Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a mult...Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.展开更多
This study utilized data from an X-band phased array weather radar and ground-based rain gauge observations to conduct a quantitative precipitation estimation(QPE)analysis of a heavy rainfall event in Xiong an New Are...This study utilized data from an X-band phased array weather radar and ground-based rain gauge observations to conduct a quantitative precipitation estimation(QPE)analysis of a heavy rainfall event in Xiong an New Area from 20:00 on August 21 to 07:00 on August 22,2022.The analysis applied the Z-R relationship method for radar-based precipitation estimation and evaluated the QPE algorithm s performance using scatter density plots and binary classification scores.The results indicated that the QPE algorithm accurately estimates light to moderate rainfall but significantly underestimates heavy rainfall.The study identified disparities in the predictive accuracy of the QPE algorithm across various precipitation intensity ranges,offering essential insights for the further refinement of QPE techniques.展开更多
The performance of different quantitative precipitation estimation(QPE) relationships is examined using the polarimetric variables from the X-band polarimetric phased-array radars in Guangzhou,China.Three QPE approach...The performance of different quantitative precipitation estimation(QPE) relationships is examined using the polarimetric variables from the X-band polarimetric phased-array radars in Guangzhou,China.Three QPE approaches,namely,R(ZH),R(ZH,ZDR) and R(KDP),are developed for horizontal reflectivity,differential reflectivity and specific phase shift rate,respectively.The estimation parameters are determined by fitting the relationships to the observed radar variables using the T-matrix method.The QPE relationships were examined using the data of four heavy precipitation events in southern China.The examination shows that the R(ZH) approach performs better for the precipitation rate less than 5 mm h-1, and R(KDP) is better for the rate higher than 5 mm h-1, while R(ZH,ZDR) has the worst performance.An adaptive approach is developed by taking the advantages of both R(ZH) and R(KDP) approaches to improve the QPE accuracy.展开更多
In this paper,a quantitative precipitation estimation based on the hydrometeor classification(HCA-QPE)algorithm was proposed for the first operational S band dual-polarization radar upgraded from the CINRAD/SA radar o...In this paper,a quantitative precipitation estimation based on the hydrometeor classification(HCA-QPE)algorithm was proposed for the first operational S band dual-polarization radar upgraded from the CINRAD/SA radar of China.The HCA-QPE algorithm,localized Colorado State University-Hydrometeor Identification of Rainfall(CSUHIDRO)algorithm,the Joint Polarization Experiment(JPOLE)algorithm,and the dynamic Z-R relationships based on variational correction QPE(DRVC-QPE)algorithm were evaluated with the rainfall events from March 1 to October 30,2017 in Guangdong Province.The results indicated that even though the HCA-QPE algorithm did not use the observed rainfall data for correction,its estimation accuracy was better than that of the DRVC-QPE algorithm when the rainfall rate was greater than 5 mm h-1;and the stronger the rainfall intensity,the greater the QPE improvement.Besides,the HCA-QPE algorithm worked better than the localized CSU-HIDRO and JPOLE algorithms.This study preliminarily evaluated the improved accuracy of QPE by a dual-polarization radar system modified from CINRAD-SA radar.展开更多
With the pros and cons of the traditional optimization and probability pairing methods thoroughly considered, an improved optimal pairing window probability technique is developed using a dynamic relationship between ...With the pros and cons of the traditional optimization and probability pairing methods thoroughly considered, an improved optimal pairing window probability technique is developed using a dynamic relationship between the base reflectivity Z observed by radar and real time precipitation I by rain gauge. Then, the Doppler radar observations of base reflectivity for typhoons Haitang and Matsa in Wenzhou are employed to establish various Z-I relationships, which are subsequently used to estimate hourly precipitation of the two typhoons. Such estimations