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Article

Analysis of a Summer Convective Precipitation Event in the Shanghai Region Using Data from a Novel Single-Polarization X-Band Phased-Array Radar and Other Meteorological Observations

by
Xiaoqiong Zhen
1,2,3,
Hongbin Chen
1,3,*,
Xuehua Fan
1,3,
Hongrong Shi
1,3,
Haojun Chen
4,
Wanyi Wei
5,
Jie Fu
2,
Shuqing Ma
6,
Ling Yang
2 and
Jianxin He
2
1
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2
College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China
3
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
4
Shanghai Meteorological Information and Technology Support Center, Shanghai 200030, China
5
Eastone Washon Science and Technology Ltd., Shaoxing 312000, China
6
Meteorological Observation Center of China Meteorological Administration, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(8), 1403; https://doi.org/10.3390/rs17081403
Submission received: 13 March 2025 / Revised: 9 April 2025 / Accepted: 11 April 2025 / Published: 15 April 2025
(This article belongs to the Section Environmental Remote Sensing)
Browse Figures
Versions Notes

Abstract

:
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.

1. Introduction

Heavy rainfall is a weather phenomenon that often accompanies severe convective storms. When heavy rainfall occurs over a small area within a short period, it can trigger flash floods in mountainous regions or urban inundation. Due to current limitations in the accuracy of heavy rainfall forecasts and the timeliness of early warnings, insufficient evacuation or sheltering time may result in significant losses of life and property, especially from short-duration, localized heavy rainfall events. According to surveys, between 2001 and 2020, floods in China affected an average of over 100 million people annually, resulting in an average yearly economic loss of CNY 167.86 billion [1]. In July 2012, the “7.21” heavy rainfall event in the Beijing–Tianjin–Hebei region caused 79 fatalities in Beijing, with economic losses estimated at approximately CNY 11.64 billion [2]. In July 2021, an extreme rainfall event in Zhengzhou led to urban flooding that resulted in 292 deaths and 47 missing persons [3].
Meteorological observation professionals and atmospheric science researchers have long been committed to obtaining high spatiotemporal resolution observational data from severe precipitation processes [4,5,6,7]. Continuous efforts have been made to enhance monitoring capabilities and deepen our understanding of the evolution mechanisms of severe precipitation processes, as well as to improve the timeliness and accuracy of early warnings. Acquiring and analyzing high spatiotemporal resolution data through advanced meteorological observation equipment is, therefore, essential and fundamental for studying the mechanisms of severe convective precipitation, forecasting its development, and issuing reliable severe weather warnings.
Among ground-based remote sensing equipment, weather radars play a widely recognized role in severe storm observation. Many countries and regions have established extensive weather radar networks, such as the Weather Surveillance Radar 88 Doppler (WSR-88D) network in the United States [8], the China Next-Generation Weather Radar (CINRAD) network in China [9], and the Operational Program for Exchange of Weather Radar Information (OPERA) network in Europe [10,11]. These networks significantly enhance capabilities for monitoring, analyzing, and forecasting severe convective weather events [11,12,13,14]. Short-lived, localized severe convective storms pose particularly significant threats to life and property. Conventional S-band radars, such as the CINRAD in China, have a relatively low temporal resolution (~6 min) and coarse spatial resolution (~1 km radial resolution). For storms with lifetimes as short as approximately 30 min, the CINRAD can only provide limited observation (approximately five volume scans). Therefore, obtaining weather radar data with high spatiotemporal resolution has become an urgent requirement for detecting, researching, forecasting, and issuing timely warnings for severe convective storms [15,16].
With the gradual application of phased-array antennas in the field of weather radar, phased-array weather radar (PAWR) development and field validation experiments have started both domestically and abroad. The United States was the first to initiate research on PAWR and has conducted field observation experiments with PAWRs, demonstrating their advantages in detecting severe precipitation, hail, tornadoes, and other weather phenomena [17,18]. Similarly, Osaka University in Japan, in collaboration with Toshiba, has developed PAWRs and carried out field observation experiments [19,20,21,22,23]. Most of these experiments have utilized one or two PAWRs to observe precipitation events. In China, PAWR development began as early as 2007, followed by field observation experiments [24,25,26]. Guangdong Province was the first to implement networked observations using multiple PAWRs, resulting in data collection for several weather events [27].
To offer a comprehensive overview and facilitate comparison of the development status of high-spatiotemporal-resolution weather radars worldwide, Table 1 presents a summary of the spatial resolution and volume scan update intervals of existing high-spatiotemporal-resolution radars globally.
