Melting Layers (ML) during winter precipitation events, characterized by low-altitude, high temporal variability and spatially heterogeneous melting, pose significant challenges to conventional dual-polarization radar Hydrometeor Classification Algorithms (HCA), resulting in substantially degraded performance in rain-snow transition regions. To address these challenges, we develop an improved HCA scheme using data from 115 dual-polarization radars and vertical profiles from 116 radiosonde stations across China (July 2023u2014July 2024). The scheme integrates three key components: Optimized spatiotemporal matching with radiosonde observations, real-time ML monitoring through Quasi-Vertical Profile (QVP) analysis, and three-dimensional ML identification using the Melting Layer Detection Algorithm (MLDA). These improvements enhance both spatiotemporal precision of ML detection and regional applicability of HCA, ultimately improving the discrimination between various hydrometeor types. The improved scheme effectively resolves issues related to rapid ML variability and spatial heterogeneity in winter, reducing the ML detection update interval from 6u201412 h to 6 min and achieving spatial resolution of 1 km in range, 0.1 km in altitude, and 1u00B0 in azimuth. Sensitivity experiments using wintertime datasets from seven radars around Nanjing demonstrate that the improved scheme can accurately identify the rain-snow boundary, increasing overall classification accuracy by 11.91% and mixed-phase precipitation accuracy by over 50%. Validation against Present Weather Sensor (PWS) observations confirms that the near-surface classification accuracy exceeds 77% within 100 km. Statistics on winter algorithm activation periods from 115 radars nationwide reveal that the winter-specific algorithm operates for 18%u201451% of the year at plain sites in Northeast, North, and Central China, as well as at high-altitude mountain sites, confirming the effectiveness of the improved scheme for wintertime dual-polarization radar hydrometeor classification.

Abstract The differential reflectivity (Z DR ) column is a notable radar signature in thunderstorms, serving as a proxy for updrafts and an early indicator of storm intensity. However, a quantitative relationship among the Z DR column morphology, related microphysical characteristics, and dynamic structures based on sufficient field observations has not yet been reported. To explore these quantitative relationships, we develop a three-dimensional Z DR column identification method based on 100 Z DR columns within severe thunderstorms (primarily supercells) in the Pearl River Delta region of South China from April to July in 2021 and 2024. 3D-connected component labeling with horizontal and vertical tolerance mechanisms is integrated into the method to ensure the structural independence and integrity of multiple Z DR columns coexisting within the same thunderstorm. This method shows that Z DR columns in severe thunderstorms exhibit mean depths of approximately 2 km, mean widths of approximately 6 km, mean volumes of approximately 30 km 3 , 30–70 min lifecycles, and high liquid water contents (mean =2.23 g m −3 ). Quantitative relationships between three morphological parameters (volume, width, and depth) of Z DR columns and updraft volumes retrieved from multiradar wind fields using 1–11 m s −1 thresholds were also determined. All three morphological parameters of the Z DR column outperform the traditional 35 dBZ echo top height in indicating updraft intensity. In addition, the distribution of hydrometeors was explored. Our results, derived from extensive observations, quantitatively highlight the strong relationship between Z DR column morphology and thunderstorm dynamic structure, providing a theoretical foundation for improving severe weather warnings and forecasts.

Snow formation is a complex interplay of multiple microphysical growth processes, and the prevailing snow characteristics are inherently linked to local climate. However, the persistent shortage of observations for characterizing snow microphysics at a global scale continues to constrain our understanding of snow growth processes. Here, we investigate snow riming and aggregation signatures in stratiform precipitation through triple-frequency radar observations collected during coordinated field campaigns across Southern China, the Eastern United States, Western Europe, Northern Europe and Antarctica. The results suggest that the velocity-based riming estimates are generally consistent with triple-frequency observations, and the riming frequency increases with temperature. Our analysis of dual-frequency observations in these field campaigns qualitatively indicate the dendritic growth zone around -15 degrees C playing a key role in initiating enhanced snow size growth, and reveals a generally temperature-dependent snowflake growth characteristics. The snow over Eastern US is characterized by the most prominent riming growth, corresponding to moderate to heavy riming. Triple-frequency signatures of snowflakes over west Europe are consistent with Southern China, while the latter shows a higher degree of riming. The weakest snow growth signatures were found over west Antarctica, potentially owing to the scarcity of ice nucleating particles and available water vapor for deposition. In addition, our statistics reveal a latitudinal dependence for snowfall detection limitations with current spaceborne Ku- and Ka-band radars, and shed novel insights into future triple-frequency satellite missions as well as joint application of weather and spaceborne radars.

