The constant development of science and technology in weather radar results in high-resolution spatial and temporal rainfall estimates and improved early warnings of meteorological phenomena such as flood [1]. Weather...The constant development of science and technology in weather radar results in high-resolution spatial and temporal rainfall estimates and improved early warnings of meteorological phenomena such as flood [1]. Weather radars do not measure the rainfall amount directly, so a relationship between the reflectivity (Z) and rainfall rate (R), called the Z-R relationship (Z = aR<sup>b</sup>), where a and b are empirical constants, can be used to estimate the rainfall amount. In this research, mathematical techniques were used to find the best climatological Z-R relationships for the Low Coastal Plain of Guyana. The reflectivity data from the S-Band Doppler Weather Radar for February 17 and 21, 2011 and May 8, 2012 together with the daily rainfall depths at 29 rainfall stations located within a 150 km radius were investigated. A climatological Z-R relationship type Z = 200R<sup>1.6</sup> (Marshall-Palmer) configured by default into the radar system was used to investigate the correlation between the radar reflectivity and the rainfall by gauges. The same data sets were used with two distinct experimental Z-R relationships, Z = 300R<sup>1.4</sup> (WSR-88D Convective) and Z = 250R<sup>1.2</sup> (Rosenfeld Tropical) to determine if any could be applicable for area of study. By comprehensive regression analysis, New Z-R and R-Z relationships for each of the three events aforementioned were developed. In addition, a combination of all the samples for all three events were used to produce another relationship called “All in One”. Statistical measures were then applied to detect BIAS and Error STD in order to produce more evidence-based results. It is proven that different Z-R relationships could be calibrated into the radar system to provide more accurate rainfall estimation.展开更多
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, se...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.展开更多
Data from rain Drop Size Distributions gathered on five sites in Africa as well as those of the pilot site in Kourou (French Guyana, South America), located in different climatic zones, and collected by two types of d...Data from rain Drop Size Distributions gathered on five sites in Africa as well as those of the pilot site in Kourou (French Guyana, South America), located in different climatic zones, and collected by two types of disdrometer (the impact JW RD-69 disdrometer and the Optical Spectro-Pluviometer, OSP) are used to study the consistency of the reflectivity factor-rain rate at the ground (Z-R) relationship variability. The results clearly confirm that the relationship Z-R knows a large spatial variability, from a type of precipitation to another and within the same precipitation regardless the type of disdrometer used for DSD measurements. Base on the similarity of the relations reflectivity factor-rain rate and ratio median volume diameter over the total number of drops-rain rate, the variability of the Z-R coefficients (A, b) through the simultaneously implication of the size and number of drops which characterize the DSD was exhibited. It was shown that the relationships A-α and b-β designed to understand the involvement of parameters D0 and NT of DSD in the variability of the relationship Z-R are similar regardless the types of disdrometer used. However, the relations A-α in the Sahelian region appear to deviate from those of Guinean, equatorial and Soudanian zones. The plausible reasons were discussed.展开更多
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). I...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 similar.To 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.展开更多
While heavy rainfall frequently takes place in southern China during summer monsoon seasons,quantitative precipitation forecast skills are relatively poor.Therefore,detailed knowledge about the raindrop size distribut...While heavy rainfall frequently takes place in southern China during summer monsoon seasons,quantitative precipitation forecast skills are relatively poor.Therefore,detailed knowledge about the raindrop size distribution(DSD)is useful in improving the quantitative precipitation estimation and forecast.Based 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 considered.The 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 distribution.The 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 rainfall.Dmis 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 characteristics with distinct differences.Furthermore,frontal system rainfall tends to present a continental-like rainfall,tropical cyclone rainfall tends to have a maritime-like rainfall,and summer monsoon rainfall characteristic are between maritime-and continental-like cluster(raindrop concentration and diameter are higher than continental cluster and maritime cluster,respectively.)展开更多
Radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological purposes.Since the Z-R relation between radar and rain has a number of parameters on ...Radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological purposes.Since 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 estimation.This 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 limitations.We report on our investigation into contrast reversal experiments with radar echo and rainfall data collected by the Hunan Meteorological Observatory.Experimental 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.展开更多
