Accurate typhoon quantitative precipitation estimation (QPE) with polarimetric radar depends on the assumed drop-shape relation (DSR), but typhoon DSRs derived from surface 2D video disdrometer (2DVD) observations may not represent elevated radar sampling volumes. To address this issue, we apply the established polarimetric self-consistency framework to estimate radar-constrained effective linear DSRs from quality-controlled horizontal reflectivity factor (ZH), differential reflectivity (ZDR), and specific differential phase (KDP) observations using multi-sample optimization. The method estimates βeff, the slope parameter of the effective linear DSR, and is first evaluated using a separate non-typhoon event, for which the radar-constrained relation agrees well with the median axis ratios measured by a nearby 2DVD. It is then applied to six landfalling typhoons observed by the Guangzhou S-band polarimetric radar. The resulting βeff values range from 0.046 to 0.049 mm−1, indicating stable event-to-event behavior and greater effective oblateness than two published surface 2DVD-derived typhoon DSRs. Scattering simulations show that DSR choice has little effect on ZH but substantially affects ZDR and KDP, causing overly spherical DSRs to underestimate R(ZH, ZDR) and overestimate R(KDP). Gauge validation with more than 400 gauges shows that the radar-constrained effective and Brandes et al. DSRs provide more consistent rainfall estimates than the two surface-derived typhoon DSRs. Application without recalibration to Typhoon Lekima, observed by the independent Wenzhou S-band radar, reproduces the same overall performance grouping. These results identify a transferability limitation of surface-derived DSRs and demonstrate the value of radar-volume constraints for typhoon polarimetric QPE.

The mechanisms linking raindrop size distributions (DSDs) to environmental conditions remain poorly understood, limiting their practical application. We develop a unique fine‐scale vertical in situ data set to reveal the evolution of near‐surface DSDs and quantify how environmental factors modulate raindrop microphysics. Near‐surface raindrop breakup is identified as a common feature during the East Asian summer, with an average threshold diameter of 1.16 mm for breakup initialization. Further analysis reveals that relative humidity and wind speed exert opposing influences on raindrop microphysical processes, with coalescence favored in humid monsoon environments and breakup intensified within typhoon outer rainbands. By incorporating empirical relationships between these two environmental factors and microphysical processes, we derive observational constraints that significantly reduce biases in near‐surface rainfall estimates. For heavy rainfall cases the bias is reduced by up to 75%. These findings improve understanding of raindrop microphysics in boundary layer and help improve quantitative precipitation estimation.

The Hong Kong Observatory (HKO) installed an X-band dual-polarization Phased Array Weather Radar (PAWR) at its wind profiler station at Sha Lo Wan (SLW) in 2021 to monitor high-impact weather in Hong Kong. The PAWR could complete a volume scan in one minute with a spatial resolution of 30 meters. Dual polarimetric variables from the SLW PAWR, including differential reflectivity (ZDR), specific differential phase (KDP), and hydro-classification (HCL) products, were used to diagnose the vertical motion and lightning characteristics of mesoscale convective storms (MCS). Through variational data assimilation, three-dimensional (3-D) wind fields were constructed to validate the SLW PAWR observations. Two MCS events that occurred on 18 September 2022 and 17 June 2023 are central to this study. The findings include (1) negative ZDR serves as a good indicator of the occurrence of intense downdrafts associated with an MCS, a premise further supported by the 3-D wind field analysis results, (2) negative KDP suggested the formation of vertically aligned ice crystals which facilitated cloud electrification, and (3) HCL products indicated the presence of mixed ice crystals and graupel above the 0°C melting layer which promoted active cloud-to-cloud and cloud-to-ground lightning strokes. These results show that the SLW PAWR provides essential observations, which, when coupled with 3-D wind field analysis, can aid in enhancing the understanding of the dynamics and electrification processes within an MCS.

