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Statistics of the z–r relationship for Strong Convective Weather over the Yangtze–Huaihe river Basin and Its Application to radar reflectivity Data Assimilation for a Heavy rain Event

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Journal of Meteorological research 2018年第4期32卷 598-611页

作者： Xue FANG Aimei SHAO Xinjian YUE Weicheng LIU Key Laboratory for Semi-Arid Climate Change of the Ministry of Education College of Atmospheric Sciences Lanzhou University Lanzhou 730000 Lanzhou Central Meteorological Observatory Lanzhou 730020

The relationship between the radar reflectivity factor （z） and the rainfall rate （r） is recalculated based on radar ob- servations from 10 Doppler radars and hourly rainfall measurements at 6529 automatic weather stations over the Yangtze-Huaihe river basin. The data were collected by the National 973 Project from June to July 2013 for severe convective weather events. The z-r relationship is combined with an empirical qr-r relationship to obtain a new z-qr relationship, which is then used to correct the observational operator for radar reflectivity in the three-dimensional variational （3DVar） data assimilation system of the Weather research and Forecasting （WrF） model to im-prove the analysis and prediction of severe convective weather over the Yangtze--Huaihe river basin. The perform- ance of the corrected reflectivity operator used in the WrF 3DVar data assimilation system is tested with a heavy rain event that occurred over Jiangsu and Anhui provinces and the surrounding regions on 23 June 2013. It is noted that the observations for this event are not included in the calculation of the z-r relationship. Three experiments are conducted with the WrF model and its 3DVar system, including a control run without the assimilation of reflectivity data and two assimilation experiments with the original and corrected refleetivity operators. The experimental results show that the assimilation of radar reflectivity data has a positive impact on the rainfall forecast within a few hours with either the original or corrected reflectivity operators, but the corrected reflectivity operator achieves a better per-forrnance on the rainfall forecast than the original operator. The corrected reflectivity operator extends the effective time of radar data assimilation for the prediction of strong reflectivity. The physical variables analyzed with the corrected reflectivity operator present more reasonable mesoscale structures than those obtained with the original re-flectivity operator

关键词： z-r relationship Weather research and Forecasting (WrF) model three-dimensional variational(3DVar) system data assimilation observation operator

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Understanding the Variability of z-r relationships Caused by Natural Variations in raindrop Size Distributions(DSD):Implication of Drop Size and Number

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Atmospheric and Climate Sciences 2011年第3期1卷 147-164页

作者： Abe D.Ochou Eric-Pascal zahiri Bakary Bamba Manlandon Koffi Laboratoire de Physique de l’Atmosphere et Mecanique des fluides(LAPA-MF) Universite de Cocody-AbidjanAbidjanCote d’Ivoire Institut National Polytechnique Houphouet-Boigny YamoussoukroCote d’Ivoire

In the issue of rainfall estimation by radar through the necessary relationship between radar reflectivity z and rain rate r (z-r), the main limitation is attributed to the variability of this relationship. Indeed, several pre-vious studies have shown the great variability of this relationship in space and time, from a rainfall event to another and even within a single rainfall event. recent studies have shown that the variability of raindrop size distributions and thereby z-r relationships is therefore, more the result of complex dynamic, thermody-namic and microphysical processes within rainfall systems than a convective/stratiform classification of the ground rainfall signature. The raindrop number and size at ground being the resultant of various processes mentioned above, a suitable approach would be to analyze their variability in relation to that of z-r relation-ship. In this study, we investigated the total raindrop concentration number NT and the median volume di-ameter D0 used in numerous studies, and have shown that the combination of these two ‘observed’ parame-ters appears to be an interesting approach to better understand the variability of the z-r relationships in the rainfall events, without assuming a certain analytical raindrop size distribution model (exponential, gamma, or log-normal). The present study is based on the analysis of disdrometer data collected at different seasons and places in Africa, and aims to show the degree of the raindrop size and number implication in regard to the z-r relationships variability.