The melting layer (ML) in stratiform precipitation can have attenuation effects on microwave signals, introducing major uncertainties in remote sensing and telecommunications. In this study, we derived parameterizations for ML attenuation at the Ku- and Ka-bands, which are frequencies widely used in ground-based and spaceborne meteorological radars. To achieve this, a Doppler-spectra-based dual-frequency differential approach was applied to a unique, long-term multi-frequency radar data set. Our results suggest that total ML attenuation strongly depends on the ML thickness, while the specific ML attenuation aligns well with previous case studies and simulations. Furthermore, we quantified the impact of riming snow on ML attenuation, and found that the Ka-band specific ML attenuation for rimed snow is nearly half that of unrimed snow in light rainfall, whereas this distinction becomes obscured as the rain rate increases. Based on the Ku- and Ka-band ML parameterizations (derived as functions of rain rate and reflectivity, respectively), we extended these parameterizations to the W-band to facilitate broader applications across diverse radar missions. By presenting the first long-term observational analysis of Ku- and Ka-band ML attenuation, this study facilitates the accurate quantification of ML attenuation, serving as a unique reference for ground-based and spaceborne radar observations.

Abstract The study addressed the challenges of low detection probability and limited vortex feature extraction capabilities when applying China’s X-band phased array radar (XPAR) data to the National Severe Storms Laboratory mesocyclone and tornado detection algorithms (OriMDA and OriTDA), which were originally designed for S-band radar data. Using a dataset of 65 meso-γ-scale vortices (MVs) and 15 tornadic vortex signatures (TVSs) collected from five XPAR radars in Guangdong, China (June–July 2022), modified algorithms (NewMDA and NewTDA) that adopt newly revised localized vortex detection standards (New_Std) were introduced and evaluated. The NewMDA and NewTDA with New_Std showed improvements, yielding a probability of detection (POD) of 0.91 and 0.87, a false alarm ratio (FAR) of 0.40 and 0.56, and a critical success index (CSI) of 0.56 and 0.40, respectively. In comparison, applying the original vortex detection standards (Ori_Std) to NewMDA and NewTDA yielded a POD of 0.60 and 0.19, a FAR of 0.16 and 0.51, and a CSI of 0.54 and 0.16, respectively, while OriMDA and OriTDA produced a POD of 0.61 and 0.20, a FAR of 0.32 and 0.97, and a CSI of 0.47 and 0.02, respectively. NewMDA demonstrated an improved ability to differentiate between multiple MVs by resolving their three-dimensional structures, while NewTDA provided more accurate shear characteristics and captured more complete tornado evolution. When applied to S-band radar data, the modified algorithms performed consistently with their original counterparts, with modest improvements in TVS identification (POD increased by 0.06, FAR decreased by 0.07, and CSI improved by 0.05). Further analysis on the impact of radar wavelength differences between S-band and X-band radar data on MV and TVS detection suggested that the performance of the S-band radar exhibited a certain gap compared to XPAR, yielding a POD of 0.21 and 0.07, a FAR of 0.07 and 0.05, and a CSI of 0.21 and 0.07 for MVs and TVSs, respectively, along with indications of vortex degradation featuring weaker shear intensity, shallower depth, and shorter duration.

The operational utility of S-band weather radars—the backbone of the China New Generation Weather Radar (CINRAD) network—is severely compromised by ground clutter from dense high-rise buildings and surrounding mountainous terrain in megacities. Although over 170 units have been upgraded to dual-polarization, the extent to which such clutter systematically degrades data quality, hydrometeor classification, and quantitative precipitation estimation (QPE) has not been quantified across diverse urban environments. Meanwhile, X-band radars are being deployed extensively as gap-fillers, yet a systematic comparison of their clutter susceptibility relative to S-band systems—and whether dense X-band networking can actively compensate for S-band observational deficits—remains absent. Addressing this knowledge gap is critical for optimizing multi-band collaborative observation strategies and improving severe weather nowcasting in densely populated metropolitan areas. In this study, observations from 25 S-band and 50 X-band radars during the 2024 flood season in Beijing, Hangzhou, and Guangzhou are analyzed. A long-term statistical averaging method is applied to accumulated data from large-scale precipitation events to isolate systematic clutter signatures from random precipitation variability. The study systematically compares the ground clutter impact characteristics between S-band and X-band radars deployed across three Chinese megacities, and quantitatively evaluates the mitigation efficacy of dense X-band radar networking in clutter-affected regions of S-band radars. It is found that for S-band radars, dual-polarization anomalies at low elevations account for 35