For the existing ground-based weather radar network, coordinated scanning strategies are typically categorized into two primary methods. One method involves deploying multiple independent PAWRs for networked collaborative observation [23,32,33,34,35]. The other method, which is adopted by the Array Weather Radar (AWR) system, involves controlling multiple phased-array radar frontends with a single radar backend. This approach offers significant advantages in terms of data consistency and scanning coordination [36,37].
The AWR system is an innovative distributed phased-array weather radar system jointly developed by the China Meteorological Administration Meteorological Observation Center, the Institute of Atmospheric Physics of the Chinese Academy of Sciences, Chengdu University of Information Technology, and collaborating radar manufacturers. By integrating concepts of rapid scanning and collaborative networking, the AWR uses multiple phased-array radar frontends arranged in a triangular configuration, creating overlapping detection areas called enhanced detection areas (EDAs) and a fine detection area (FDA). By utilizing a specialized Synchronized Azimuthal Scanning (SAS) strategy, three radar frontends achieve a detection data time difference (DTD) of only 2 s within overlapping detection areas. Therefore, the primary advantage and original design intention of AWR is the capacity to obtain multiple non-coplanar radial velocity data within overlapping detection areas through the deployment of several phased-array radar frontends. This arrangement enables the retrieval of wind field data at various altitudes within storm bodies located in these overlapping areas. Furthermore, if these radar frontends possess dual-polarization capabilities, AWR can additionally provide polarimetric radar products.
Compared to traditional S-band radars, the AWR maintains high spatial resolution while effectively expanding detection coverage through an increased number of radar frontends, thus addressing the inherent limitations in the coverage of X-band radars. Additionally, the AWR can capture detailed storm dynamics with high spatial and temporal resolution, revealing internal precipitation particle movement. This capability is crucial for analyzing internal storm flow structures and atmospheric kinematic characteristics, thereby assisting meteorologists in examining storm formation, development, and dissipation processes from a dynamic perspective. Ultimately, this advanced technical solution aims to enhance severe storm prediction accuracy, extend early warning lead time, and improve preparedness for complex precipitation events.
The first AWR system was developed in China in 2018 and has been successfully deployed for observation experiments in various regions, including Changsha [38,39,40], Shanghai, Beijing, Foshan, Guizhou, and others [24,25,26,27,37,41,42]. The prototype AWR radar, initially installed in Changsha, featured a standard configuration of one radar backend and three phased-array frontends. Previous observational studies have demonstrated the ability of this system to achieve rapid scanning cycles (12 s per volume scan) and high spatial resolution (0.1 km × 0.1 km × 0.1 km grid resolution). Subsequent field experiments in multiple locations, such as Shanghai and Foshan, have further confirmed the high potential of AWR systems for obtaining detailed wind field products, high-resolution reflectivity fields, and capturing fine-scale storm evolution features.
On 13 August 2019, a severe convective precipitation event occurred in the Shanghai region. This event was captured by a single-polarization X-band AWR (hereafter referred to as the Shanghai AWR), which provided observational data with high spatiotemporal resolution. The Shanghai AWR, comprising three phased-array radar frontends, was arranged in a triangular layout in Baoshan (BS AWR, 31.4°N, 121.4°E), Pudong (PD AWR, 31.1°N, 121.7°E), and Chongming (CM AWR, 31.5°N, 121.8°E) districts, as illustrated in Figure 1a–c. Additionally, relevant observational data were also collected by other meteorological observational equipment, including an S-band CINRAD radar (Qingpu SA radar), two L-band boundary layer wind profiler radars (WPRs), and multiple ground-based automatic weather stations (AWSs). This study combines these multi-source observational datasets and uses the high-resolution AWR radar data to analyze the characteristics of the convective precipitation event at different stages of its lifecycle.
The structure of this study is as follows: Section 2 introduces the observational datasets and the associated observational equipment used in this study; specifically, the distribution and observational characteristics of various meteorological observational equipment used in this event are described. Section 3 begins with an in-depth analysis of the synoptic situation and atmospheric conditions, using data from the Meteorological Information Comprehensive Analysis and Processing System (MICAPS) and radiosonde observations at the Banshan station. Next, the lifecycle of the convective precipitation event of interest is clearly defined; subsequently, preliminary analyses of the precipitation event are performed using the obtained multi-source observational data. Furthermore, high-spatiotemporal-resolution AWR radar data are utilized to comprehensively analyze the precipitation event. Finally, Section 4 summarizes the results, provides a detailed discussion, and outlines the directions for future research.