关键词： raindrop Size Distribution radar reflectivity Factor rain rate Median Volume Diameter Total Number of Drops Per Unit Volume z-r relationship Convective rain Stratiform rain Squall Lines Thunderstorm

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radar Quantitative Precipitation Estimation Based on the Gated recurrent Unit Neural Network and Echo-Top Data

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Advances in Atmospheric Sciences 2023年第6期40卷 1043-1057页

作者： Haibo zOU Shanshan WU Miaoxia TIAN Key Laboratory of Poyang Lake Wetland and Watershed research of Ministry of Education&School of Geography and Environmental Science Jiangxi Normal UniversityNanchang 330022China Meteorological Sciences Institute of Jiangxi Province Nanchang 330046China Key Laboratory of Natural Disaster Monitoring Early Warning and Assessment of Jiangxi ProvinceNanchang 330022China Jiangxi Climate Center Nanchang 330046China

The Gated recurrent Unit(GrU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(z), radar echo-top height(ET) is also a good indicator of rainfall rate(r). In this study, we propose a new method, GrU_z-ET, by introducing z and ET as two independent variables into the GrU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GrU_z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional z-r relationship(z=300r1.4), the optimal z-r relationship(z=79r1.68) and the GrU neural network with only z as the independent input variable(GrU_z). The results indicate that the GrU_z-ET performs the best, while the traditional z-r relationship performs the worst. The performances of the rest two methods are *** further evaluate the performance of the GrU_z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GrU_z-ET and the smallest for the traditional z-r relationship, and the root mean square error is just the opposite. In addition, these statistics of GrU_z are similar to those of optimal z-r relationship. Thus, it can be concluded that the performance of the GrU_zET is the best in the four methods for the quantitative precipitation estimation.

关键词： quantitative precipitation estimation Gated recurrent Unit neural network z-r relationship echo-top height

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Statistical Characteristics of raindrop Size Distribution in the South China Monsoon region(Guangdong Province)

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Journal of Tropical Meteorology 2023年第1期29卷 55-67页

作者：陈超张阿思吴乃庚刘显通孙晓光王洪 Guangdong Meteorological Observatory Guangzhou 510641 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081 China Guangzhou Institute of Tropical and Marine Meteorology China Meteorological AdministrationGuangzhou 510641 China College of Electronic Engineering Chengdu University of Information TechnologyChengdu 610225 China

While heavy rainfall frequently takes place in southern China during summer monsoon seasons,quantitative precipitation forecast skills are relatively ***,detailed knowledge about the raindrop size distribution(DSD)is useful in improving the quantitative precipitation estimation and *** on the data during 2018-2022 from 86stations in a ground-based optical disdrometer measurement network,the characteristics of the DSD in Guangdong province are investigated in terms of the particle size distribution(N(D)),mass-weighted mean diameter(Dm) and other integral DSD parameters such as radar reflectivity(z),rainfall rate(r) and liquid water content(LWC).In addition,the effects of geographical locations,weather systems(tropical cyclones,frontal systems and the summer monsoon) and precipitation types on DSD characteristics are also *** results are shown as follows.1) Convective precipitation has a broader N(D) and larger mean particle diameter than the stratiform precipitation,and the DSD observations in Guangdong are consistent with the three-parameter gamma *** relationships between the z and r for stratiform and convective precipitation are also derived for the province,i.e.,z=332.34 r1.32and z=366.26r1.42which is distinctly different from that of the Next-generation Weather radar(NEXrAD) z-r relationship in United States.2) In the rainy season(April-September),the Dm, r and LWC are larger than those in the dry season(OctoberMarch).Moreover the above parameters are larger,especially in mid-May,which is the onset of the South China Sea summer monsoon.3) The spatial analysis of DSD shows that the coastal station observations indicate a smaller Dmand a larger normalized intercept parameter(log10Nw),suggestive of maritime-like *** larger and log10Nwis smaller in the inland area,suggestive of continental-like rainfall.4) Affected by such weather systems as the tropical cyclone,frontal system and summer monsoon,the DSD shows

关键词： raindrop size distribution spectral parameters z-r relationship weather types

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An Approach for radar Quantitative Precipitation Estimation Based on Spatiotemporal Network

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Computers, Materials & Continua 2020年第10期65卷 459-479页

作者： Shengchun Wang Xiaozhong Yu Lianye Liu Jingui Huang Tsz Ho Wong Chengcheng Jiang School of Information Science and Engineering Hunan Normal UniversityChangsha410081China Hunan Meteorological Observatory Changsha410118China Blackmagic Design RowvilleVIC 3178Australia

radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological *** the z-r relation between radar and rain has a number of parameters on different areas,and the rainfall varies with seasons,the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall *** paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model(ST-QPE),which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address these *** report on our investigation into contrast reversal experiments with radar echo and rainfall data collected by the Hunan Meteorological *** results are verified and analyzed by using statistical and meteorological methods,and show that the ST-QPE model can inverse the rainfall information corresponding to the radar echo at a given moment,which provides practical guidance for accurate short-range precipitation nowcasting to prevent and mitigate disasters efficiently.

关键词： QPE z-r relationship spatiotemporal network algorithm radar echo

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