For the Z-R relationship in radar-based rainfall estimation, the distribution of corresponding R values for a given Z value (or the corresponding Z value for a given R value) may be highly skewed. However, the traditi...For the Z-R relationship in radar-based rainfall estimation, the distribution of corresponding R values for a given Z value (or the corresponding Z value for a given R value) may be highly skewed. However, the traditional power-law model is physically deduced and fitted under the normal-distribution presumption of radar wave echoes associated with a rain rate value, and it may not be very appropriate. Considering this problem, the authors devised several generalized linear models with different forms and distribution presumptions to represent the Z-R relationship. Radar-reflectivity scans observed by a CINRAD/SC Doppler radar and 5-minute rainfall accumulation recorded by 10 ground gauges were used to fit these models. All data used in this study were collected during some large rainfalls of the period from 2005 to 2007. The radar and all gauges were installed in the catchment of the Yishu River, a branch of the Huaihe River in China. Three models based on normal distribution and a dBZ presumption of gamma distribution were fitted using maximum-likelihood techniques, which were resolved by genetic algorithms. Comparisons of estimated maximized likelihoods based on assumptions of gamma and normal distribution showed that all generalized linear models (GLMs) of presumed gamma distribution were better fitted than GLMs based on normal distribution. In a comparison of maximum-likelihood, the differences between these three models were small. Three error statistics were used to assess the agreement between radar estimated rainfall and gauge rainfall: relative bias (B), root mean square error (RMSE), and correlation coefficient (r). The results showed that no one model was excellent in all criteria. On the whole, the GLM-based models gave smaller relative bias than the traditional power-law model. It is suggested that validations conducted in many previous works should have been made against a specific criterion but overlooked others.展开更多
The regular occurrence of flash floods over the region of Jeddah, Saudi Arabia in the past decade has highlighted the serious need for the development of early warning systems. Radar stations have been installed in Je...The regular occurrence of flash floods over the region of Jeddah, Saudi Arabia in the past decade has highlighted the serious need for the development of early warning systems. Radar stations have been installed in Jeddah in the last decade whose active radius covers the Middle Western area of the country. Therefore, radar information and the associated the rainfall estimates are potentially useful components of an effective early warning system. Weather radar can potentially provide high-resolution spatial and temporal rainfall estimates that bring more accuracy to flood warnings as well as having applications in areas with insufficient rainfall stations coverage. Weather radar does not measure rainfall depth directly. An empirical relationship between reflectivity (Z) and rainfall rate (R), called the Z-R relationship (Z = ARb), is generally used to assess the rainfall depth. In this study, the rainfall events during August-September 2007 were analyzed to develop a Z-R relationship using the Spatial Probability Technique (SPT). This technique is based on a basic GIS function and the probability matching method. Using this technique, the Z-R pairs can be analyzed for both linear and empirical power relationships. It is found that the empirical power function is more appropriate to describe Z-R relationship than a linear function for the studied area. The method is applied with some success to the flooding event of November 25, 2009. However, the investigation of the Z-R relationship is only one step in the development of a warning system;further study of other parameters relevant to rainfall and flash flood occurrence is needed.展开更多
This study evaluates the improvement of the radar Quantitative Precipitation Estimation (QPE) by involving microphysical processes in the determination of </span><i><span style="font-family:Verdana...This study evaluates the improvement of the radar Quantitative Precipitation Estimation (QPE) by involving microphysical processes in the determination of </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms. Within the framework of the AMMA campaign, measurements of an X-band radar (Xport), a vertical pointing Micro Rain Radar (MRR) to investigate microphysical processes and a dense network of rain </span><span style="font-family:Verdana;">gauges deployed in Northern Benin (West Africa) in 2006 and 2007 were</span><span style="font-family:Verdana;"> used as support to establish such estimators and evaluate their performance compared to other estimators in the literature. By carefully considering and correcting MRR attenuation and calibration issues, the </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> estimator developed </span><span style="font-family:Verdana;">with the contribution of microphysical processes and non-linear least</span></span><span style="font-family:Verdana;">-</span><span style="font-family:""><span style="font-family:Verdana;">squares adjustment proves to be more efficient for quantitative rainfall estimation and produces the best statistic scores than other optimal </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms in the literature. We also find that it gives results comparable to some polarimetric algorithms including microphysical information through DSD integrated parameter retrievals.展开更多
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 ...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. This suggests that the new statistical Z-R relationship is more suitable for predicting severe con- vective weather over the Yangtze-Huaihe River basin than the Z-R relationships currently in use.展开更多