Abstract Our understanding of raindrop size distributions (DSDs) aloft is largely limited, owing to a scarcity of effective measurements. In this study, a two‐step optimal estimation technique is developed for retrieving binned DSDs based on a newly developed vertically‐pointing triple‐frequency radar system. In the first step, we employ the Tridon and Battaglia (2015, https://doi.org/10.1002/2014jd023023) approach for retrieving the DSD shape using Ka‐ and W‐band radar Doppler spectra. In the second step, we propose the synergetic use of C‐band radar (negligible attenuation) and W‐band radar (significant attenuation) for the retrieval of DSD number concentrations, making it a fully radar‐based technique. We applied this approach to a Mei‐yu frontal rainfall event in Eastern China. Validation against ground‐based OTT Parsivel2 disdrometer observations quantitatively shows good agreement. Beyond traditional disdrometer observations, our radar retrieval captures the first view of binned DSD variations in Meiyu front rainfall.

A multiradar mosaic is a key solution to the insufficient detection range of a single radar. In the traditional gridpreprocessed mosaicking method (GPM), radar polar coordinate data are interpolated into Cartesian grids to compensate for vertically undersampled regions in radar volume scans. However, such interpolation fails to accurately reconstruct the polarization parameters in these regions. Therefore, this study presentss a polar coordinate direct-mosaicking method (PDM) for the high-density radar network in South China, which directly operates on polar coordinate data and avoids initial interpolation. Based on typical precipitation cases from May to August 2021, three key issues in the PDM are addressed: First, horizontal reflectivity (ZH) biases and differential reflectivity (ZDR) offsets are corrected; second, the number of radars in the mosaicking process is evaluated, with five radars determined to be optimal; and third, the weights of different radar data are optimized by considering vertical and horizontal distances, along with the melting layer position. Compared with the GPM, the PDM yields a more accurate representation of the melting layer, with a smaller mean height error (192 m compared with 470 m) and a more realistic estimation of thickness (661 m compared with 1507 m). It also improves the continuity of polarimetric parameters within convective core regions. The case studies indicate that the PDM enables earlier identification of ZDR columns and more accurate estimation of their heights. These results demonstrate that the PDM improves the accuracy of polarization parameter mosaics and offers potential for future applications in cloud microphysics research.

This study provides the first evaluation of the detection capabilities of the FY-3G dual-frequency Precipitation Measurement Radar (PMR) using storm top observations. Additionally, the impact of the GPM-CO orbit boost in November 2023 on Dual-frequency Precipitation Radar (DPR) observations is assessed from the perspective of the PMR. The minimum detectable radar reflectivities for the FY-3G Ku- and Ka-band radars were determined to be 12.03 dBZ and 8.60 dBZ, respectively. Notably, the better sensitivity of the FY-3G Ka-band radar enabled the detection of more and higher storm tops and significantly reduced the misidentification of the melting layer as the storm top compared to the GPM Ka-band Precipitation Radar (KaPR). At the same time, the PMR is more significantly affected by ground clutter, partly as a trade-off for its enhanced sensitivity, compared to the DPR. This increased susceptibility necessitates more rigorous ground validation of its near-surface precipitation retrieval. The sensitivity degradation of the KaPR resulting from the GPM-CO orbit boost further intensifies the underestimation of snow dual-frequency ratio (DFR) measurements, resulting in an increased classification of precipitation as convective by the DFRm method. Our results are expected to illuminate future algorithm development and data comparisons of FY-3G and GPM-CO radars.