The heterogeneous land surface spanning the Yellow River irrigated oasis and the adjacent Kubuqi and Ulan Buh Desert (Hetao area) in Inner Mongolia, China, has been noted to frequently generate planetary boundary layer convergence line (BLCL), providing an important source of low-level lifting for convection initiation (CI). As the first field experiment to collect comprehensive observations of vegetation-contrast-resulting thermal circulations that consistently generate BLCLs and lead to CI, the Desert-Oasis Convergence Line and Deep Convection Experiment (DECODE) was conducted from 5 July to 9 August 2022 in the Hetao area. Two oasis and four desert observation sites were set up in the region that exhibits the highest frequency of BLCL and CI occurrences, equipped with a suite of advanced instruments probing land-atmosphere interactions, planetary boundary layer processes, and evolution of BLCLs and their associated CI, including Doppler lidars, microwave radiometers, soil temperature and moisture sensors, eddy covariance systems, portable radiosondes, C-band polarimetric Doppler radar, aircraft, and Geostationary High-speed Imager onboard FY-4B satellite. DECODE captured 29 BLCLs (16 with CI), 66 gust fronts, 12 horizontal convective rolls, and one tornado. The observations unveiled full thermal circulations spanning the desert-oasis boundary characterized by a horizontal width of-25 km, a convergence height of-1 km above ground level (AGL), and divergence from 2 to-3.5 km AGL, with vertical wind speeds of up to 2 m s-1. Future publications stemming from DECODE will delve into a spectrum of scientific inquiries, including but not limited to land surface and boundary layer processes, BLCL dynamics, CI mechanisms, convective organization, predictability, and model evaluation.

Sand and dust storm (SDS) weather has caused several severe hazards in many regions worldwide, e.g., environmental pollution, traffic disruptions, and human casualties. Widespread surveillance cameras show great potential for high spatiotemporal resolution SDS observation. This study explores the possibility of employing the surveillance camera as an alternative SDS monitor. Based on SDS image feature analysis, a Multi-Stream Attention-aware Convolutional Neural Network (MA-CNN), which learns SDS image features at different scales through a multi-stream structure and employs an attention mechanism to enhance the detection performance, is constructed for an accurate SDS observation task. Moreover, a dataset with 13,216 images was built to train and test the MA-CNN. Eighteen algorithms, including nine well-known deep learning models and their variants built on an attention mechanism, were used for comparison. The experimental results showed that the MA-CNN achieved an accuracy performance of 0.857 on the training dataset, while this value changed to 0.945, 0.919, and 0.953 in three different real-world scenarios, which is the optimal performance among the compared algorithms. Therefore, surveillance camera-based monitors can effectively observe the occurrence of SDS disasters and provide valuable supplements to existing SDS observation networks.

In this study, the quantitative precipitation estimation (QPE) capability of three X-band dual-polarization phased array radars (PAR) in Guangzhou, South China, was demonstrated, with an S-band operational dual-polarization radar as the benchmark. Rainfall rate (R) estimators based on the specific differential phase (KDP) for summer precipitation for both X-band and S-band radars were derived from the raindrop size distributions (DSDs) observed by a 2-dimensional video disdrometer (2DVD) in South China. Rainfall estimates from the radars were evaluated with gauge observations in three events, including pre-summer rainfall, typhoon precipitation, and local severe convective precipitation. Observational results showed that radar echoes from the X-band PARs suffered much more severely from attenuation than those from the S-band radar. Compared to S-band observations, the X-band echoes can disappear when the signal-to-noise ratio drops to a certain level due to severe attenuation, resulting in different estimated rainfall areas for X- and S-band radars. The attenuation corrected by KDP had good consistency with S-band observations, but the accuracy of attenuation correction was affected by DSD uncertainty and may vary in different types of precipitation. The QPE results demonstrated that the R(KDP) estimator produced better rainfall accumulations from the X-band PAR observations compared to the S-band observations. For both the X-band and S-band radars, the estimates of hourly accumulated rainfall became more accurate in heavier rainfall, due to the decreases of both the DSD uncertainty and the impact of measurement errors. In the heavy precipitation area, the estimation accuracy of the X-band radar was high, and the overestimation of the S-band radar was obvious. Through the analysis of the ZH-ZDR distribution in the three weather events, it was found that the X-band PAR with the capability of high spatiotemporal observations can capture minute-level changes in the microphysical characteristics, which help improve the estimation accuracy of ground rainfall.