Freezing rain(FZR)presents significant risks to energy,transportation,and agriculture,leading to substantial economiclossesandcasualties,particularlyinsouthwestern,central,andeasternChina,withonlyoccasionaloccurrences...Freezing rain(FZR)presents significant risks to energy,transportation,and agriculture,leading to substantial economiclossesandcasualties,particularlyinsouthwestern,central,andeasternChina,withonlyoccasionaloccurrences in northern China.This study investigates an extreme,large-scale FZR event that occurred during 8–9 November 2021 in Heilongjiang Province of Northeast China,marking the region’s most intense FZR since 1958.Surface station observations revealed distinct characteristics of the FZR,and the stations were classified into three types by using the k-means clustering:stations with continuous FZR(FZR_Con),stations with FZR of mixed hydrometeor types(FZR_Mix),and stations with FZR transitioning to rain(FZR_Rain).Vertical atmospheric temperature and humidity profiles significantly influenced the raindrop size distribution(DSD)for the three station types.All three station types exhibited an inversion layer in the upper atmosphere,though they formed through two distinct mechanisms:(1)the supercooled warm rain mechanism and(2)the melting mechanism.This study found that the massweighted mean diameters(D--m)were larger than those observed in FZR events in central China and in stratiform rain in northern and northwestern China.FZR_Mix,which formed through the supercooled warm rain mechanism,exhibited the largest D_(m) among the three types.In contrast,FZR_Con and FZR_Rain formed through the melting mechanism,involving the melting of ice crystals and snow particles.The drier refreezing layer in FZR_Rain,compared to FZR_Con,resulted in a lower normalized number concentration(N_(w))and a larger D_(m).Positive exponential relationships between D_(m) and R(precipitation rate),as well as N_(w) and R,across all FZR types,highlighting dominant role of microphysical processes such as collision and coalescence.Variations in the gamma distribution parameters—shape(μ)and slope(λ)—as well as in the radar Z–R relationships among the FZR types further underscore differences in the microphysical processes and regional precipitation characteristics.This study enhances our understanding of the macro-and microphysical properties of FZR formed through different mechanisms,providing valuable reference for improved radar-based precipitation estimation in mid-and high-latitude regions.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
文摘The constant development of science and technology in weather radar results in high-resolution spatial and temporal rainfall estimates and improved early warnings of meteorological phenomena such as flood [1]. Weather radars do not measure the rainfall amount directly, so a relationship between the reflectivity (Z) and rainfall rate (R), called the Z-R relationship (Z = aR<sup>b</sup>), where a and b are empirical constants, can be used to estimate the rainfall amount. In this research, mathematical techniques were used to find the best climatological Z-R relationships for the Low Coastal Plain of Guyana. The reflectivity data from the S-Band Doppler Weather Radar for February 17 and 21, 2011 and May 8, 2012 together with the daily rainfall depths at 29 rainfall stations located within a 150 km radius were investigated. A climatological Z-R relationship type Z = 200R<sup>1.6</sup> (Marshall-Palmer) configured by default into the radar system was used to investigate the correlation between the radar reflectivity and the rainfall by gauges. The same data sets were used with two distinct experimental Z-R relationships, Z = 300R<sup>1.4</sup> (WSR-88D Convective) and Z = 250R<sup>1.2</sup> (Rosenfeld Tropical) to determine if any could be applicable for area of study. By comprehensive regression analysis, New Z-R and R-Z relationships for each of the three events aforementioned were developed. In addition, a combination of all the samples for all three events were used to produce another relationship called “All in One”. Statistical measures were then applied to detect BIAS and Error STD in order to produce more evidence-based results. It is proven that different Z-R relationships could be calibrated into the radar system to provide more accurate rainfall estimation.
文摘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.
文摘Data from rain Drop Size Distributions gathered on five sites in Africa as well as those of the pilot site in Kourou (French Guyana, South America), located in different climatic zones, and collected by two types of disdrometer (the impact JW RD-69 disdrometer and the Optical Spectro-Pluviometer, OSP) are used to study the consistency of the reflectivity factor-rain rate at the ground (Z-R) relationship variability. The results clearly confirm that the relationship Z-R knows a large spatial variability, from a type of precipitation to another and within the same precipitation regardless the type of disdrometer used for DSD measurements. Base on the similarity of the relations reflectivity factor-rain rate and ratio median volume diameter over the total number of drops-rain rate, the variability of the Z-R coefficients (A, b) through the simultaneously implication of the size and number of drops which characterize the DSD was exhibited. It was shown that the relationships A-α and b-β designed to understand the involvement of parameters D0 and NT of DSD in the variability of the relationship Z-R are similar regardless the types of disdrometer used. However, the relations A-α in the Sahelian region appear to deviate from those of Guinean, equatorial and Soudanian zones. The plausible reasons were discussed.
