Utilizing spaceborne precipitation radar (SR) to evaluate ground-based weather radar (GR) reflectivity consistency is essential for early detection of performance issues. However, existing SR vs. GR matching methods often overlook some error sources, such as insufficient beam coverage and non-uniform precipitation. This study introduces enhanced methods for SR vs. GR matching, including beam blocking identification, precipitation uniformity assessment, and improved time matching. Validation was performed using the FY-3G satellite’s precipitation measurement radar (PMR) and four S-band radars in Hunan Province, China. Our analysis demonstrates that integrating clear-sky ground clutter, long-duration precipitation echoes, and terrain data effectively detects actual beam blocking in GR. Selecting data with a time difference of 180 seconds or less, within 230 kilometers of the GR center, GR reflectivity between 20 dBZ and 35 dBZ, and a signal-to-noise ratio (SNR) above 15 dB significantly mitigates random errors in SR vs. GR matching results. Additionally, we explore a method for tracking reflectivity deviations within a “ground-satellite-ground” radar network, validated through adjacent GR comparisons. This approach, based on the Changsha Meteorological Radar Calibration Center’s reference radar. The method was validated using adjacent ground radar comparison, showing a correlation between the two methods with a minimum deviation transfer difference of 0.29 dB and a maximum of 1.13 dB. Additionally, the Yiyang radar exhibited slightly higher reflectivity, indicating that the method is both reliable and practically applicable.

Low‐level jets (LLJs), as dynamically significant narrow air currents in the lower troposphere, profoundly influence regional weather and anthropogenic activities. Motivated by persistent LLJs detected through Doppler LiDAR observations during March to April 2021, this study systematically characterizes these LLJs over Juehua Island (western Bohai Sea coast) by integrating high‐resolution Weather Research and Forecasting Model simulations. Based on a 61‐day simulation spanning March to April 2021, this study reveals a notable LLJ occurrence frequency of 25.3%, dominated by two distinct directional modes: southwesterly and northeasterly. The high frequency and pronounced directional dominance of LLJs in this region motivated a systematic investigation combining Empirical Orthogonal Function (EOF) analysis and sensitivity experiments. The LLJ‐favorable synoptic patterns were extracted through EOF analysis of the sea‐level pressure (SLP) field. The northwest‐southeast SLP dipole pattern plays a dominant role, contributing 68% to LLJ formation. Southwesterly and northeasterly LLJs are favored by “high‐southeast, low‐northwest” and its opposite synoptic dipoles, respectively. Sensitivity experiments quantify the contribution of local geography at 21%, collectively accounted for by the Bohai Sea (16%) and the northwestern highlands (5%). Additionally, the Bohai Sea preferentially enhances southwest LLJs, which exhibit lower LLJ heights. In contrast, the northwestern highlands elevate LLJ heights and align LLJ directions with the terrain orientation, resulting in a more clustered wind direction distribution. This study quantified LLJ‐favorable synoptic patterns, Bohai Sea and northwestern high terrain influences. These findings could provide valuable insights for regional LLJ forecasting.

The calibration accuracy of key weather radar antenna parameters, including beam pointing, beamwidth, and antenna gain, directly affects quantitative precipitation estimation (QPE) and multi-radar network products. Conventional calibration approaches such as external field beacons and far-field tests are often constrained by site conditions and high implementation costs, making them difficult to apply routinely in operational radar networks. To address this limitation, this study proposes a robust solar calibration method for key antenna parameters of weather radars based on a dedicated Volume Coverage Pattern for Sun calibration, hereafter referred to as VCPSun. The proposed method uses a high-density solar scanning strategy with midpoint time alignment and feed-forward control of solar apparent motion. Combined with solar sample identification, propagation path correction, two-dimensional Gaussian surface fitting, and deconvolution of solar-source broadening and scan-smearing effects, the method enables reliability retrieval of beam pointing, beamwidth, and antenna gain. A high-frequency intensive observing experiment was conducted using a China New Generation Weather Radar, model SA-D (CINRAD/SA-D), deployed at the Changsha Meteorological Radar Calibration Center, with independent far-field test results used for validation. The results show that the retention rate of quality-controlled solar samples reached 85.7%, supporting stable reconstruction of the main-lobe power pattern. The retrieved mean beam pointing biases for both polarizations were within ±0.05°. After correction, the relative differences in beamwidth with respect to far-field measurements were respectively 3.26% and 1.52% for the H-polarization azimuth and elevation directions and 2.09% and 1.84% for the V-polarization azimuth and elevation directions, with the overall mean relative difference being less than 3.5%. The antenna gain differences relative to the independent far-field reference values were within 0.2 dB, at −0.062 dB for H-polarization and −0.144 dB for V-polarization. Comparative analysis with historical one-dimensional SunCheck records and an ablation test of the beamwidth correction chain further demonstrate that high-density two-dimensional sampling and physical deconvolution corrections improve the robustness and quantitative accuracy of the solar-based retrieval. These results demonstrate the feasibility of reliable in situ calibration of key antenna parameters for operational weather radars. The proposed method provides a potential technical pathway for in situ quantitative assessment of antenna performance in S-band CINRAD/SA-D radars, although further validation using additional radars and longer observation periods is required prior to network-wide application.