AbstractThe Precipitation Measurement Radar (PMR) onboard FengYun‐3G consists of a Ku‐/Ka‐band radar, which is characterized by similar configurations with the Dual‐frequency Precipitation Radar (DPR) carried by Global Precipitation Measurement mission Core Observatory. However, directly comparing observations from two radars is challenging due to a scarcity of their coincidences. In this study, sea surface echoes in their track intersections were employed to cross‐calibrate PMR. Then, we show that the dual‐frequency ratio (DFR) in snow stably increases with Ku‐band reflectivity in statistics, allowing for an assessment of the consistency between PMR and DPR observations. Surprisingly, our results reveal a underestimation of DFR in DPR inner swath, while observations from PMR are in good agreement with those from DPR outer swath. This study demonstrates the novel use of natural targets for spaceborne dual‐frequency radar calibration, and presents a unique view into the connection between the two spaceborne precipitation radar missions in operation.

Meteorological radars, as remote sensing instruments, play a vital role in observing clouds and precipitation. However, due to the complexity of hydrometeors in shape, density, diameter, orientation, and particle size distributions, accurate quantification of the inner microphysical characteristics of a cloud/precipitation system is challenging for a single-frequency radar. Recently, the advancement in scattering theory of hydrometeors, computer science, and hardware manufacturing (such as millimeter-wave devices) has stimulated the application of multi-frequency radars, bringing novel observations for an improved understanding of cloud and precipitation microphysics. Over the past few years, the multi-frequency vertical detection techniques have evolved from the new retrieval methods being enlightened by scattering theory to a new stage of the crucial microphysical processes being revealed by field observations. In this paper, from the perspectives of liquid and frozen hydrometeor microphysics, we introduce the key techniques used for dual- and triple-frequency radar retrieval techniques based on the scattering and attenuation of hydrometeors. Meanwhile, enlightened by the scattering of hydrometeors, we propose that the multi-frequency radar detecting techniques are developing from the classical W/Ka/X wavelengths to a "triple-frequency plus" stage, involving radars with shorter wavelengths and/or longer wavelengths. With spaceborne radars being developed from single-frequency to dual-frequency radars, the improvement of ground-based multi-frequency radars is expected to provide crucial support to future spaceborne multi-frequency radar missions.

The distributions of cloud phases play an important role in influencing the weather and climate system. The characteristics of clouds above the Tibetan Plateau (TP) can profoundly affect regional and global atmospheric circulation. To research the distributions of cloud phases in the TP region, a retrieval algorithm was developed based on the combination of polarization lidar and millimeter cloud radar measurements and applied to the data from a comprehensive field campaign on the central TP in the summer of 2014. The structure and phase of four different types of clouds were retrieved accordingly, which validates the reliability of the algorithm. The result shows that the occurrence frequency of low clouds remains around 50%, which is very high throughout the whole day in Nagqu, Tibetan in summer. The liquid and mixed cloud frequencies are higher in the morning and afternoon, while ice cloud mainly occurs from the afternoon to midnight. Liquid and ice phase distributions show an inverse relationship in the atmospheric layer from 2 to 8 km in height. Meanwhile, the proportion of the liquid phase to the cloud top is significantly higher than that to the cloud body, which indicates that the supercooled water is more likely to appear at the cloud top than in the cloud. The fractional probabilities of the ice phase and liquid phase in the total cloud top phase intersect at about $-26.7\,\,^{\circ }\text{C}$ .

X-band dual-polarization phased-array weather radars (X-PARs) have been used in South China extensively. Eliminating the attenuation and system bias of X-band radar data is the key to utilizing the advantage of X-PAR networks. In this paper, the disdrometer raindrop-size distribution (DSD) measurements are used to calculate the radar polarimetric variables and analyze the characteristics of precipitation attenuation. Furthermore, based on the network of S-band dual-polarization Doppler weather radar (S-POL) and X-PARs, an attenuation-correction method for X-PAR reflectivity is proposed with S-POL constraints in view of the radar-mosaic requirements of a multi-radar network. Linear programming is used to calculate the attenuation-correction parameters of different rainfall areas, which realizes the attenuation correction for X-PAR. The results show that the attenuation-correction parameters simulated based on the disdrometer DSD vary with different precipitation classification; the attenuation-corrected reflectivity of X-PARs is consistent with S-POL and can realize a more precise observation of the evolution of the convective system. Compared with previous attenuation-correction methods with constant correction parameters, the improved method can reduce the deviation between X-PAR reflectivity and that of S-POL in heavy rainfall areas and areas of strong attenuation. The method proposed in this paper is stable and effective. After effective quality control, it is found that the X-PAR network deployed in South China observes data accurately and is consistent with S-POL; thus, it is expected to achieve high temporal–spatial resolution within a radar mosaic.

