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.

scholarly journals Improved Attenuation-Based Radar Precipitation Estimation Considering the Azimuthal Variabilities of Microphysical Properties

2020 ◽  
Vol 21 (7) ◽  
pp. 1605-1620
Author(s):  
Hao Huang ◽  
Kun Zhao ◽  
Haonan Chen ◽  
Dongming Hu ◽  
Peiling Fu ◽  
...  
Keyword(s):  
Variational Approach ◽  
Optimization Approach ◽  
Radar Rainfall ◽  
Microphysical Properties ◽  
Quantitative Precipitation Estimation ◽  
Hourly Rainfall ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Differential Reflectivity ◽  
The Impact

AbstractThe attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation AH, which requires a good estimate of the ray-averaged ratio between AH and specific differential phase KDP, also known as the coefficient α. In this study, a variational approach is proposed to optimize the coefficient α for better estimates of AH and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity ZDR dependence on horizontal reflectivity factor ZH. Similar to the ZDR-slope approach, the variational approach can obtain the optimized α consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged α values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize α for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized α for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged α.

scholarly journals A Survey and Perspectives on Mathematical Models for Quantitative Precipitation Estimation Using Lightning

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Carlos Manuel Minjarez-Sosa ◽  
Julio Waissman
Keyword(s):  
Mathematical Models ◽  
Convective Precipitation ◽  
Future Research ◽  
The Novel ◽  
Mathematical Methods ◽  
Simple Observation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Cloud To Ground Lightning ◽  
Electrically Charged

Lightning is one of the most spectacular phenomena in nature. It is produced when there is a breakdown in the resistance in the electric field between the ground and an electrically charged cloud. By simple observation, we observe that precipitation, especially the most intense, is often accompanied by lightning. Given this observation, lightning has been employed to estimate convective precipitation since 1969. In early studies, mathematical models were deduced to quantify this relationship and used to estimate precipitation. Currently, the use of several techniques to estimate precipitation is gaining momentum, and lightning is one of the novel techniques to complement the traditional techniques for Quantitative Precipitation Estimation. In this paper, the authors provide a survey of the mathematical methods employed to estimate precipitation through the use of cloud-to-ground lightning. We also offer a perspective on the future research to this end.

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.

A Case Study of Radar-Derived Quantitative Precipitation Estimation

2013 ◽  
Vol 726-731 ◽  
pp. 4541-4546 ◽  
Author(s):  
Li Li Yang ◽  
Yi Yang ◽  
You Cun Qi ◽  
Xue Xing Qiu ◽  
Zhong Qiang Gong
Keyword(s):  
Doppler Radar ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Convective Precipitation ◽  
Precipitation Process ◽  
Fuzzy Logic Algorithm ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

The convective and stratiform precipitations have different precipitation mechanisms. Different reflectivityrainfall rate (ZR) relations should be used for them. A heavy precipitation process on 22nd July, 2009(UTC) in Anhui Province is analyzed with Hefei Doppler radar and 269 rain gauges. First, the type of precipitation is obtained by a fuzzy logic algorithm with radar data. Then the reflectivity values are converted to rainfall rates using an adaptive Z-R relation according to different rain types. It is tested with the case and showed significant improvements over the current operational Z-R QPE when compared with gauges. Results also show that the precipitation process is caused by stratiform and convective precipitation; the rain estimated from radar corresponds well with cloud classification.

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 C-Band Polarimetric Radar QPE Based on Specific Differential Propagation Phase for Extreme Typhoon Rainfall

2013 ◽  
Vol 30 (7) ◽  
pp. 1354-1370 ◽  
Author(s):  
Yadong Wang ◽  
Jian Zhang ◽  
Alexander V. Ryzhkov ◽  
Lin Tang
Keyword(s):  
Complex Terrain ◽  
Extreme Rainfall ◽  
Correction Method ◽  
Screening Process ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Drop Shape ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Typhoon Rainfall