基金jointly supported by the National Science Foundation of China (Grant Nos. 42275007 and 41865003)Jiangxi Provincial Department of science and technology project (Grant No. 20171BBG70004)。
文摘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 similar.To 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.
基金National Natural Science Foundation of China(42075014,41975138)Natural Science Foundation of Guangdong Province(2022A1515011814,2021A1515011539,2020A1515010602)+3 种基金Open Grants of State Key Laboratory of Severe Weather(2022LASW-B15)Radar Application and Short-term Severe-weather Predictions and Warnings Technology Program(GRMCTD202002)Key Scientific and Technological Research Project of GRMC(GRMC2020Z03)Water Resource Science and Technology Innovation Program of Guangdong Province(2022-02)。
文摘While heavy rainfall frequently takes place in southern China during summer monsoon seasons,quantitative precipitation forecast skills are relatively poor.Therefore,detailed knowledge about the raindrop size distribution(DSD)is useful in improving the quantitative precipitation estimation and forecast.Based 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 considered.The 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 distribution.The 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 rainfall.Dmis 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 characteristics with distinct differences.Furthermore,frontal system rainfall tends to present a continental-like rainfall,tropical cyclone rainfall tends to have a maritime-like rainfall,and summer monsoon rainfall characteristic are between maritime-and continental-like cluster(raindrop concentration and diameter are higher than continental cluster and maritime cluster,respectively.)
基金This work is supported by the Key Research and Development Program of Hunan Province(No.2019SK2161)the Key Research and Development Program of Hunan Province(No.2016SK2017).
文摘Radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological purposes.Since 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 estimation.This 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 limitations.We report on our investigation into contrast reversal experiments with radar echo and rainfall data collected by the Hunan Meteorological Observatory.Experimental 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.
基金financially supported by the National Natural Science Foundation of China (Grant No. 40971024)the National Basic Research Program of China (Grant No. 2006CB400502)the Special Meteorology Project (GYHY(QX)2007-6-1)
文摘For the Z-R relationship in radar-based rainfall estimation, the distribution of corresponding R values for a given Z value (or the corresponding Z value for a given R value) may be highly skewed. However, the traditional power-law model is physically deduced and fitted under the normal-distribution presumption of radar wave echoes associated with a rain rate value, and it may not be very appropriate. Considering this problem, the authors devised several generalized linear models with different forms and distribution presumptions to represent the Z-R relationship. Radar-reflectivity scans observed by a CINRAD/SC Doppler radar and 5-minute rainfall accumulation recorded by 10 ground gauges were used to fit these models. All data used in this study were collected during some large rainfalls of the period from 2005 to 2007. The radar and all gauges were installed in the catchment of the Yishu River, a branch of the Huaihe River in China. Three models based on normal distribution and a dBZ presumption of gamma distribution were fitted using maximum-likelihood techniques, which were resolved by genetic algorithms. Comparisons of estimated maximized likelihoods based on assumptions of gamma and normal distribution showed that all generalized linear models (GLMs) of presumed gamma distribution were better fitted than GLMs based on normal distribution. In a comparison of maximum-likelihood, the differences between these three models were small. Three error statistics were used to assess the agreement between radar estimated rainfall and gauge rainfall: relative bias (B), root mean square error (RMSE), and correlation coefficient (r). The results showed that no one model was excellent in all criteria. On the whole, the GLM-based models gave smaller relative bias than the traditional power-law model. It is suggested that validations conducted in many previous works should have been made against a specific criterion but overlooked others.
文摘The regular occurrence of flash floods over the region of Jeddah, Saudi Arabia in the past decade has highlighted the serious need for the development of early warning systems. Radar stations have been installed in Jeddah in the last decade whose active radius covers the Middle Western area of the country. Therefore, radar information and the associated the rainfall estimates are potentially useful components of an effective early warning system. Weather radar can potentially provide high-resolution spatial and temporal rainfall estimates that bring more accuracy to flood warnings as well as having applications in areas with insufficient rainfall stations coverage. Weather radar does not measure rainfall depth directly. An empirical relationship between reflectivity (Z) and rainfall rate (R), called the Z-R relationship (Z = ARb), is generally used to assess the rainfall depth. In this study, the rainfall events during August-September 2007 were analyzed to develop a Z-R relationship using the Spatial Probability Technique (SPT). This technique is based on a basic GIS function and the probability matching method. Using this technique, the Z-R pairs can be analyzed for both linear and empirical power relationships. It is found that the empirical power function is more appropriate to describe Z-R relationship than a linear function for the studied area. The method is applied with some success to the flooding event of November 25, 2009. However, the investigation of the Z-R relationship is only one step in the development of a warning system;further study of other parameters relevant to rainfall and flash flood occurrence is needed.
