the light rain pattern, it performs best of all radar QPE estimators according to statistical scores; (iii) Both the optimal radar rainfall relationship and the consistency between radar measurements aloft and surface observations are required to obtain accurate rainfall estimates close to the ground. The contamination of melting solid hydrometeors on <em>A</em><sub>H</sub> and/or <em>K</em><sub>DP </sub>may make them less effective than <em>Z</em><sub>H</sub>. In addition, appropriate α coefficient can eliminate the melting impact on the <em>A</em><sub>H</sub>-based rainfall estimator.</p>

Estimation of Rainfall Based on the Results of Polarimetric Echo Classification

2008 ◽  
Vol 47 (9) ◽  
pp. 2445-2462 ◽  
Author(s):  
Scott E. Giangrande ◽  
Alexander V. Ryzhkov
Keyword(s):  
Type A ◽  
New Method ◽  
Polarimetric Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Different Types ◽  
Rms Error ◽  
Radar Resolution ◽  
Rainfall Estimates

Abstract The quality of polarimetric radar rainfall estimation is investigated for a broad range of distances from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D). The results of polarimetric echo classification have been integrated into the study to investigate the performance of radar rainfall estimation contingent on hydrometeor type. A new method for rainfall estimation that capitalizes on the results of polarimetric echo classification (EC method) is suggested. According to the EC method, polarimetric rainfall relations are utilized if the radar resolution volume is filled with rain (or rain and hail), and multiple R(Z) relations are used for different types of frozen hydrometeors. The intercept parameters in the R(Z) relations for each class are determined empirically from comparisons with gauges. It is shown that the EC method exhibits better performance than the conventional WSR-88D algorithm with a reduction by a factor of 1.5–2 in the rms error of 1-h rainfall estimates up to distances of 150 km from the radar.

scholarly journals Utilization of a C-band Polarimetric Radar for Severe Rainfall Event Analysis in Complex Terrain over Eastern China

2018 ◽  
Vol 11 (1) ◽  
pp. 22 ◽  
Author(s):  
Yabin Gou ◽  
Yingzhao Ma ◽  
Haonan Chen ◽  
Jiapeng Yin
Keyword(s):  
Eastern China ◽  
Liquid Water Content ◽  
Feedback Mechanism ◽  
Rainfall Event ◽  
Event Analysis ◽  
Polarimetric Radar ◽  
Quantitative Precipitation Estimation ◽  
Correction Technique ◽  
Precipitation Estimation ◽  
Rain Drop

Polarimetric radar measurements and products perform as the cornerstones of modern severe weather warning and nowcast systems. Two radar quantitative precipitation estimation (QPE) frameworks, one based on a radar-gauge feedback mechanism and the other based on standard rain drop size distribution (DSD)-derived rainfall retrieval relationships, are both evaluated and investigated through an extreme severe convective rainfall event that occurred on 23 June 2015 in the mountainous region over eastern China, using the first routinely operational C-band polarimetric radar in China. Complex rainstorm characteristics, as indicated by polarimetric radar observables, are also presented to account for the severe rainfall field center located in the gap between gauge stations. Our results show that (i) the improvements of the gauge-feedback-derived radar QPE estimator can be attributed to the attenuation correction technique and dynamically adjusted Z–R relationships, but it greatly relies on the gauge measurement accuracy. (ii) A DSD-derived radar QPE estimator based on the specific differential phase (KDP) performs best among all rainfall estimators, and the interaction between the mesocyclone and the windward slope of the mountainous terrain can account for its apparent overestimation. (iii) The rainstorm is mainly dominated by small-sized and moderate-sized raindrops, with the mean volume diameter being less than 2 mm, but its KDP column (KDP > 3°·km−1) has a liquid water content that is higher than 2.4815 g·m−3, and a high raindrop concentration (Nw) with log10(Nw) exceeding 5.1 mm−1m−3. In addition, small hailstones falling and melting are also found in this event, which further aggregates Nw upon the severe rainfall center in the gap between gauge stations.