are calibrated by variational techniques. The results show that there exist significant differences in the Z-I relationships for the typhoons, leading to different typhoon precipitation efficiencies. The typhoon precipitation estimated by applying radar base reflectivity is capable of exhibiting clearly the spiral rain belts and mesoscale cells, and well matches the observed rainfall. Error statistical analyses indicate that the estimated typhoon precipitation is better with variational calibration than the one without. The variational calibration technique is able to maintain the characteristics of the distribution of radar-estimated typhoon precipitation, and to significantly reduce the error of the estimated precipitation in comparison with the observed rainfall.展开更多
In this study,a regional Parsivel OTT disdrometer network covering urban Zhengzhou and adjacent areas is employed to investigate the temporal-spatial variability of raindrop size distributions(DSDs)in the Zhengzhou ex...In this study,a regional Parsivel OTT disdrometer network covering urban Zhengzhou and adjacent areas is employed to investigate the temporal-spatial variability of raindrop size distributions(DSDs)in the Zhengzhou extreme rainfall event on 20 July 2021.The rain rates observed by disdrometers and rain gauges from six operational sites are in good agreement,despite significant site-to-site variations of 24-h accumulated rainfall ranging from 198.3 to 624.1 mm.The Parsivel OTT observations show prominent temporal-spatial variations of DSDs,and the most drastic change was registered at Zhengzhou Station where the record-breaking hourly rainfall of 201.9 mm over 1500-1600 LST(local standard time)was reported.This hourly rainfall is characterized by fairly high concentrations of large raindrops,and the mass-weighted raindrop diameter generally increases with the rain rate before reaching the equilibrium state of DSDs with the rain rate of about 50 mm h^(−1).Besides,polarimetric radar observations show the highest differential phase shift(K_(dp))and differential reflectivity(Z_(dr))near surface over Zhengzhou Station from 1500 to 1600 LST.In light of the remarkable temporal-spatial variability of DSDs,a reflectivity-grouped fitting approach is proposed to optimize the reflectivity-rain rate(Z-R)parameterization for radar quantitative precipitation estimation(QPE),and the rain gauge measurements are used for validation.The results show an increase of mean bias ratio from 0.57 to 0.79 and a decrease of root-mean-square error from 23.69 to 18.36 for the rainfall intensity above 20.0 mm h^(−1),as compared with the fixed Z-R parameterization.This study reveals the drastic temporal-spatial variations of rain microphysics during the Zhengzhou extreme rainfall event and warrants the promise of using reflectivity-grouped fitting Z-R relationships for radar QPE of such events.展开更多
Dual-polarization(dual-pol)radar can measure additional parameters that provide more microphysical information of precipitation systems than those provided by conventional Doppler radar.The dual-pol parameters have be...Dual-polarization(dual-pol)radar can measure additional parameters that provide more microphysical information of precipitation systems than those provided by conventional Doppler radar.The dual-pol parameters have been successfully utilized to investigate precipitation microphysics and improve radar quantitative precipitation estimation(QPE).The recent progress in dual-pol radar research and applications in China is summarized in four aspects.Firstly,the characteristics of several representative dual-pol radars are reviewed.Various approaches have been developed for radar data quality control,including calibration,attenuation correction,calculation of specific differential phase shift,and identification and removal of non-meteorological echoes.Using dual-pol radar measurements,the microphysical characteristics derived from raindrop size distribution retrieval,hydrometeor classification,and QPE is better understood in China.The limited number of studies in China that have sought to use dual-pol radar data to validate the microphysical parameterization and initialization of numerical models and assimilate dual-pol data into numerical models are summarized.The challenges of applying dual-pol data in numerical models and emerging technologies that may make significant impacts on the field of radar meteorology are discussed.展开更多