2. Observational Equipment and Datasets

2.1. Weather Radars and Wind Profiler Radars (WPRs)

As noted, Shanghai AWR is a phased-array radar system consisting of three phased-array radar frontends arranged in a triangular configuration. The AWR is a novel PAWR system comprising at least three phased-array radar frontends and a single radar backend. Based on the triangular arrangement of the phased-array frontends, the AWR can be categorized as a distributed phased-array weather radar system. The three radar frontends are positioned in a triangular configuration at different locations, enabling the acquisition of non-coplanar radial velocity measurements from precipitation events. The radar backend strictly coordinates these radar frontends according to the SAS strategy. Within the FDA, when three non-coplanar radial velocity observations are available in Cartesian grids, the horizontal wind components (u, v) can be resolved [43]. In the EDAs, a three-dimensional variational (3DVAR) wind retrieval algorithm is employed, using radial velocity observations as primary constraints. Through iterative minimization of the cost function via a conjugate gradient method, an optimal wind field solution can be determined [44,45].
However, there are limitations when synthesizing or retrieving wind fields from the non-coplanar radial velocity data provided by different radar frontends of the AWR. Specifically, if the intersection angle between scanning beams from two radar frontends is less than 30° (or greater than 150°), the accuracy of wind field synthesis or retrieval will be compromised. This is primarily because when the scanning beams of two radar frontends approach alignment along the axis connecting them, their measured radial velocities become nearly equal in magnitude but opposite in direction. As a result, the radial velocities obtained from these two radar frontends contain little or no orthogonal components, thus providing insufficient information to accurately resolve the wind fields. Similarly, the accuracy of the wind retrieval algorithm is negatively affected under these conditions.
The distance between BS AWR (Figure 1a) and PD AWR (Figure 1b) is approximately 41 km; between PD AWR (Figure 1b) and CM AWR (Figure 1c) approximately 40 km; and between CM AWR (Figure 1c) and BS AWR (Figure 1a) approximately 42 km. Each radar frontend covers a detection radius of approximately 44 km, centered at its installation site. In Figure 2, the overlapping detection areas of all three radar frontends (positions marked by red stars) comprise the Fine Detection Area (FDA), while areas with overlapping detection coverage from two radar frontends are Enhanced Detection Areas (EDAs). The Shanghai AWR system updates its volume scan data every 30 s, providing radar products that include, but are not limited to, the reflectivity factor of the precipitation event and wind-field data within the FDA and EDAs. Table 2 summarizes the primary technical specifications of the Shanghai AWR system.
The layout and relative locations of various meteorological observational instruments in the Shanghai region are shown in Figure 2. An S-band CINRAD radar used by the Shanghai Meteorological Service (hereafter referred to as the Qingpu SA radar) is represented by a solid blue star in Figure 2. The spatiotemporal resolution of data from the Shanghai AWR and Qingpu SA radar is compared in Table 3.
In addition to weather radars, this study also utilized data from two boundary-layer L-band wind profiler radars (WPRs) and a secondary wind radar for atmospheric soundings. The WPRs are located at the Baoshan Meteorological Observatory and the Shanghai Expo Park, marked by yellow and green stars, respectively, in Figure 2. Furthermore, data from the GFE (L) secondary wind radar, operated at the Baoshan Meteorological Observatory for upper-air sounding, were used for a comprehensive analysis of the precipitation event. When paired with digital radiosondes and weather balloons, the secondary wind radar can measure meteorological parameters such as wind speed, wind direction, temperature, pressure, and humidity. Table 4 presents the main specifications of these two wind radar systems, which provide complementary wind measurements below the 5 km level [25].

