Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

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.

Quantitative Precipitation Estimation Using X-band Phased Array Weather Radar

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.

Operational Evaluation of the Quantitative Precipitation Estimation by a CINRAD-SA Dual Polarization Radar System

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.

Radar Quantitative Precipitation Estimation Based on the Gated Recurrent Unit Neural Network and Echo-Top Data

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.

Application of X-band Polarimetric Phased-array Radars in Quantitative Precipitation Estimation

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.

A STUDY ON VARIABLE QUANTITATIVE PRECIPITATION ESTIMATION USING DOPPLER RADAR DATA

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.

QpefBD:A Benchmark Dataset Applied to Machine Learning for Minute-Scale Quantitative Precipitation Estimation and Forecasting

Nowcasts of strong convective precipitation and radar-based quantitative precipitation estimations have always been hot yet challenging issues in meteorological sciences.Data-driven machine learning,especially deep learning,provides a new technical approach for the quantitative estimation and forecasting of precipitation.A high-quality,large-sample,and labeled training dataset is critical for the successful application of machine-learning technology to a specific field.The present study develops a benchmark dataset that can be applied to machine learning for minutescale quantitative precipitation estimation and forecasting(QpefBD),containing 231,978 samples of 3185 heavy precipitation events that occurred in 6 provinces of central and eastern China from April to October 2016-2018.Each individual sample consists of 8 products of weather radars at 6-min intervals within the time window of the corresponding event and products of 27 physical quantities at hourly intervals that describe the atmospheric dynamic and thermodynamic conditions.Two data labels,i.e.,ground precipitation intensity and areal coverage of heavy precipitation at 6-min intervals,are also included.The present study describes the basic components of the dataset and data processing and provides metrics for the evaluation of model performance on precipitation estimation and forecasting.Based on these evaluation metrics,some simple and commonly used methods are applied to evaluate precipitation estimates and forecasts.The results can serve as the benchmark reference for the performance evaluation of machine learning models using this dataset.This paper also gives some suggestions and scenarios of the QpefBD application.We believe that the application of this benchmark dataset will promote interdisciplinary collaboration between meteorological sciences and artificial intelligence sciences,providing a new way for the identification and forecast of heavy precipitation.

Application of the Attenuation Correction Technology in C-band Radar Precipitation Estimation

[ Objective] The research aimed to study application of the attenuation correction technology in C-band radar precipitation estimation. [ Method~ Based on CINRAD-CB radar data in Shaanxi, we conducted the attenuation correction experiment by using iteration method and Kufa method respectively. Moreover, we conducted application expedment of the Kufa attenuation correction method in the quantitative precipitation esti- mation. [ Result~ Attenuation correction technology could compensate for attenuation problem of the echo at the distant range. Calculation result of the iteration method finally tended to that of the Kufa method. Moreover, iteration method spent more time. Therefore, Kufa attenuation correction technology was more suitable for business operation. When strong echo was near radar, generated attenuation was more obvious, and application value of the attenuation correction was bigger. Attenuation correction technology was used for quantitative precipitation estimation, which was favor- able for improving accuracy of the precipitation estimation. But we should conduct detailed planning on calculation scheme of the precipitation esti- mation because that different calculation schemes had great influences on accuracy of the quantitative precipitation estimation. [ Cendusien] This research provided a basis for improving accuracy of the quantitative precipitation estimation in Shaanxi. Key words Attenuation correction

A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts

Traditional precipitation skill scores are affected by the well-known"double penalty"problem caused by the slight spatial or temporal mismatches between forecasts and observations.The fuzzy(neighborhood)method has been proposed for deterministic simulations and shown some ability to solve this problem.The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts.We developed an ensemble precipitation verification skill score,i.e.,the Spatial Continuous Ranked Probability Score(SCRPS),and used it to extend spatial verification from deterministic into ensemble forecasts.The SCRPS is a spatial technique based on the Continuous Ranked Probability Score(CRPS)and the fuzzy method.A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency,which were then used in the reference score to calculate the skill score of the SCRPS.The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained.The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used.

A MODEL FOR QUANTITATIVELY ESTIMATING SHORTRANGE PRECIPITATION BASED ON GMS DIGITALIZED CLOUD MAPS-PART Ⅰ:ANALYSIS OF QUANTITATIVE CLOUD-PRECIPITATION RELATIONS AND MODEL DESIGN

Some typical samples are used to explore the quantitative correlation with their features between a convective cloud and its rainfall field,with which to develop two morphological functions for the correlation and by singling out their most suitable groups of parameters we propose a model for quantitatively estimating precipitation in the context o{ the in-advance recognition of meso-α convective system properties and its precipitating center.From the model fitting precision and forecasting accuracy we find that it is feasible to utilize geostationary meteorological satellite (GMS) digitalized imagery for estimating short-term rainfall in a quantitative manner.Also,evidence suggests that the model is supposed to be restricted in its applicability due to the fact that the employed samples are from rather typical rainfall events that are large-scale,slow-moving and have well-defined genesis and dissipative stages.

A MODEL FOR QUANTITATIVELY ESTIMATING SHORTRANGE PRECIPITATION BASED ON GMS DIGITALIZED CLOUD MAPS-PART Ⅱ:ASSESSMENT OF RESULTS FROM THE MODEL FOR SATELLITE-IMAGE QUANTITATIVELY ESTIMATING PRECIPITATION

This is Part Ⅱ of this series.It introduces the technique for recognizing MαCS phased properties and its precipitation center or centers by means of dynamic digitalized cloud maps and presents the assessment of the effectiveness of the model proposed in Part Ⅰ as to its fitting and forecasting accuracy.

Raindrop Size Distributions in the Zhengzhou Extreme Rainfall Event on 20 July 2021:Temporal-Spatial Variability and Implications for Radar QPE

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.

Variations of Raindrop Size Distribution and Radar Retrieval in Outer Rainbands of Typhoon Mangkhut(2018)

The evolution of the microphysical properties of raindrops from Typhoon Mangkhut’s outer rainbands as the storm made landfall in South China in September 2018 was investigated.The observations by three two-dimensional video disdrometers deployed in central Guangdong Province were analyzed concurrently.It was found that the radial distribution of the median volume diameter(D_(0))and normalized intercept parameter(N_(w))varied in different stages,and that raindrops smaller than 3.0 mm contributed more than 99%of the total precipitation.Considering the characteristics of precipitation in the typhoon outer rainband,a modified stratiform rain(SR)-convective rain(CR)separator line is proposed based on D_(0) and N_(w) scatterplots.Meanwhile,an“S-C likelihood index”is introduced,which was used to classify three rain types(SR,CR,and mixed rain).The CR results were highly consistent with those of the improved typhoon precipitation classification method based on rain rate.By calculating effectively the radar reflectivity factor(Ze)in the Ku and Ka bands,D0-Ze and N_(w)-D_(0) empirical relations were thereby derived for improving the accuracy of rainfall retrieval.Among the four quantitative precipitation estimators using S-band dual-polarimetric radar parameters simulated by the T-matrix method,the estimator that adopted the specific differential phase and differential reflectivity was found to be the most effective for both SR and CR.