Currently,Doppler weather radar in China is generally used for quantitative precipitation estimation(QPE)based on the Z–R relationship.However,the estimation error for mixed precipitation is very large.In order to im...Currently,Doppler weather radar in China is generally used for quantitative precipitation estimation(QPE)based on the Z–R relationship.However,the estimation error for mixed precipitation is very large.In order to improve the accuracy of radar QPE,we propose a dynamic radar QPE algorithm with a 6-min interval that uses the reflectivity data of Doppler radar Z9002 in the Shanghai Qingpu District and the precipitation data at automatic weather stations(AWSs)in East China.Considering the time dependence and abrupt changes of precipitation,the data during the previous 30-min period were selected as the training data.To reduce the complexity of radar QPE,we transformed the weather data into the wavelet domain by means of the stationary wavelet transform(SWT)in order to extract high and low-frequency reflectivity and precipitation information.Using the wavelet coefficients,we constructed a support vector machine(SVM)at all scales to estimate the wavelet coefficient of precipitation.Ultimately,via inverse wavelet transformation,we obtained the estimated rainfall.By comparing the results of the proposed method(SWTSVM)with those of Z=300×R1.4,linear regression(LR),and SVM,we determined that the root mean square error(RMSE)of the SWT-SVM method was 0.54 mm per 6 min and the average Threat Score(TS)could exceed 40%with the exception of the downpour category,thus remaining at a high level.Generally speaking,the SWT-SVM method can effectively improve the accuracy of radar QPE and provide an auxiliary reference for actual meteorological operational forecasting.展开更多
基金supported by National Key R&D Program of China(Grant No.2022YFC3003903)the S&T Program of Hebei(Grant No.19275408D),the Key-Area Research and Development Program of Guangdong Province(Grant No.2020B1111200001)+1 种基金the Key Project of Monitoring,Early Warning and Prevention of Major Natural Disasters of China(Grant No.2019YFC1510304)the Joint Fund of Key Laboratory of Atmosphere Sounding,CMA,and the Research Centre on Meteorological Observation Engineering Technology,CMA(Grant No.U2021Z05).
文摘Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.
文摘This study utilized data from an X-band phased array weather radar and ground-based rain gauge observations to conduct a quantitative precipitation estimation(QPE)analysis of a heavy rainfall event in Xiong an New Area from 20:00 on August 21 to 07:00 on August 22,2022.The analysis applied the Z-R relationship method for radar-based precipitation estimation and evaluated the QPE algorithm s performance using scatter density plots and binary classification scores.The results indicated that the QPE algorithm accurately estimates light to moderate rainfall but significantly underestimates heavy rainfall.The study identified disparities in the predictive accuracy of the QPE algorithm across various precipitation intensity ranges,offering essential insights for the further refinement of QPE techniques.
基金Guangzhou Science and Technology Plan Project(202103000030)Guangdong Meteorological Bureau Science and Technology Project(GRMC2020Z08)a project co-funded by the Development Team of Radar Application and Severe Convection Early Warning Technology(GRMCTD202002)。
文摘The performance of different quantitative precipitation estimation(QPE) relationships is examined using the polarimetric variables from the X-band polarimetric phased-array radars in Guangzhou,China.Three QPE approaches,namely,R(ZH),R(ZH,ZDR) and R(KDP),are developed for horizontal reflectivity,differential reflectivity and specific phase shift rate,respectively.The estimation parameters are determined by fitting the relationships to the observed radar variables using the T-matrix method.The QPE relationships were examined using the data of four heavy precipitation events in southern China.The examination shows that the R(ZH) approach performs better for the precipitation rate less than 5 mm h-1, and R(KDP) is better for the rate higher than 5 mm h-1, while R(ZH,ZDR) has the worst performance.An adaptive approach is developed by taking the advantages of both R(ZH) and R(KDP) approaches to improve the QPE accuracy.