Abstract To obtain accurate radar quantitative precipitation estimation (QPE) for extreme rainfall events such as land-falling typhoon systems in complex terrain, a new method was developed for C-band polarimetric radars. The new methodology includes a correction method based on vertical profiles of the specific differential propagation phase (VPSDP) for low-level blockage and an optimal relation between rainfall rate () and the specific differential phase (). In the VPSDP-based correction approach, a screening process is applied to fields, where missing or unreliable data from lower tilts caused by severe beam blockage are replaced with data from upper and unblocked tilts. The data from upper tilts are adjusted to account for variations in the vertical profile of . The corrected field is then used for rain-rate estimations. To acquire an accurate QPE result, a new relation for C-band polarimetric radars was derived through simulations using drop size distribution (DSD) and drop shape relation (DSR) observations from typhoon systems in Taiwan. The VPSDP-based correction method with the new relation was evaluated using the typhoon cases of Morakot (2009) and Fanapi (2010).

scholarly journals Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation Estimation

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Jing Ren ◽  
Yong Huang ◽  
Li Guan ◽  
Jie Zhou
Keyword(s):  
Radar Data ◽  
Convective Precipitation ◽  
Integration Scheme ◽  
Echo Intensity ◽  
Rain Intensity ◽  
Integration Methods ◽  
Quantitative Precipitation Estimation ◽  
Normalized Error ◽  
Precipitation Estimation ◽  
The Difference

We discuss two different integration methods for radar-based quantitative precipitation estimation (QPE): the echo intensity integral and the rain intensity integral. Theoretical analyses and simulations were used to test differences between these two methods. Cumulative rainfall calculated by the echo intensity integral is usually greater than that from rain intensity integral. The difference of calculated precipitation using these two methods is generally smaller for stable precipitation systems and larger for unstable precipitation systems. If the echo intensity signal is sinusoidal, the discrepancy between the two methods is most significant. For stratiform and convective precipitation, the normalized error ranges from −0.138 to −0.15 and from −0.11 to −0.122, respectively. If the echo intensity signal is linear, the normalized error ranges from 0 to −0.13 and from 0 to −0.11, respectively. If the echo intensity signal is exponential, the normalized error ranges from 0 to −0.35 and from 0 to −0.30, respectively. When both the integration scheme and real radar data were used to estimate cumulative precipitation for one day, their spatial distributions were similar.

scholarly journals Evaluation of the dual-polarization weather radar quantitative precipitation estimation using long-term datasets

2021 ◽  
Vol 25 (3) ◽  
pp. 1245-1258
Author(s):  
Tanel Voormansik ◽  
Roberto Cremonini ◽  
Piia Post ◽  
Dmitri Moisseev
Keyword(s):  
Weather Radar ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Convective Precipitation ◽  
Dual Polarization ◽  
Climate Conditions ◽  
Differential Phase ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Abstract. Accurate, timely, and reliable precipitation observations are mandatory for hydrological forecast and early warning systems. In the case of convective precipitation, traditional rain gauge networks often miss precipitation maxima, due to density limitations and the high spatial variability of the rainfall field. Despite several limitations like attenuation or partial beam blocking, the use of C-band weather radar has become operational in most European weather services. Traditionally, weather-radar-based quantitative precipitation estimation (QPE) is derived from horizontal reflectivity data. Nevertheless, dual-polarization weather radar can overcome several shortcomings of the conventional horizontal-reflectivity-based estimation. As weather radar archives are growing, they are becoming increasingly important for climatological purposes in addition to operational use. For the first time, the present study analyses one of the longest datasets from fully operational polarimetric C-band weather radars; these are located in Estonia and Italy, in very different climate conditions and environments. The length of the datasets used in the study is 5 years for both Estonia and Italy. The study focuses on long-term observations of summertime precipitation and their quantitative estimations by polarimetric observations. From such derived QPEs, accumulations for 1 h, 24 h, and 1-month durations are calculated and compared with reference rain gauges to quantify uncertainties and evaluate performances. Overall, the radar products showed similar results in Estonia and Italy when compared to each other. The product where radar reflectivity and specific differential phase were combined based on a threshold exhibited the best agreement with gauge values in all accumulation periods. In both countries reflectivity-based rainfall QPE underestimated and specific differential-phase-based product overestimated gauge measurements.

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

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).

Sign in / Sign up

Export Citation Format

Share Document