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 Quantitative Precipitation Estimation of the Epic 2013 Colorado Flood Event: Polarization Radar-Based Variational Scheme

2016 ◽  
Vol 55 (7) ◽  
pp. 1477-1495 ◽  
Author(s):  
Wei-Yu Chang ◽  
Jothiram Vivekanandan ◽  
Kyoko Ikeda ◽  
Pay-Liam Lin
Keyword(s):  
Power Law ◽  
Rain Rate ◽  
Flood Event ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Observational Error ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Radar Measurements ◽  
Event Based

AbstractThe accuracy of rain-rate estimation using polarimetric radar measurements has been improved as a result of better characterization of radar measurement quality and rain microphysics. In the literature, a variety of power-law relations between polarimetric radar measurements and rain rate are described because of the dynamic or varying nature of rain microphysics. A variational technique that concurrently takes into account radar observational error and dynamically varying rain microphysics is proposed in this study. Rain-rate estimation using the variational algorithm that uses event-based observational error and background rain climatological values is evaluated using observing system simulation experiments (OSSE), and its performance is demonstrated in the case of an epic Colorado flood event. The rain event occurred between 11 and 12 September 2013. The results from OSSE show that the variational algorithm with event-based observational error consistently estimates more accurate rain rate than does the “R(ZHH, ZDR)” power-law algorithm. On the contrary, the usage of ad hoc or improper observational error degrades the performance of the variational method. Furthermore, the variational algorithm is less sensitive to the observational error of differential reflectivity ZDR than is the R(ZHH, ZDR) algorithm. The variational quantitative precipitation estimation (QPE) retrieved more accurate rainfall estimation than did the power-law dual-polarization QPE in this particular event, despite the fact that both algorithms used the same dual-polarization radar measurements from the Next Generation Weather Radar (NEXRAD).

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 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 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 Microphysical Characteristics of Winter Precipitation in Eastern China from 2014 to 2019

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 920
Author(s):  
Kang Pu ◽  
Xichuan Liu ◽  
Hongbing He ◽  
Yu Sun ◽  
Shuai Hu ◽  
...  
Keyword(s):  
Eastern China ◽  
Winter Precipitation ◽  
Precipitation Intensity ◽  
Precipitation Data ◽  
Solid Precipitation ◽  
Quantitative Precipitation Estimation ◽  
Different Types ◽  
Wet Snow ◽  
Precipitation Estimation ◽  
Relationship Of

To improve solid precipitation monitoring in the hydrology and meteorology field, 1-min precipitation data observed by the PARticle SIze VELocity (PARSIVEL) disdrometer in Nanjing, eastern China, from February 2014 to February 2019 for all days with solid precipitation, were used to study the microphysical characteristics of winter precipitation. In this study, the empirical V-D (velocity–diameter) relationships and observed surface temperature are used for matching precipitation types, and the precipitation data are divided into rain, graupel, wet snow and dry snow. The results show that dry snow and wet snow have maximum Dm (mass-weighted mean diameter) and minimum log10Nw (normalized intercept parameter), while rain shows the opposite. Additionally, the μ-Λ (shape parameter–slope parameter) curve of dry snow and wet snow is very close, and the μ value of dry snow and wet snow is higher than that of graupel and higher than that of rain for the same Λ value. Furthermore, the Ze-S (equivalent reflectivity factor–precipitation intensity) relationships among different types of precipitation are significantly different. If only the Ze-S relationship of rain is used for quantitative precipitation estimation (QPE), then, for small precipitation intensity, solid precipitation will be overestimated, while, for large precipitation intensity, it will be underestimated.

scholarly journals Quantitative Precipitation Estimation with Operational Polarimetric Radar Measurements in Southern China: A Differential Phase–Based Variational Approach

2018 ◽  
Vol 35 (6) ◽  
pp. 1253-1271 ◽  
Author(s):  
Hao Huang ◽  
Kun Zhao ◽  
Guifu Zhang ◽  
Qing Lin ◽  
Long Wen ◽  
...  
Keyword(s):  
Variational Approach ◽  
Southern China ◽  
A Priori ◽  
Polarimetric Radar ◽  
Random Errors ◽  
Differential Phase ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Radar Measurements ◽  
The Impact