2.2. Ground-Based Automatic Weather Station

The Shanghai Meteorological Service has established multiple ground-based AWSs to conduct routine ground meteorological observations, including temperature, hourly precipitation, wind direction, and wind speed. These data are used for weather diagnostics and forecasting, as well as meteorological data exchanges between cities and even nations. Within the FDA of the Shanghai AWR and closest to the precipitation event analyzed in this study are three ground-based AWSs: 5469, 5474, and 5475. These are marked as A1, A2, and A3, respectively, and indicated by blue dots in Figure 2.

3. Observation and Analysis of the Severe Convective Precipitation Process

3.1. Synoptic Situation and Atmospheric Background Analysis

Between 03:00 and 09:00 UTC on 13 August 2019, persistent precipitation occurred over the Shanghai urban area. Synoptic situation and atmospheric background analyses were conducted using 00:00 UTC radiosonde data from the China Meteorological Administration’s MICAPS. Figure 3a–c show the synoptic charts at 500 hPa, 700 hPa, and 850 hPa, respectively.
At 500 hPa, an upper-level trough was located over central China, and Shanghai (marked by a green circle) was situated in the southwest warm, moist flow ahead of the trough. This setting provided a moisture source conducive to precipitation. However, temperature profiles indicated a warm center at 500 hPa over Shanghai, seemingly not meeting the typical “cold over warm” structure favorable for precipitation. At 700 hPa, Typhoon Lekima (highlighted within the blue ellipse) had moved away from Shanghai, but the region remained under the influence of the typhoon’s peripheral effects. Wind field data at 700 hPa showed predominately west and southwest winds with moderate humidity. At 850 hPa, a shear line (depicted by the red double solid line) existed between southern Jiangsu and southern Anhui provinces, with the potential to shift eastward and impact Shanghai. Within the green circle, the dew point depression at 850 hPa over Shanghai was 2 °C (<5 °C), signifying high humidity. This abundant low-level moisture fulfilled the necessary conditions for precipitation.
Radiosonde data are essential reference information for atmospheric environmental monitoring and numerical weather prediction. These data provide insights into temperature, humidity, pressure, and wind conditions at various altitudes, reflecting the physical state and dynamics of the atmosphere. They help characterize atmospheric stability, moisture content, ground-based temperature inversions, and wind direction and speed at different levels. Located in northwestern Shanghai, the Baoshan Radiosonde Station (the yellow star in Figure 2) is a standard national sounding station. Using the GFE(L) secondary wind radar described in Section 2.1, along with radiosondes and weather balloons, it conducts regular upper-air soundings twice daily at 00:00 and 12:00 UTC.
The Temperature–log Pressure (T-logP) diagram at 00:00 UTC on 13 August 2019 at the Baoshan Radiosonde Station (Figure 4) shows that the air is relatively dry between 500 and 600 hPa, while the lower levels exhibit higher humidity, indicating abundant low-level moisture. The Lifting Condensation Level (LCL) is low (below 950 hPa), meeting the condition for dry aloft, moist below, which is favorable for convection. The wind data within the red box at 400–500 hPa (right side of Figure 4) indicate upper-level winds rotating counterclockwise with height, suggesting cold advection. Meanwhile, in the lower layer below 925 hPa, winds rotate clockwise with height, indicating warm advection. Significant wind shear exists between these layers.
Overall, the radiosonde data indicate a “cold over warm” thermal structure, along with an inversion layer near 700 hPa. This inversion separates the moist, warm air below from the dry, cold air above, promoting the development of convection. At this time, the Convective Available Potential Energy (CAPE) value is relatively high at 1359.8 J/kg, suggesting that given a trigger mechanism, convective weather could occur.
The ground observation distribution maps, provided at various times by the MICAPS system (Figure 5), show the following: At 03:00 UTC, the ground wind field over Shanghai exhibited cyclonic convergence, favoring the occurrence of precipitation. At this time, the ground temperature reached a maximum of 34 °C. At 05:00 UTC, the convergence line shifted to the southcentral part of Shanghai, with heavy rainfall occurring within the purple circle, resulting in 23–24 mm of 6-h rainfall accumulation. At 06:00 UTC, the ground wind field convergence line moved westward, and the precipitation further intensified, with the 6-h rainfall amount reaching up to 49 mm. Although the ground wind field often appeared chaotic due to complex underlying ground conditions or obstruction from buildings, the convergence line indicated small-scale convergence in the area. At 08:00 UTC, precipitation weakened, the ground wind field shifted predominantly northward and westward, and no distinct convergence line was present. The energy accumulated prior to precipitation dissipated, and the ground temperature noticeably dropped compared with pre-rain conditions.
To better understand the cloud distribution and its changes on a larger scale during this convective precipitation event, Figure 6 presents visible cloud imagery from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4 geostationary satellite. The images were taken at two times, 03:38:34 and 04:38:34 UTC—an hour apart—using channel 2 (0.65 μm). Thicker clouds have higher albedo, appear brighter, and are displayed in lighter shades. A comparison of Figure 6a,b reveals that over the Shanghai region (inside the red circle), the clouds became thicker and higher after one hour, moving in an easterly direction.

