Weather radars are commonly used to track the development of convective storms due to their high resolution and accuracy. However, the coverage of existing weather radar is very limited, especially in mountainous and ocean areas. Geostationary meteorological satellites can provide near global coverage and near real-time observations, which can compensate for the lack of radar observations. In this paper, a deep learning method was used to estimate the radar composite reflectivity from observations of China’s new-generation geostationary meteorological satellite FY-4A and topographic data. The derived radar reflectivity products from satellite observations can be used over regions without radar coverage. In general, the deep learning model can reproduce the overall position, shape, and intensity of the radar echoes. In addition, evaluation of the reconstruction radar observations indicates that a modified model based on the attention mechanism (Attention U-Net model) has better performance than the traditional U-Net model in terms of all statistics such as the probability of detection (POD), critical success index (CSI), and root-mean-square error (RMSE), and the modified model has stronger capability on reconstructing details and strong echoes.

This study investigates a severe summer convective hailstorm that occurred in Shanghai on 18 August 2019, using multisource meteorological datasets, with a particular focus on the innovative application of a single-polarization X-band array weather radar (AWR). Radiosonde data revealed high convective available potential energy and unstable atmospheric indices, while wind profiler radars (WPRs) showed initial easterly moisture transport near the ground and strong southwesterly flow aloft, both contributing significantly to intense convection. Ground-based automatic meteorological stations (AMSs) recorded abrupt temperature drops of approximately 10 °C and wind speed increases exceeding 20 m s⁻¹, which aligned closely with the rapid expansion of the hailstorm. In addition, an integrated analysis of data from AWR, WPRs, and AMSs enabled detailed tracking of the storm’s evolution, providing deeper insights into the interplay between moisture transport and dynamic lifting. The AWR’s unique ability to capture divergence and vorticity fields at different altitudes revealed low-level convergence coupled with high-level divergence and cyclonic rotation, sustaining convective updrafts. This study underscores the value of high-resolution AWR data in capturing short-lived, intense precipitation processes, thereby enhancing our understanding of wind field structures and storm development. These findings highlight the comprehensive application of AWR data and the potential of this new high-spatiotemporal-resolution radar for investigating the mechanisms of short-lived severe convective processes.

On 13 August 2019, a severe convective precipitation event affected the Shanghai region. At 850 hPa, a low-level shear line influenced Shanghai with surface convergence, while at 700 hPa, an inversion layer separated warm, moist lower air from colder, drier air aloft, favoring convection. Observations also revealed vertical wind shear, facilitating additional convective growth. Observations from local automatic weather stations (AWSs) and wind profiler radars (WPRs) indicate that five minutes before rainfall began, ground heat and northerly winds collided, triggering the precipitation. Both the S-band Qingpu SA radar and a novel single-polarization X-band Array weather radar system (Array Weather Radar, AWR) with three phased-array radar frontends and one radar backend captured this event. Compared with the relatively coarse spatiotemporal resolution of the Qingpu SA radar, the AWR provides high-resolution wind-field data, enabling the derivation of horizontal divergence and vertical vorticity. A detailed analysis of reflectivity, divergence, and vorticity in the AWR’s overlapping detection areas shows that, during the development and mature stages of the cell’s lifecycle, the volume of echoes with Z > 25 dBZ consistently increases, whereas echoes with Z > 45 dBZ grow in an oscillatory pattern, reaching five peaks. Moreover, at the altitudes where Z > 45 dBZ appears, regions of cyclonic vorticity emerge.

Array Weather Radar (AWR) is a novel type weather radar equipped with a distributed phased array technology. As a new instrument with new technology, the AWR offers very high spatiotemporal resolution that enables detection of the fine-scale flow field and reflectivity of severe convective storms. This new AWR provides coordinated observations of a target from three subarrays of transmitter-receiver antenna units. This paper introduces a resolution enhancement concept that the very high range resolution of one subarray can be used to compensate lower azimuth and elevation resolutions of the other subarrays of the AWR. The resolution enhancement effect is estimated using data point density. A data fusion method is then presented to obtain a unified high-resolution reflectivity from the networked and coordinated AWR subarray observations. First, based on the reflectivity data from the AWR subarray volume scans, numbers of the data-point filling in both the azimuth and elevation directions are calculated. Then, the fusion of three subarray reflectivity data is achieved through the vertical and horizontal filling and merging in a common coordinate system. The final product of the fused high-resolution reflectivity is verified using both subjective and objective evaluations. The verification experiments included radar echoes of two simulated weather scenarios, a small-scale heavy precipitation and a tornado, along with a real precipitation event. The real precipitation event was observed from the AWR system that is installed and operational at the Changsha Huanghua International Airport. The performance of the proposed high-resolution reflectivity fusion method yields a 35% smaller root mean square error and an 11% increase in the correlation coefficient to the maximum extent. The real event result shows that the final fused high-resolution reflectivity depicted a more detailed and complete echo structure compared to the China New Generation Weather Radar network observation.

X‐band Phased array radars are characterized by high spatial and temporal resolution, but suffer from a range of data quality problems, such as echo voids after the filtering of ground clutter, abnormal radials, radial obstructions and irregular missing radar echoes. This paper proposes a radar echo image restoration model (GCD) based on color correction and detail enhancement of adversarial generative networks with dual‐stream encoder‐decoder. To overcome the challenge of restoring strong echo regions, a multi‐scale Feature Alignment (FA)‐based strong echo color correction module is designed to achieve FA between original features and radial obstruction features. Additionally, a Local Detail Enhancement Module is designed to enhance the high‐frequency texture information in the strong echo regions. The GCD not only applies to all types of radar PPI images but also significantly reduces the time required for initial data processing compared to traditional methods (Sliding Window Filling method). Specifically, the speedup ratio for single‐image testing is 361.28. Addressing the issue of radial obstructions, GCD achieves an improvement of 10.15 in PSNR and a notable decrease of 18.921 dBZ in |dBZ|, thereby resolving the problem of false enhancement of meteorological echo edges that occurs with the Sliding Window Filling method. When compared to the base model, GCD further enhances the restoration of strong echoes, with a decrease in |dBZ| by 0.654. The method proposed fills the gap in the field of radar data quality control in image processing and promotes the development of artificial intelligence in this area.