文摘This study evaluates the improvement of the radar Quantitative Precipitation Estimation (QPE) by involving microphysical processes in the determination of </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms. Within the framework of the AMMA campaign, measurements of an X-band radar (Xport), a vertical pointing Micro Rain Radar (MRR) to investigate microphysical processes and a dense network of rain </span><span style="font-family:Verdana;">gauges deployed in Northern Benin (West Africa) in 2006 and 2007 were</span><span style="font-family:Verdana;"> used as support to establish such estimators and evaluate their performance compared to other estimators in the literature. By carefully considering and correcting MRR attenuation and calibration issues, the </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> estimator developed </span><span style="font-family:Verdana;">with the contribution of microphysical processes and non-linear least</span></span><span style="font-family:Verdana;">-</span><span style="font-family:""><span style="font-family:Verdana;">squares adjustment proves to be more efficient for quantitative rainfall estimation and produces the best statistic scores than other optimal </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms in the literature. We also find that it gives results comparable to some polarimetric algorithms including microphysical information through DSD integrated parameter retrievals.
基金Supported by the National(Key)Basic Research and Development(973)Program of China(2013CB430102)National Natural Science Foundation of China(41275102 and 41330527)
文摘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. This suggests that the new statistical Z-R relationship is more suitable for predicting severe con- vective weather over the Yangtze-Huaihe River basin than the Z-R relationships currently in use.
基金Supported by the Hubei Provincial Natural Science Foundation and Meteorological Innovation and Development Project of China(2023AFD096,2022CFD122,and 2023AFD100)Science and Technology Development Fund of Hubei Meteorological Bureau(2023Y18)+3 种基金Qinghai Province 2023 Key R&D and Transformation Plan(2023-SF-111)Special Program for Innovation and Development of China Meteorological Administration(CXFZ2022J010)Natural Science Foundation of Wuhan(2024020901030454)CMA Meteorological Observation Centre Field Experiment Project in 2024(GCSYJH24-30)。
文摘Freezing rain(FZR)presents significant risks to energy,transportation,and agriculture,leading to substantial economiclossesandcasualties,particularlyinsouthwestern,central,andeasternChina,withonlyoccasionaloccurrences in northern China.This study investigates an extreme,large-scale FZR event that occurred during 8–9 November 2021 in Heilongjiang Province of Northeast China,marking the region’s most intense FZR since 1958.Surface station observations revealed distinct characteristics of the FZR,and the stations were classified into three types by using the k-means clustering:stations with continuous FZR(FZR_Con),stations with FZR of mixed hydrometeor types(FZR_Mix),and stations with FZR transitioning to rain(FZR_Rain).Vertical atmospheric temperature and humidity profiles significantly influenced the raindrop size distribution(DSD)for the three station types.All three station types exhibited an inversion layer in the upper atmosphere,though they formed through two distinct mechanisms:(1)the supercooled warm rain mechanism and(2)the melting mechanism.This study found that the massweighted mean diameters(D--m)were larger than those observed in FZR events in central China and in stratiform rain in northern and northwestern China.FZR_Mix,which formed through the supercooled warm rain mechanism,exhibited the largest D_(m) among the three types.In contrast,FZR_Con and FZR_Rain formed through the melting mechanism,involving the melting of ice crystals and snow particles.The drier refreezing layer in FZR_Rain,compared to FZR_Con,resulted in a lower normalized number concentration(N_(w))and a larger D_(m).Positive exponential relationships between D_(m) and R(precipitation rate),as well as N_(w) and R,across all FZR types,highlighting dominant role of microphysical processes such as collision and coalescence.Variations in the gamma distribution parameters—shape(μ)and slope(λ)—as well as in the radar Z–R relationships among the FZR types further underscore differences in the microphysical processes and regional precipitation characteristics.This study enhances our understanding of the macro-and microphysical properties of FZR formed through different mechanisms,providing valuable reference for improved radar-based precipitation estimation in mid-and high-latitude regions.
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.