3.2. Lifecycle Division of the Severe Convective Cell

Figure 7 shows that on 13 August 2019, the Qingpu SA radar’s 2.37° PPI (Plan Position Indicator) showed a small precipitation echo (highlighted by a red ellipse) approximately 60 km northeast of the radar location at 02:30:16 UTC. With reflectivity greater than 25 dBZ, the echo had a size of about 2 km × 3 km and a maximum reflectivity of 40.5 dBZ. After 03:20:00 UTC, most of this precipitation cell entered the FDA of the Shanghai AWR, quickly underwent development, reached maturity, and eventually dissipated. This study focuses on a detailed analysis of the evolution of this precipitation cell within the FDA (03:20:00–05:00:00 UTC).
The lifecycle of a convective precipitation cell typically comprises three stages: initiation and development, maturity, and dissipation. Given the limited spatiotemporal resolution of the Qingpu SA radar data, it is challenging to precisely delineate these lifecycle stages. To highlight the high spatiotemporal resolution of the Shanghai AWR, we analyzed the time evolution of reflectivity factors that are observed to determine the different stages of the convective cell’s lifecycle.
Reflectivity data points exceeding thresholds of 25 dBZ, 35 dBZ, 55 dBZ, and 60 dBZ were tallied (Figure 8). The time interval corresponding to the peak at 60 dBZ of reflectivity was used to define the maturity stage of the convective cell. Consequently, the lifecycle of the convective cell under study was divided into three stages (highlighted by the green boxes in Figure 8): the initiation and development stage (Stage A, before 04:19:00 UTC), the maturity stage (Stage B, 04:20:00–04:40:00 UTC), and the dissipation stage (Stage C, after 04:41:00 UTC).
To demonstrate the advantage of high-spatiotemporal-resolution data in convective storm detection, we compared observations from the Shanghai AWR and Qingpu SA radar. Figure 9a–c present data from both weather radars for the three lifecycle stages of the precipitation event.
Based on the previously defined lifecycle stages, Figure 9a–c primarily display data from the Shanghai AWR, supplemented by data from the Qingpu SA radar, showing how both radars captured this convective precipitation process. The central black-background panels represent reflectivity from the Qingpu SA radar, while the surrounding white-background panels overlay Shanghai AWR reflectivity and wind fields at corresponding heights, illustrating the high spatiotemporal resolution of the AWR data.
While the Qingpu SA radar updates volume scan data every 6 min, the Shanghai AWR updates every 30 s. Due to image size limitations, and to show a longer detection data span, Figure 9a–c display the Shanghai AWR results in 1 min intervals. Each subfigure covers a continuous 12 min observation period, with the Shanghai AWR providing 24 volume scans in this time, whereas the Qingpu SA radar captures only two. This highlights the time-resolution advantage of the Shanghai AWR.
Additionally, the radial resolution of the Qingpu SA radar is 1 km, which is why the black-background images display a mosaic-like grid structure. As shown in Table 3, the azimuthal resolution of the Qingpu SA radar is approximately 1° and remains constant regardless of distance. However, as the distance to the target increases, beam spreading becomes more pronounced. This can lead to reduced spatial resolution and degraded accuracy in identifying and positioning distant meteorological features. In comparison, the Shanghai AWR’s grid resolution is 0.1 km × 0.1 km × 0.1 km. Figure 9a–c show that the reflectivity fields from the Shanghai AWR are finer and smoother than those from the Qingpu SA radar, providing more detailed information.
Figure 9 demonstrates that high-spatiotemporal-resolution data provide much more detailed information on the convective precipitation cell of interest. However, due to space constraints, additional images are not presented. Below, a statistical analysis is performed on the convective precipitation cell data observed by the two weather radars.
Figure 10a,b show the temporal variation in the total reflectivity factor data points exceeding 30 dBZ and 45 dBZ, as observed by the two weather radars, while Figure 10c provides a comparative statistical chart of the echo top (ET) products. In Figure 10a,b, red triangles represent the statistical results from the volume scanning of the Qingpu SA radar, and black dots represent those from the Shanghai AWR. Figure 10a indicates that the number of high-reflectivity data points from the Shanghai AWR, sampled every 1 min, continuously increases over time—with only occasional minor decreases—exhibiting an overall upward trend. Figure 10b shows that the number of high-reflectivity data points from the Shanghai AWR displays five distinct peaks, with the overall trend gradually increasing. Figure 10c compares the ET products from the Qingpu SA radar and the Shanghai AWR. Although the overall trends in ET are generally similar between the two radars, the Shanghai AWR data exhibit more pronounced and rapid increases and decreases, occurring earlier than those observed by the Qingpu SA radar.