This study analyzes microphysical signatures relevant to the short‐cycle lightning activity in the inner core of Typhoon Hato (2017) before landfall in China. Observations reveal that the lightning bursts were accompanied by enhanced inner‐core convection with a behavior cycle of about 3 hr. The coupling between wavenumber‐2 vortex Rossby waves (VRWs) and shear‐forced convective asymmetries resulted in a local updraft enhancement. Furthermore, supercooled liquid water droplets invigorated a striking enhancement of graupel via riming immediately above the freezing level, further enhancing charge separation and lightning generation outside the eyewall. Furthermore, when similar phase‐locking was associated with other propagating VRWs, graupel and updraft volumes were significantly boosted, leading to short‐cycle lightning outbreaks in the inner core.

Dual‐polarization radars can provide rich three‐dimensional (3D) information on cloud precipitation structure. To utilize the existing polarimetric radar network in operational data assimilation systems, a new polarimetric radar‐based cloud analysis method is introduced. New features include the employment of fuzzy‐logic hydrometeor classification, improved estimation of liquid and ice regions, and newly‐added number concentration estimation. The new scheme is evaluated with a typical squall line case. Results show that the 3D cloud precipitation structure and short‐term precipitation forecast is consistently improved. With extra information from the polarimetric observations, the new scheme is able to produce reasonable polarimetric signatures for analysis. More supercooled water results in more latent heat release which enhances the updraft, leading to stronger convection in the subsequent forecast. This study emphasizes the importance of correct initial liquid and ice particle condition for the prediction of deep convection.

This study presents a variational approach for optimized estimation of specific differential phasez for polarimetric radars using a linear forward operator. A cubic B-spline interpolating filter is included to mitigate the impact of measurement error in the total differential phase and ensure the spatial continuity of . For rain, non-negative constraints are introduced to ensure that the estimates are within the physical bounds. The variational approach is flexible to incorporate the background information constructed from the measurements of horizontal reflectivity factor and differential reflectivity based on the self-consistent relationship of polarimetric variables. The variational approach is evaluated using simulated experiments, as well as real observations from an S-band operational weather radar. Without including background information, the variational approach has slightly better performance compared to the approach based on linear programming (LP), and the background information helps to further improve the performance. In addition, the linear forward operator makes this variational approach computationally efficient. It needs less than 3x0025; computational power required by the approach based on LP, making it more suitable for real-time operational applications.

Typhoon Lekima (2019) possessed a double‐eyewall structure before making landfall in eastern China, with its outer eyewall showing quasi‐periodic convective intensification. Several physical mechanisms that may cause eyewall convection asymmetries were examined. The upshear occurrence of the strongest convection could not be explained by either the typhoon's motion or the effect of descending inflow from outer rainbands. Radar reflectivity analysis showed that phase locking occurred between the wavenumber‐2 vortex Rossby waves (VRWs) propagating radially outward from the inner eyewall and the azimuthally propagating wavenumber‐1 VRWs on the inner edge of the outer eyewall. Additional phase locking further arose between the aforementioned wavenumber‐1 VRWs and the azimuthally propagating wavenumber‐2 VRWs on the outer edge of the outer eyewall. These two phase‐locking processes led to the pronounced quasi‐periodic intensification of the convective asymmetry in the western semicircle of Lekima's outer eyewall.

Abstract The impact of assimilating China's operational X‐band Phased‐Array radar's (X‐PAR) data on the analysis and warning forecast of the vortex structure and intensity of the June 8, 2018 Foshan, Guangdong province, tornadic storm was investigated for the first time using an Ensemble Kalman Filter (EnKF) data assimilation system. Both radar radial velocity (Vr) and reflectivity (Z) from two S‐band operational radars and one X‐PAR were assimilated. Deterministic forecasts were launched every 6 min from 05:42 UTC (20 min before the tornado touched down) to 06:00 UTC from the EnKF mean analysis field. Five experiments were conducted to examine the added capability of Z assimilation of the EnKF system, and to investigate the impact of assimilating X‐PAR data on the analysis and prediction of the tornadic storm. Compared to the experiment without Z assimilation, the assimilation of Z reduced the analysis error and greatly reduced the forecast error of Z. The assimilation of X‐PAR data greatly improved the vortex structure of the tornadic storm at low levels, and improved the intensity of the rear inflow of the tornadic storm, especially with a higher assimilation frequency. Compared to the experiments without X‐PAR data assimilation, assimilating X‐PAR data improved the predictability of tornadic storm.