During the second comprehensive scientific expedition to the Qinghai Tibet Plateau, an X-band phased array polarimetric radar（X-PAR） was installed in Motuo. For the first time, the most advanced dual-polarization phased array radar is used to continuously observe the precipitation in the valley area. The monthly, diurnal, and altitude variations of echo intensity and echo top height of precipitation in Motuo were quantitatively analyzed using the observation data of Motuo X-PAR from November 2019 to October 2020 to reveal the characteristics of precipitation in the southeast valley of the plateau. The results are then compared to those obtained using Doppler radar during the summer monsoon in Naqu.The results show that:（1） The echo peak height, echo area, proportion of strong echo, and echo distribution range from April to October are greater than those from November to March in Motuo, indicating that the precipitation frequency is high and convective precipitation is more from April to October, particularly in June. However, the increase in the number of weak echoes in April shows that the echo intensity from April to October is less than from November to March.According to the monthly variation characteristics of cloud precipitation in Motuo and the plateau monsoon index, the year is divided into the dry season（November to March） and the rainy season（April to October）.（2） The echo frequency,top height, and area of precipitation in the rainy season are higher than those in the dry season. The diurnal variations of echo frequency, top height, and area show that the strongest convection occurs in the afternoon in both seasons.Precipitation occurs primarily in the afternoon and first half of the night during the dry season and in the second half of the night during the rainy season.（3） In Motuo, the echo intensity of precipitation is mostly less than 30 dBZ. The echo frequency is higher both in dry and rainy seasons for altitude ＞3 km and ＜3 km, respectively.（4） During the summer monsoon, the echo peak height of Motuo is lower than that of Naqu, and the diurnal variation trend of its peak height and area differs from that of Naqu. Besides, daily precipitation in Naqu is primarily concentrated in the afternoon and the first half of the night. In contrast, precipitation in Motuo is focused mainly in the second half of the night. The characteristics of cloud precipitation in the dry season of Motuo are similar to those in the summer monsoon period of Naqu.

The differential propagation phase (ΦDP) of X-band dual-polarization weather radar (including X-band dual-polarization phased-array weather radar, X-PAR) is important for estimating precipitation and classifying hydrometeors. However, the measured differential propagation phase contains the backscatter differential phase (δ), which poses difficulties for the application of the differential propagation phase from X-band radars. This paper presents the following: (1) the simulation and characteristics analysis of the backscatter differential phase based on disdrometer DSD (raindrop size distribution) measurement data; (2) an improved method of the specific differential propagation phase (KDP) estimation based on linear programming and backscatter differential phase elimination; (3) the effect of backscatter differential phase elimination on the specific differential propagation phase estimation of X-PAR. The results show the following: (1) For X-band weather radar, the raindrop equivalent diameters D > 2 mm may cause a backscatter differential phase between 0 and 20°; in particular, the backscatter differential phase varies sharply with raindrop size between 3.2 and 4.5 mm. (2) Using linear programming or smoothing filters to process the differential propagation phase could suppress the backscatter differential phase, but it is hard to completely eliminate the effect of the backscatter differential phase. (3) Backscatter differential phase correction may improve the calculation accuracy of the specific differential propagation phase, and the optimization was verified by the improved self-consistency of polarimetric variables, correlation between specific differential propagation phase estimations from S- and X-band radar and the accuracy of quantitative precipitation estimation. The X-PAR deployed in Shenzhen showed good observation performance and the potential to be used in radar mosaics with S-band weather radar.