基金National Key Research and Development Program of China(2017YFC1404700,2018YFC1506905)Open Research Program of the State Key Laboratory of Severe Weather(2018LASW-B09,2018LASW-B08)+7 种基金Science and Technology Planning Project of Guangdong Province,China(2019B020208016,2018B020207012,2017B020244002)National Natural Science Foundation of China(41375038)Special Scientific Research Fund of Meteorological Public Welfare Profession of China(GHY201506006)2017-2019Meteorological Forecasting Key Technology Development Special Grant(YBGJXM(2017)02-05)Guangdong Science&Technology Plan Project(2015A020217008)Zhejiang Province Major Science and Technology Special Project(2017C03035)Scientific and Technological Research Projects of Guangdong Meteorological Service(GRMC2018M10)Natural Science Foundation of Guangdong Province(2018A030313218)
文摘In this paper,a quantitative precipitation estimation based on the hydrometeor classification(HCA-QPE)algorithm was proposed for the first operational S band dual-polarization radar upgraded from the CINRAD/SA radar of China.The HCA-QPE algorithm,localized Colorado State University-Hydrometeor Identification of Rainfall(CSUHIDRO)algorithm,the Joint Polarization Experiment(JPOLE)algorithm,and the dynamic Z-R relationships based on variational correction QPE(DRVC-QPE)algorithm were evaluated with the rainfall events from March 1 to October 30,2017 in Guangdong Province.The results indicated that even though the HCA-QPE algorithm did not use the observed rainfall data for correction,its estimation accuracy was better than that of the DRVC-QPE algorithm when the rainfall rate was greater than 5 mm h-1;and the stronger the rainfall intensity,the greater the QPE improvement.Besides,the HCA-QPE algorithm worked better than the localized CSU-HIDRO and JPOLE algorithms.This study preliminarily evaluated the improved accuracy of QPE by a dual-polarization radar system modified from CINRAD-SA radar.
基金Key Project of Social Development in Zhejiang Province (2006C13025, 2007C13G1610002)
文摘With the pros and cons of the traditional optimization and probability pairing methods thoroughly considered, an improved optimal pairing window probability technique is developed using a dynamic relationship between the base reflectivity Z observed by radar and real time precipitation I by rain gauge. Then, the Doppler radar observations of base reflectivity for typhoons Haitang and Matsa in Wenzhou are employed to establish various Z-I relationships, which are subsequently used to estimate hourly precipitation of the two typhoons. Such estimations are calibrated by variational techniques. The results show that there exist significant differences in the Z-I relationships for the typhoons, leading to different typhoon precipitation efficiencies. The typhoon precipitation estimated by applying radar base reflectivity is capable of exhibiting clearly the spiral rain belts and mesoscale cells, and well matches the observed rainfall. Error statistical analyses indicate that the estimated typhoon precipitation is better with variational calibration than the one without. The variational calibration technique is able to maintain the characteristics of the distribution of radar-estimated typhoon precipitation, and to significantly reduce the error of the estimated precipitation in comparison with the observed rainfall.
基金Supported by the National Key Research and Development Program of China(2022YFC3003901)National Natural Science Foundation of China(42305087 and 42105141)+2 种基金Science and Technology Innovation Project for Ecosystem Construction of Zhengzhou Supercomputing Center in Henan Province(201400210800)Meteorological Joint Project of Henan Provincial Science and Technology(222103810094 and 232103810091)Basic Research Fund of Chinese Academy of Meteorological Sciences(451490 and 2023Z008).
文摘In this study,a regional Parsivel OTT disdrometer network covering urban Zhengzhou and adjacent areas is employed to investigate the temporal-spatial variability of raindrop size distributions(DSDs)in the Zhengzhou extreme rainfall event on 20 July 2021.The rain rates observed by disdrometers and rain gauges from six operational sites are in good agreement,despite significant site-to-site variations of 24-h accumulated rainfall ranging from 198.3 to 624.1 mm.The Parsivel OTT observations show prominent temporal-spatial variations of DSDs,and the most drastic change was registered at Zhengzhou Station where the record-breaking hourly rainfall of 201.9 mm over 1500-1600 LST(local standard time)was reported.This hourly rainfall is characterized by fairly high concentrations of large raindrops,and the mass-weighted raindrop diameter generally increases with the rain rate before reaching the equilibrium state of DSDs with the rain rate of about 50 mm h^(−1).Besides,polarimetric radar observations show the highest differential phase shift(K_(dp))and differential reflectivity(Z_(dr))near surface over Zhengzhou Station from 1500 to 1600 LST.In light of the remarkable temporal-spatial variability of DSDs,a reflectivity-grouped fitting approach is proposed to optimize the reflectivity-rain rate(Z-R)parameterization for radar quantitative precipitation estimation(QPE),and the rain gauge measurements are used for validation.The results show an increase of mean bias ratio from 0.57 to 0.79 and a decrease of root-mean-square error from 23.69 to 18.36 for the rainfall intensity above 20.0 mm h^(−1),as compared with the fixed Z-R parameterization.This study reveals the drastic temporal-spatial variations of rain microphysics during the Zhengzhou extreme rainfall event and warrants the promise of using reflectivity-grouped fitting Z-R relationships for radar QPE of such events.