To better understand the characteristics of simulated raindrop size distributions (DSDs) and ice microphysical processes for convective systems in the East Asia monsoon region, a typical Meiyu event is simulated with the Weather Research and Forecasting (WRF) model using three two-moment bulk microphysics schemes in this study. The simulated microphysical characteristics are then evaluated using polarimetric radar observations and retrievals. Although the observed linear storm structures are well simulated in terms of their location, there are significant deviations regarding simulated polarimetric radar variables and DSD parameters when compared to the observations. Compared to radar retrievals, all the simulated low-level raindrops are found to have lower number concentration and larger mean sizes. To investigate the sources of the simulated DSD biases, vertical distributions of radar reflectivity and specific ice hydrometers (snow, graupel), as well as profiles of liquid/ice water contents from radar retrievals and simulations are further compared. In addition, variations about the occurrence frequency and transfer rate for four categories of ice processes (deposition, aggregation, riming, and melting) among the three schemes are analyzed. Results indicate that the overprediction of snow and graupel from riming processes is likely to be responsible for the production of extremely large raindrops at low levels.

The microphysical structure of Meiyu precipitation in Eastern China is investigated using two‐dimensional video disdrometer (2DVD) and S‐band polarimetric radar observations. The constrained‐gamma raindrop size distribution (DSD) model derived from 2DVD observations performs well in representing Meiyu DSDs. The vertical variability of polarimetric variables and retrieved DSD parameters are then investigated. The results show different patterns of vertical behavior for convective and stratiform rain due to different ice‐phase and precipitation microphysical processes. The radar reflectivity of stratiform rain presents a distinct bright band (with an average echo top between 6 and 7 km). In contrast, the convective rain shows a larger reflectivity with a relatively higher echo top at 8 km. Polarimetric signatures of convective rain above the 0°C isotherm imply the coexistence of rimed particles and aggregates, which is indispensable for the intense precipitation on the ground. However, with a bulk precipitation formed below the melting layer, warm rain processes are still critical pathways for the growth of raindrops and the subsequent generation of heavy rainfall. Furthermore, both convective and stratiform rain is dominated by raindrops <4 mm, and the increase in their rain intensity can mostly be attributed to the increase in raindrop concentration. The identified maritime nature of convective rain has a much higher (roughly more than two times) number concentration of raindrops than that of convection in a similar climate region. This study provides a more comprehensive picture of Meiyu precipitation microphysics in Eastern China.

The attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation A(H), which requires a good estimate of the ray-averaged ratio between A(H) and specific differential phase K-DP, also known as the coefficient alpha. In this study, a variational approach is proposed to optimize the coefficient alpha for better estimates of A(H) and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the alpha optimization approach using a slope of differential reflectivity Z(DR) dependence on horizontal reflectivity factor Z(H). Similar to the Z(DR)-slope approach, the variational approach can obtain the optimized alpha consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged a values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize alpha for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized alpha for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged alpha.

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.

Drop size distribution (DSD) is a fundamental parameter in rain microphysics. Retrieving DSDs from polarimetric radar measurements extends the capabilities of rain microphysics research and quantitative precipitation estimation. In this study, issues in rain DSD retrieval were studied with simulated and measured data. It was found that a three-parameter gamma distribution model was not suitable for directly retrieving DSD from polarimetric radar measurements. A statistical constraint, such as the shape-slope relation used in the constrained-gamma (C-G) distribution model, helped to reduce the uncertainties and errors in the retrieval. The inclusion of specific differential phase (K-DP) measurements resulted in more accurate DSD retrieval and rain physical parameter estimation if the measurement errors were properly characterized in the error minimization analysis (EMA), which was verified using two real precipitation events. The study demonstrated the potential of using full polarimetric radar measurements to improve rain DSD retrieval.