scholarly journals Improving Polarimetric C-Band Radar Rainfall Estimation with Two-Dimensional Video Disdrometer Observations in Eastern China

2017 ◽  
Vol 18 (5) ◽  
pp. 1375-1391 ◽  
Author(s):  
Gang Chen ◽  
Kun Zhao ◽  
Guifu Zhang ◽  
Hao Huang ◽  
Su Liu ◽  
...  
Keyword(s):  
Doppler Radar ◽  
Eastern China ◽  
Summer Precipitation ◽  
Summer Rainfall ◽  
Squall Line ◽  
Two Dimensional ◽  
Horizontal Polarization ◽  
Radar Rainfall ◽  
Convective Systems ◽  
Differential Reflectivity

Abstract In this study, the capability of using a C-band polarimetric Doppler radar and a two-dimensional video disdrometer (2DVD) to estimate monsoon-influenced summer rainfall during the Observation, Prediction and Analysis of Severe Convection of China (OPACC) field campaign in 2014 and 2015 in eastern China is investigated. Three different rainfall R estimators, for reflectivity at horizontal polarization [R(Zh)], for reflectivity at horizontal polarization and differential reflectivity factor [R(Zh, Zdr)], and for specific differential phase [R(KDP)], are derived from 2-yr 2DVD observations of summer precipitation systems. The radar-estimated rainfall is compared to gauge observations from eight rainfall episodes. Results show that the two polarimetric estimators, R(Zh, Zdr) and R(KDP), perform better than the traditional Zh–R relation [i.e., R(Zh)]. The KDP-based estimator [i.e., R(KDP)] produces the best rainfall accumulations. The radar rainfall estimators perform differently across the three organized convective systems (mei-yu rainband, typhoon rainband, and squall line). Estimator R(Zh) overestimates rainfall in the mei-yu rainband and squall line, and R(Zh, Zdr) mitigates the overestimation in the mei-yu rainband but has a large bias in the squall line. QPE from R(KDP) is the most accurate among the three estimators, but it possesses a relatively large bias for the squall line compared to the mei-yu case. The high variability of drop size distribution (DSD) related to the precipitation microphysics in different types of rain is largely responsible for the case-dependent QPE performance using any single radar rainfall estimator. The squall line has a distinct ice-phase process with a large mean size of raindrops, while the mei-yu rainband and typhoon rainband are composed of smaller raindrops. Based on the statistical QPE error in the ZH–ZDR space, a new composite rainfall estimator is constructed by combining R(Zh), R(Zh, Zdr), and R(KDP) and is proven to outperform any single rainfall estimator.

scholarly journals Detection of the melting level with polarimetric weather radar

2021 ◽  
Vol 14 (4) ◽  
pp. 2873-2890
Author(s):  
Daniel Sanchez-Rivas ◽  
Miguel A. Rico-Ramirez
Keyword(s):  
Weather Radar ◽  
Radiosonde Data ◽  
Accurate Estimation ◽  
Vertical Profiles ◽  
Polarimetric Radar ◽  
Radar Rainfall ◽  
Radar Measurements ◽  
Polarimetric Weather Radar ◽  
Melting Level ◽  
Ml Detection

Abstract. Accurate estimation of the melting level (ML) is essential in radar rainfall estimation to mitigate the bright band enhancement, classify hydrometeors, correct for rain attenuation and calibrate radar measurements. This paper presents a novel and robust ML-detection algorithm based on either vertical profiles (VPs) or quasi-vertical profiles (QVPs) built from operational polarimetric weather radar scans. The algorithm depends only on data collected by the radar itself, and it is based on the combination of several polarimetric radar measurements to generate an enhanced profile with strong gradients related to the melting layer. The algorithm is applied to 1 year of rainfall events that occurred over southeast England, and the results were validated using radiosonde data. After evaluating all possible combinations of polarimetric radar measurements, the algorithm achieves the best ML detection when combining VPs of ZH, ρHV and the gradient of the velocity (gradV), whereas, for QVPs, combining profiles of ZH, ρHV and ZDR produces the best results, regardless of the type of rain event. The root mean square error in the ML detection compared to radiosonde data is ∼200 m when using VPs and ∼250 m when using QVPs.

