An investigation is undertaken to explore a sudden quasi-linear precipitation and gale event that transpired in the afternoon of 30 May 2024 over Beijing. It was situated at the southwestern periphery of a double-center low-vortex system, where a moisture-rich belt efficiently channeled abundant warm, humid air northward from the south. The interplay between dynamical lifting, convergent airflow-induced uplift, and the amplifying effects of the northern mountainous terrain’s topography creates favorable conditions that support the development and persistence of quasi-linear convective precipitation, accompanied by gale-force winds at the surface. The study also analyzes the impacts of five microphysics schemes (Lin, WSM6, Goddard, Morrison, and WDM6) employed in a weather research and forecasting (WRF) numerical model, with which the simulated rainfall and radar reflectivity are compared against ground-based rain gauge network and weather radar observations, respectively. Simulations with the five microphysics schemes demonstrate commendable skills in replicating the macroscopic quasi-linear pattern of the event. Among the schemes assessed, the WSM6 scheme exhibits its superior agreement with radar observations. The Morrison scheme demonstrates superior performance in predicting cumulative rainfall. Nevertheless, five microphysics schemes exhibit limitations in predicting the rainfall amount, the rainfall duration, and the rainfall area, with a discernible lag of approximately 30 min in predicting precipitation onset, indicating a tendency to forecast peak rainfall events slightly posterior to their true occurrence. Furthermore, substantial disparities emerge in the simulation of the vertical distribution of hydrometeors, underscoring the intricacies of microphysical processes.

Lightning rod configuration is crucial in radar stations. With widespread application of dual-polarisation technology, lightning rods have a significant impact on radar differential reflectivity, particularly for X-band radars with shorter wavelengths. Quantitative analyses and methods for reducing the impact of lightning rods on radar data quality have become particularly important. In this study, lightning rods of two different sizes were configured on Beijing’s Fangshan X-band radar to perform antenna far-field tests and precipitation process comparative observation tests, and to conduct a quantitative impact assessment of the antenna electrical performance parameters and radar differential reflectivity. First, far-field tests were conducted on the impact of small- and original-diameter lightning rods on the Fangshan X-band radar. The results showed that the horizontal polarisation beam width was reduced by 0.081 and 0.08°, while the vertical polarisation beam width was reduced by 0.02 and 0.11°, respectively. Second, light rain or snowfall with a signal-to-noise ratio greater than 15 dB, and a correlation coefficient greater than 0.985, were selected for comparative observation. When other environmental influences could not be isolated, the original lightning rod showed a maximum ZDR value of 1.32 dB and a maximum azimuth span of 35°. The maximum ZDR value of the small-diameter lightning rod was 0.18 dB and the maximum azimuth span was 20°; however, its deviation from the theoretical maximum value is only 0.05 dB. Therefore, once the system configuration is determined, the design of an appropriate lightning rod scheme can effectively improve radar data quality.

Understanding the evolution of the atmospheric boundary layer height (BLH) is essential for characterizing air–surface exchange and air pollution processes. This study investigates the consistency and applicability of three BLH retrieval methods based on multi-source remote sensing observations at Beijing Southern Suburb station during autumn–winter 2023. Using Doppler wind lidar (DWL) and microwave radiometer (MWR) data, the Haar wavelet covariance transform (HWCT), vertical velocity variance (Var), and parcel methods were applied, and 10 min averages were used to suppress short-term fluctuations. Statistical analysis shows good overall consistency among the methods, with the strongest correlation between HWCT and Var method (R = 0.62) and average systematic positive bias of 0.4–0.6 km for the parcel method. Case studies under clear-sky, cloudy, and hazy conditions reveal distinct responses: HWCT effectively captures aerosol gradients but fails under cloud contamination, the Var method reflects turbulent dynamics and requires adaptive thresholds, and the Parcel method robustly describes thermodynamic evolution. The results demonstrate that the three methods are complementary in capturing the material, dynamic, and thermodynamic characteristics of the boundary layer, providing a comprehensive framework for evaluating BLH variability and improving multi-sensor retrievals under diverse meteorological conditions.

Parameter estimation is important in weather radar signal processing. Time-domain processing (TDP) and frequency-domain processing (FDP) are two basic parameter estimation methods used in the weather radar field. TDP is widely used in operational weather radars because of its high efficiency and robustness; however, it must be assumed that the received signal has a symmetrical or Gaussian power spectrum, which limits its performance. FDP does not require assumptions about its power spectrum model and has a seamless connection to spectrum analysis; however, its application performance has not been fully validated to ensure its robustness in an operational environment. In this study, we introduce several technical details in FDP, including window function selection, aliasing correction, and noise correction. Additionally, we evaluate the performance of FDP and compare the performance of FDP and TDP based on simulated and measured weather in-phase/quadrature (I/Q) data. The results show that FDP has potential for operational applications; however, further improvements are required, e.g., windowing processing for signals mixed with severe clutter.

Weather radar calibration is essential for ensuring data consistency and quantitative precipitation estimation in X-band radar networks. Existing absolute calibration methods (e.g., metal sphere, horn antenna) suffer from high cost, poor timeliness, and difficulty in automation due to meteorological conditions and airspace restrictions, while spatiotemporal matching methods based on volume scan data suffer from interpolation and matching inaccuracies. To address these issues, this study proposes a collaborative calibration method for X-band radar networks based on opposing Range–Height Indicator (RHI) scans. The method uses a rigorously calibrated reference radar as a benchmark and performs opposing RHI scans with the radar under calibration to obtain synchronized observations within the spatial overlap region. Precise spatial matching is achieved using the nearest-neighbor algorithm based on beam-broadening cross-coverage thresholds, and bias is extracted using both the midline 9-point averaging method (midline method) and spatially constrained regional Statistics method (regional method). Based on a total of 58 sets of opposing RHI scanning cases conducted under stratiform precipitation, scattered precipitation, and weak cloud conditions, the results show that under conditions where echo continuity is maintained near the midline of stratiform and scattered precipitation, both the midline method and the regional method can obtain stable matching data. The midline method achieves a median correlation coefficient (0.821–0.942) higher than that of the regional method (0.860–0.872), and its bias standard deviation remains relatively stable (midline method: 1.39–2.20 dB; regional method: 2.61–3.16 dB). Continuous RHI calibration tests confirm that within a 30-min window, the fluctuation of the data matching correlation coefficient is less than 0.05, and the fluctuation of the bias mean is controlled within ±0.3 dB. Under weak cloud conditions, although the midline method can still achieve a high correlation coefficient, the correctness of its results still requires auxiliary validation through other calibration means. This study provides a relatively efficient and effective technical approach for the automated collaborative calibration of dense X-band radar networks.