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.

Statistics-based Optimization of the Polarimetric Radar Hydrometeor Classification Algorithm and Its Application for a Squall Line in South China

A modified hydrometeor classification algorithm （HCA） is developed in this study for Chinese polarimetric radars. This algorithm is based on the U.S. operational HCA. Meanwhile, the methodology of statistics-based optimization is proposed including calibration checking, datasets selection, membership functions modification, computation thresholds modification, and effect verification. Zhuhai radar, the first operational polarimetric radar in South China, applies these procedures. The systematic bias of calibration is corrected, the reliability of radar measurements deteriorates when the signal-to-noise ratio is low, and correlation coefficient within the melting layer is usually lower than that of the U.S. WSR-88D radar. Through modification based on statistical analysis of polarimetric variables, the localized HCA especially for Zhuhai is obtained, and it performs well over a one-month test through comparison with sounding and surface observations. The algorithm is then utilized for analysis of a squall line process on 11 May 2014 and is found to provide reasonable details with respect to horizontal and vertical structures, and the HCA results---especially in the mixed rain-hail region--can reflect the life cycle of the squall line. In addition, the kinematic and microphysical processes of cloud evolution and the differences between radar- detected hail and surface observations are also analyzed. The results of this study provide evidence for the improvement of this HCA developed specifically for China.

Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise(DBSCAN)algorithm.To identify convective and stratiform clouds in different developmental phases,two-dimensional(2D)and three-dimensional(3D)models are proposed by applying reflectivity factors at 0.5°and at 0.5°,1.5°,and 2.4°elevation angles,respectively.According to the thresholds of the algorithm,which include echo intensity,the echo top height of 35 dBZ(ET),density threshold,andεneighborhood,cloud clusters can be marked into four types:deep-convective cloud(DCC),shallow-convective cloud(SCC),hybrid convective-stratiform cloud(HCS),and stratiform cloud(SFC)types.Each cloud cluster type is further identified as a core area and boundary area,which can provide more abundant cloud structure information.The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing,Xuzhou,and Qingdao.The results show that cloud clusters can be intuitively identified as core and boundary points,which change in area continuously during the process of convective evolution,by the improved DBSCAN algorithm.Therefore,the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification.Because density thresholds are different and multiple elevations are utilized in the 3D model,the identified echo types and areas are dissimilar between the 2D and 3D models.The 3D model identifies larger convective and stratiform clouds than the 2D model.However,the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds.In addition,the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.

A Study of Structure and Mechanism of a Meso-beta-scale Convective Vortex and Associated Heavy Rainfall in the Dabie Mountain Area Part I: Diagnostic Analysis of the Structure

An analysis was conducted on the evolutional process of a mesoscale convective vortex （MCV） and associated heavy rainfall in the Dabie Mountain area on 21-22 June 2008,as well as their structural characteristics in different stages,by using the mesoscale reanalysis data with 3 km and 1 h resolution generated by the Local Analysis and Prediction System （LAPS） in the Southern China Heavy Rainfall Experiment.The results showed that the latent heat released by convection in the midtroposphere was the main energy source for the development of a low-level vortex.There was a positive feedback interaction between the convection and the vortex,and the evolution of the MCV was closely related to the strength of the positive interaction.The most typical characteristics of the thermal structure in different stages were that,there was a relatively thin diabatic heating layer in the midtroposphere in the formative stage;the thickness of diabatic heating layer significantly increased in the mature stage;and it almost disappeared in the decay stage.The characteristics of the dynamic structure were that,in the formative stage,there was no anticyclonic circulation at the high level;in the mature stage,an anticyclonic circulation with strong divergence was formed at the high level;in the decay stage,the anticyclonic circulation was damaged and the high-level atmosphere was in a disordered state of turbulence.Finally,the structural schematics of the MCV in the formative and mature stage were established respectively.

Water Cycle and Microphysical Processes Associated with a Mesoscale Convective Vortex System in the Dabie Mountain Area

The water vapor budget and the cloud microphysical processes associated with a heavy rainfall system in the Dabie Mountain area in June 2008 were analyzed using mesoscale reanalysis data（grid resolution 0.03 × 0.03,22 vertical layers,1-h intervals）,generated by amalgamating the local analysis and prediction system（LAPS）.The contribution of each term in the water vapor budget formula to precipitation was evaluated.The characteristics of water vapor budget and water substances in various phase states were evaluated and their differences in heavy and weak rainfall areas were compared.The precipitation calculated from the total water vapor budget accounted for 77% of actual precipitation;surface evaporation is another important source of water vapor.Water vapor within the domain of interest mainly came from the lower level along the southern boundary and the lower-middle level along the western boundary.This altitude difference for water vapor flux was caused by different weather systems.The decrease of local water vapor in the middle-lower layer in the troposphere during the system development stage also contributed to precipitation.The strength and the layer thickness of water vapor convergence and the content of various water substances in the heavy rainfall areas were obviously larger than in the weak rainfall areas.The peak values of lower-level water vapor convergence,local water vapor income,and the concentration of cloud ice all preceded the heaviest surface rainfall by a few hours.

Mesoscale Structure of Rainstorm Retrieved from Dual-Doppler Radar Observations Using the 4DVAR Assimilation Technique

The four-dimensional variational （4DVAR） data assimilation method was applied to dual-Doppler radar data about two Meiyu rainstorms observed during CHeRES （China Heavy Rain Experiment and Study）. The purpose of this study is to examine the performance of the 4DVAR technique in retrieving rainstorm mesoscale structure and to reveal the feature of rainstorm mesoscale structure. Results demonstrated that the 4DVAR assimilation method was able to retrieve the detailed structure of wind, thermodynamics, and microphysics fields from dual-Doppler radar observations. The retrieved wind fields agreed with the dual- Doppler synthesized winds and were accurate. The distributions of the retrieved perturbation pressure, perturbation temperature, and microphysics fields were also reasonable through the examination of their physical consistency. Both of the two heavy rainfalls were caused by merging cloud processes. The wind shear and convergence lines at middle and lower levels were their primary dynamical characteristics. The convective system was often related to low-level convergence and upper-level divergence coupled with up- drafts. During its mature stage, the convective system was characterized by low pressure at lower level and high pressure at upper level, associated with warmer at middle level and colder at lower and upper levels than the environment. However, a region of cooling and high pressure occurred in the lower and middle levels compared to warming and low pressure in the upper level during its dissipating ＇.stage. The water vapor, cloud water, and rainwater corresponded to the convergence, the updraft and the intensive reflectivity, respectively.