scholarly journals Improving Radar Rainfall Estimations with Scaled Raindrop Size Spectra in Mei-Yu Frontal Rainstorms

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5257
Author(s):  
Hepeng Zheng ◽  
Zuhang Wu ◽  
Lifeng Zhang ◽  
Yanqiong Xie ◽  
Hengchi Lei
Keyword(s):  
Scaling Law ◽  
Model Fitting ◽  
Sensor Data ◽  
Polarimetric Radar ◽  
Radar Rainfall ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Potential Applications ◽  
Raindrop Size ◽  
Different Parts

Hydrological calibration of raw weather radar rainfall estimation relies on in situ rainfall measurements. Raindrop size distribution (DSD) was collected during three typical Mei-Yu rainstorms in July 2014 using three particle size velocity (Parsivel) DSD sensors along the Mei-Yu front in Nanjing, Chuzhou, and the western Pacific, respectively. To improve the radar precipitation estimation in different parts of the Mei-Yu front, a scaling method was adopted to formulate the DSD model and further derive the Z–R relations. The results suggest a distinct variation of DSDs in different parts of the Mei-Yu front. Compared with statistical radar Z–ARb relations obtained by mathematical fitting techniques, the use of a DSD model fitting based on a scaling law formulation theoretically shows a significant improvement in both stratiform (33.9%) and convective (2.8%) rainfall estimations of the Mei-Yu frontal system, which indicates that using a scaling law can better reflect the DSD variations in different parts of the Mei-Yu front. Polarimetric radar has indisputable advantages with multiparameter detection ability. Several dual-polarization radar estimators are also established by DSD sensor data, and the R(ZH, ZDR) estimator is proven to be more accurate than traditional Z–R relations in Mei-Yu frontal rainfall, with potential applications for operational C-band polarimetric radar.

scholarly journals Quantification of Precipitation Using Polarimetric Radar Measurements during Several Typhoon Events in Southern China

2020 ◽  
Vol 12 (12) ◽  
pp. 2058
Author(s):  
Qiulei Xia ◽  
Wenjuan Zhang ◽  
Haonan Chen ◽  
Wen-Chau Lee ◽  
Lei Han ◽  
...  
Keyword(s):  
Southern China ◽  
Rain Gauge ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Flood Warning ◽  
Microphysical Properties ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Radar Measurements

Accurate quantitative precipitation estimation (QPE) during typhoon events is critical for flood warning and emergency management. Dual-polarization radar has proven to have better performance for QPE, compared to traditional single-polarization radar. However, polarimetric radar applications have not been extensively investigated in China, especially during extreme events such as typhoons, since the operational dual-polarization system upgrade only happened recently. This paper extends a polarimetric radar rainfall system for local applications during typhoons in southern China and conducts comprehensive studies about QPE and precipitation microphysics. Observations from S-band dual-polarization radar in Guangdong Province during three typhoon events in 2017 are examined to demonstrate the enhanced radar rainfall performance. The microphysical properties of hydrometeors during typhoon events are analyzed through raindrop size distribution (DSD) data and polarimetric radar measurements. The stratiform precipitation in typhoons presents lower mean raindrop diameter and lower raindrop concentration than that of the convection precipitation. The rainfall estimates from the adapted radar rainfall algorithm agree well with rainfall measurements from rain gauges. Using the rain gauge data as references, the maximum normalized mean bias ( N M B ) of the adapted radar rainfall algorithm is 20.27%; the normalized standard error ( N S E ) is less than 40%; and the Pearson’s correlation coefficient ( C C ) is higher than 0.92. For the three typhoon events combined, the N S E and N M B are 36.66% and -15.78%, respectively. Compared with several conventional radar rainfall algorithms, the adapted algorithm based on local rainfall microphysics has the best performance in southern China.

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