基金primarily supported by the National Key Research and Development Program of China(Grant Nos.2017YFC1501703 and 2018YFC1506404)the National Natural Science Foundation of China(Grant Nos.41875053,41475015 and 41322032)+2 种基金the National Fundamental Research 973 Program of China(Grant Nos.2013CB430101 and2015CB452800)the Open Research Program of the State Key Laboratory of Severe Weatherthe Key Research Development Program of Jiangsu Science and Technology Department(Social Development Program,No.BE2016732)
文摘Dual-polarization(dual-pol)radar can measure additional parameters that provide more microphysical information of precipitation systems than those provided by conventional Doppler radar.The dual-pol parameters have been successfully utilized to investigate precipitation microphysics and improve radar quantitative precipitation estimation(QPE).The recent progress in dual-pol radar research and applications in China is summarized in four aspects.Firstly,the characteristics of several representative dual-pol radars are reviewed.Various approaches have been developed for radar data quality control,including calibration,attenuation correction,calculation of specific differential phase shift,and identification and removal of non-meteorological echoes.Using dual-pol radar measurements,the microphysical characteristics derived from raindrop size distribution retrieval,hydrometeor classification,and QPE is better understood in China.The limited number of studies in China that have sought to use dual-pol radar data to validate the microphysical parameterization and initialization of numerical models and assimilate dual-pol data into numerical models are summarized.The challenges of applying dual-pol data in numerical models and emerging technologies that may make significant impacts on the field of radar meteorology are discussed.
基金Supported by the National Natural Science Foundation of China(41575046)Project of Commonweal Technique and Application Research of Zhejiang Province of China(2016C33010)Project of Shanghai Meteorological Center of China(SCMO-ZF-2017011)。
文摘Currently,Doppler weather radar in China is generally used for quantitative precipitation estimation(QPE)based on the Z–R relationship.However,the estimation error for mixed precipitation is very large.In order to improve the accuracy of radar QPE,we propose a dynamic radar QPE algorithm with a 6-min interval that uses the reflectivity data of Doppler radar Z9002 in the Shanghai Qingpu District and the precipitation data at automatic weather stations(AWSs)in East China.Considering the time dependence and abrupt changes of precipitation,the data during the previous 30-min period were selected as the training data.To reduce the complexity of radar QPE,we transformed the weather data into the wavelet domain by means of the stationary wavelet transform(SWT)in order to extract high and low-frequency reflectivity and precipitation information.Using the wavelet coefficients,we constructed a support vector machine(SVM)at all scales to estimate the wavelet coefficient of precipitation.Ultimately,via inverse wavelet transformation,we obtained the estimated rainfall.By comparing the results of the proposed method(SWTSVM)with those of Z=300×R1.4,linear regression(LR),and SVM,we determined that the root mean square error(RMSE)of the SWT-SVM method was 0.54 mm per 6 min and the average Threat Score(TS)could exceed 40%with the exception of the downpour category,thus remaining at a high level.Generally speaking,the SWT-SVM method can effectively improve the accuracy of radar QPE and provide an auxiliary reference for actual meteorological operational forecasting.