Polarimetric radar and disdrometer observations obtained during the 2014 Observation, Prediction, and Analysis of Severe Convection of China (OPACC) field campaign are used in this study to investigate the microphysical characteristics of three primary types of organized intense rainfall events (meiyu rainband, typhoon outer rainband, and squall line) in eastern China. Drop size distributions (DSDs) of these three events on the ground are derived from measurements of a surface disdrometer, while the corresponding three-dimensional microphysical structures are obtained from the Nanjing University C-band polarimetric radar (NJU-CPOL). Although the environmental moisture and instability conditions are different, all three events possess relatively high freezing level favorable for warm-rain processes where the high medium to small raindrop concentration at low levels is consistent with the high surface rainfall rates. Convection is tallest in the squall line where abundant ice-phase processes generate large amounts of rimed particles (graupel and hail) above the freezing level and the largest surface raindrops are present among these three events. The storm tops of both the typhoon and meiyu rainbands are lower than that in the squall line, composed of less active ice processes above the freezing level. The typhoon rainrate is more intense than that of meiyu, enhanced by higher coalescence efficiency. A revised generalized intercept parameter versus mass-weighted mean diameter (Nw-Dm) space diagram is constructed to describe the DSD distributions over the three events and illustrate the relative DSD positions for heavy precipitation. DSDs of these intense rainfall convections observed in this midlatitude region of eastern Asia somewhat represent the typical DSD characteristics in low latitudes, suggesting that the parameterization of microphysical characteristics in eastern China in numerical models needs to be further investigated to improve rain fall forecasts in these heavy rainfall events.

为研究梅雨期极端对流系统的微物理特征,利用2013-2014年江淮梅雨期间南京溧水S波段双偏振雷达探测资料和地面自动站小时降水资料,统计分析了两类极端对流降水系统的微物理特征及差异.这两类极端对流系统的定义基于地面降水强度和雷达回波顶高,分别为所有对流中降水强度最强的1％(R类:小时降水强度＞46.2mm/h)和对流发展高度最高的1％(H类:20 dBz回波顶高＞14.5 km).结果 显示这两类极端对流系统仅有30％的样本重合,显示了二者之间的弱相关性.对于相同的反射率因子ZH,R类极端对流系统的近地面差分反射率因子ZDR通常较H类极端对流小约0.2dB,表明R类极端对流具有较小的平均粒径.结合双偏振雷达反演的粒子大小和相态分布显示,虽然两类极端对流都表现出海洋性对流降水特征,但R类极端对流较H类极端对流的总体雨滴粒径更小而数浓度更高,导致R类极端对流系统的地面降水更强.与R类极端对流系统相比,H类极端对流系统的上升运动更强,将更多的水汽和过冷水输送到0℃层以上,有利于形成更大的冰相粒子(如霰粒子等),并通过融化形成大雨滴.以上研究表明,梅雨期降水强度和对流发展深度并没有必然的联系,极端降水主要是中等高度的对流引起.

A variational approach for optimized drop size distribution (DSD) retrieval, attenuation correction, and rainfall estimation from polarimetric radar measurements (horizontal reflectivity factor Z(H), differential reflectivity Z(DR), and differential phase shift Phi(DP)) is proposed in this study. The spatial continuity of rain is guaranteed by introducing the radial B-spine filter and azimuthal Kalman filter, which helps to mitigate the impact of the random errors in Z(DR) and Phi(DP). The approach is evaluated using a simulated experiment and two real cases observed by a mobile C-band and an operational S-band polarimetric radar in south China. The comparative results demonstrate that the proposed variational approach can correct attenuation more accurately than the conventional method using assumed linear relationships between specific attenuation and specific differential phase. The rainfalls derived from the DSD estimates based on this proposed approach agree well with the rain gauge measurements. It is also shown that the proposed method has a better performance than the existing variational retrieval and the composite estimator.

Quantitative precipitation estimation (QPE) with polarimetric radar measurements suffers from different sources of uncertainty. The variational approach appears to be a promising way to optimize the radar QPE statistically. In this study a variational approach is developed to quantitatively estimate the rainfall rate (R) from the differential phase (ΦDP). A spline filter is utilized in the optimization procedures to eliminate the impact of the random errors in ΦDP, which can be a major source of error in the specific differential phase (KDP)-based QPE. In addition, R estimated from the horizontal reflectivity factor (ZH) is used in the a priori with the error covariance matrix statistically determined. The approach is evaluated by an idealized case and multiple real rainfall cases observed by an operational S-band polarimetric radar in southern China. The comparative results demonstrate that with a proper range filter, the proposed variational radar QPE with the a priori included agrees well with the rain gauge measurements and proves to have better performance than the other three approaches, that is, the proposed variational approach without the a priori included, the variational approach proposed by Hogan, and the conventional power-law estimator-based approach.