Although radar-based quantitative precipitation estimation (QPE) has been widely investigated from various perspectives, very few studies have been devoted to extreme-rainfall QPE. In this study, the performance of specific differential phase (K-DP)-based QPE during the record-breaking Zhengzhou rainfall event that occurred on 20 July 2021 is assessed. Firstly, the OTT Parsivel disdrometer (OTT) observations are used as input for T-matrix simulation, and different assumptions are made to construct R(K-DP) estimators. K-DP estimates from three algorithms are then compared in order to obtain the best K-DP estimates, and gauge observations are used to evaluate the R(K-DP) estimates. Our results generally agree with previous known-truth tests and provide more practical insights from the perspective of QPE applications. For rainfall rates below 100 mm h(-1), the R(K-DP) agrees rather well with the gauge observations, and the selection of the K-DP estimation method or controlling factor has a minimal impact on the QPE performance provided that the controlling factor used is not too extreme. For higher rain rates, a significant underestimation is found for the R(K-DP), and a smaller window length results in a higher K-DP and, thus, less underestimation of rain rates. We show that the QPE based on the "best K-DP estimate " cannot reproduce the gauge measurement of 201.9 mm h(-1) with commonly used assumptions for R(K-DP), and the potential factors responsible for this result are discussed. We further show that the gauge with the 201.9 mm h(-1) report was in the vicinity of local rainfall hot spots during the 16:00-17:00 LST period, while the 3 h rainfall accumulation center was located southwest of Zhengzhou city.

To further enhance the application of dual-polarization radar in hail nowcasting, we develop an integrated convective characteristic extraction (ICCE) algorithm based on the storm cell identification and tracking (SCIT) algorithm using dual-polarization radar data and its secondary products (hydrometeor classification data and mesocyclone data). The ICCE identifies and tracks not storm cells but convective systems, and it adds other storm characteristics, such as storm microphysics (hail- and graupel-related) and storm dynamics (mesocyclone-related), to the original storm characteristics, such as storm structure (reflectivity-related) and storm tracking (motion-related). The data of four mesocyclonic hailstorms observed by the two S-band dual-polarization radars in Guangdong Province, China, are utilized, from which we draw the following conclusions: (1) ICCE excels in identifying, characterizing, matching, and tracking convective systems; and (2) the newly added storm microphysics and dynamics characteristics can more accurately quantify the relationship between mesocyclone development, hail growth, and convective system enhancement throughout the evolution of the convective system.

For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used for QPE and an algorithm that uses CRQI to improve the QPE performance. Nine heavy rainfall events that occurred in Guangdong Province, China, were used to evaluate the QPE performance in five contrast tests. The QPE performance was evaluated in terms of the overall statistics, spatial distribution, near real-time statistics, and microphysics. CRQI was used to identify good-quality data pairs (i.e., PR-based QPE and RG observation) for correcting estimators (i.e., relationships between the rainfall rate and the PR parameters) in real-time. The PR-based QPE performance was improved because estimators were corrected according to variations in the drop size distribution, especially for data corresponding to 1.1 mm < average Dm < 1.4 mm, and 4 < average log10Nw < 4.5. Some underestimations caused by the beam broadening effect, excessive beam height, and partial beam blockages, which could not be mitigated by traditional algorithms, were significantly mitigated by the proposed algorithm using CRQI. The proposed algorithm reduced the root mean square error by 17.5% for all heavy rainfall events, which included three precipitation types: convective precipitation (very heavy rainfall), squall line (huge raindrops), and stratocumulus precipitation (small but dense raindrops). Although the best QPE performance was observed for stratocumulus precipitation, the biggest improvement in performance with the proposed algorithm was observed for the squall line.

Cloud radars are widely used in observing clouds and precipitation. However, the raw data products of cloud radars are usually affected by multiple factors, which may lead to misinterpretation of cloud and precipitation processes. In this study, we present a Doppler-spectra-based data processing framework to improve the data quality of a multi-mode pulse-compressed Ka–Ku radar system. Firstly, non-meteorological signal close to the ground was identified with enhanced Doppler spectral ratios between different observing modes. Then, for the Doppler spectrum affected by the range sidelobe due to the implementation of the pulse compression technique, the characteristics of the probability density distribution of the spectral power were used to identify the sidelobe artifacts. Finally, the Doppler spectra observations from different modes were merged via the shift-then-average approach. The new radar moment products were generated based on the merged Doppler spectrum data. The presented spectral processing framework was applied to radar observations of a stratiform precipitation event, and the quantitative evaluation shows good performance of clutter or sidelobe suppression and spectral merging.