Enhancing radar data assimilation at cloud-resolving scales is essential for advancing typhoon analysis and forecasting. This study focuses on Typhoon Linfa, the 10th Pacific Typhoon of 2015, and proposes T-TREC-IS (Typhoon Circulation Tracking Radar Echo by Correlations with Iterative Smoothing), an enhanced version of the T-TREC algorithm. The enhancement incorporates an iterative smoothing constraint into the T-TREC algorithm, which improves the continuity of the retrieved wind field and mitigates the effects of velocity aliasing in radar data, thereby increasing the operational feasibility of the method. Building on this improvement, we evaluate the effectiveness of assimilating the T-TREC-IS-retrieved wind field for analyzing and forecasting Typhoon Linfa. The results demonstrate that the iterative smoothing constraint effectively filters out velocity de-aliasing errors during radar data quality control, enhances wind field intensity near the typhoon core, and retrieves the typhoon circulation more accurately. The refined wind field exhibits improved consistency and continuity, resulting in superior performance in subsequent assimilation analyses and forecasts.

Using multi-source observation data including automatic stations, radar, satellite, new detection equipment, and the Fifth Generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-5) data, along with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) platform, an analysis was conducted on a rainstorm process that occurred in Nanjing on 15 June 2020, with the aim of providing reference for future urban flood control planning and heavy rainfall forecasting and early warning. The results showed that this rainstorm process was generated under the background of an eastward-moving northeast cold vortex and a southward retreat of the Western Pacific Subtropical High. Intense precipitation occurred near the region of large top brightness temperature (TBB) gradient values or the center of low TBB values on the northern side of the convective cloud cluster. During the heavy precipitation period, the differential propagation phase shift rate (K_DP), differential reflectivity factor (Z_DR), and zero-lag correlation coefficient (ρ_HV) detected by the S-band dual-polarization radar all increased significantly. The vertical structure of the wind field detected by the wind profile radar provided a good indication of changes in precipitation intensity, showing a strong correspondence between the timing of maximum precipitation and the intrusion of upper-level cold air. The abrupt increase in the integrated liquid water content observed by the microwave radiometer can serve as an important indicator of the onset of stronger precipitation. During the Meiyu season in Nanjing, convective precipitation was mainly composed of small to medium raindrops with diameters less than 3 mm, with falling velocities of raindrops mainly clustering between 2 and 6 m·s⁻¹. The rainstorm process featured four water vapor transport channels: the mid-latitude westerly channel, the Indian Ocean channel, the South China Sea channel, and the Pacific Ocean channel. During heavy rainfall, the Pacific Ocean water vapor channel was the main channel at the middle and lower levels, while the South China Sea water vapor channel was the main channel at the upper level, both accounting for a trajectory proportion of 34.2%.

Based on the WRF-3DVar system, this study investigates the impacts of assimilating radar and automatic weather station (AWS) observations, both independently and jointly, for a squall line case that occurred over complex terrain in China on 30 May 2024. It is found that radar data assimilation with spatial truncation significantly enhances the representation of convective structures while reducing false echoes by about 40%. However, when the variance and correlation length scales are enlarged, reflectivity intensity is increased by 5–10 dBZ with false signals and positional errors also introduced, while a balanced scheme is observed to yield the highest skill scores. Assimilation of AWS alone provides limited improvements, whereas radar assimilation introduces localized structures that rapidly decay within 1–2 h due to the absence of boundary-layer constraints. The benefits of joint assimilation are clearly demonstrated in terms of spatial continuity and vertical consistency, with the assimilation order being identified as a decisive factor. When AWS is assimilated prior to radar, low-level thermodynamic and dynamic conditions are optimized, leading to strengthened cold pool structures by around 2 K, enhanced updrafts by over 20%, and improved wind distribution. The critical role of AWS-radar joint assimilation in capturing the dynamical characteristics of squall lines is thus highlighted. Detailed examination of the forecast and analysis indicates that assimilating AWS before radar not only optimizes boundary-layer conditions but also enhances the coupling between cold pools and updrafts, resulting in improved simulation accuracy in both horizontal and vertical structures. These findings provide valuable insights for advancing the prediction of severe convective systems.