AbstractQuantitative precipitation estimation (QPE) with polarimetric radar measurements suffers from different sources of uncertainty. The variational approach appears to be a promising way to optimize the radar QPE statistically. In this study a variational approach is developed to quantitatively estimate the rainfall rate (R) from the differential phase (ΦDP). A spline filter is utilized in the optimization procedures to eliminate the impact of the random errors in ΦDP, which can be a major source of error in the specific differential phase (KDP)-based QPE. In addition, R estimated from the horizontal reflectivity factor (ZH) is used in the a priori with the error covariance matrix statistically determined. The approach is evaluated by an idealized case and multiple real rainfall cases observed by an operational S-band polarimetric radar in southern China. The comparative results demonstrate that with a proper range filter, the proposed variational radar QPE with the a priori included agrees well with the rain gauge measurements and proves to have better performance than the other three approaches, that is, the proposed variational approach without the a priori included, the variational approach proposed by Hogan, and the conventional power-law estimator-based approach.

scholarly journals A Comparison of Convective and Stratiform Precipitation Microphysics of the Record-Breaking Typhoon In-Fa (2021)

2022 ◽  
Vol 14 (2) ◽  
pp. 344
Author(s):  
Zuhang Wu ◽  
Yun Zhang ◽  
Lifeng Zhang ◽  
Hepeng Zheng ◽  
Xingtao Huang
Keyword(s):  
Eastern China ◽  
Convective Precipitation ◽  
Considerable Proportion ◽  
Stratiform Rain ◽  
Convective Rain ◽  
Microphysical Properties ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Typhoon Rainfall ◽  
Spaceborne Radar

In July 2021, Typhoon In-Fa attacked eastern China and broke many records for extreme precipitation over the last century. Such an unrivaled impact results from In-Fa’s slow moving speed and long residence time due to atmospheric circulations. With the supports of 66 networked surface disdrometers over eastern China and collaborative observations from the advanced GPM satellite, we are able to reveal the unique precipitation microphysical properties of the record-breaking Typhoon In-Fa (2021). After separating the typhoon precipitation into convective and stratiform types and comparing the drop size distribution (DSD) properties of Typhoon In-Fa with other typhoons from different climate regimes, it is found that typhoon precipitation shows significant internal differences as well as regional differences in terms of DSD-related parameters, such as mass-weighted mean diameter (Dm), normalized intercept parameter (Nw), radar reflectivity (Z), rain rate (R), and intercept, shape, and slope parameters (N0, µ, Λ). Comparing different rain types inside Typhoon In-Fa, convective rain (Nw ranging from 3.80 to 3.96 mm−1 m−3) shows higher raindrop concentration than stratiform rain (Nw ranging from 3.40 to 3.50 mm−1 m−3) due to more graupels melting into liquid water while falling. Large raindrops occupy most of the region below the melting layer in convective rain due to a dominant coalescence process of small raindrops (featured by larger ZKu, Dm, and smaller N0, µ, Λ), while small raindrops account for a considerable proportion in stratiform rain, reflecting a significant collisional breakup process of large raindrops (featured by smaller ZKu, Dm, and larger N0, µ, Λ). Compared with other typhoons in Hainan and Taiwan, the convective precipitation of Typhoon In-Fa shows a larger (smaller) raindrop concentration than that of Taiwan (Hainan), while smaller raindrop diameter than both Hainan and Taiwan. Moreover, the typhoon convective precipitation measured in In-Fa is more maritime-like than precipitation in Taiwan. Based on a great number of surface disdrometer observational data, the GPM precipitation products were further validated for both rain types, and a series of native quantitative precipitation estimation relations, such as Z–R and R–Dm relations were derived to improve the typhoon rainfall retrieval for both ground-based radar and spaceborne radar.

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