3.3. Observational Results and Analysis of WPR Data Surrounding the Severe Convective Precipitation Cell

To analyze the wind field configuration and moisture transport conditions surrounding the severe convective precipitation cell of interest, this section examines data from two WPRs recorded during the period from 03:00:00 to 05:00:00 UTC.
Located in northwestern Shanghai, the Baoshan Meteorological Observatory is approximately 7.5 km away from the nearest BS AWR and, thus, more than 7.5 km from the analyzed precipitation cell during the study period. Figure 11a presents wind barb plots from WPR1 throughout the analysis period. Within this timeframe, the WPR1 data indicate predominant easterly and northeasterly winds at altitudes below 1 km, transitioning to southwesterly winds at higher altitudes, with wind speeds occasionally reaching or exceeding 20 ms−1.
WPR2, closer to the precipitation echo studied here, shows distinct features (Figure 11b). Between 04:15:00 and 05:05:00 UTC, both low- and high-altitude layers exhibit clear and persistent southwesterly winds, indicating strong moisture and dynamic transport. To further investigate moisture and ground heating conditions at ground level, we analyzed the AWS data from stations A2 and A3 (Figure 12a,b). Five minutes before precipitation reached stations A2 and A3 (at 04:30:00 and 04:40:00 UTC, respectively), ground temperatures were around 30 °C, indicating the presence of warm ground air. Combined with the northerly winds bringing cooler air from surrounding areas, this warm air likely triggered the precipitation event.

3.4. Analysis of Ground-Based AWS Data Within the Shanghai AWR FDA

As shown in Figure 2, three ground-based AWSs located within the FDA of the Shanghai AWR—A1, A2, and A3—measured hourly ground precipitation, wind speed, wind direction, and temperature during this precipitation event. Figure 12d illustrates the distances between these stations and the convective precipitation cell center over time. Initially (03:20:00 UTC), station A1 was closest to the precipitation cell center. Over the next 10–15 min, the precipitation cell rapidly approached station A1, reaching a minimum distance of about 3 km at around 03:30:00–03:35:00 UTC before moving away. Figure 12a shows that the ground temperature at station A1 remained mostly above 30 °C throughout the event, briefly dropping to about 30 °C between 03:45:00 and 04:00:00 UTC due to slight rainfall, after which the temperature increased again as the precipitation moved away. The overall wind speed at A1 remained low (2–4 ms−1), and the wind direction was mostly easterly. Thus, station A1 only recorded brief precipitation (lasting about 15 min), as the storm remained distant for most of the observation period.
Stations A2 and A3 exhibited similar temperature variations, with a continuous downward trend in ground temperature. At station A2 (Figure 12b), as the precipitation cell approached (around 04:35:00 UTC), the temperature decrease became sharper, dropping rapidly to approximately 30 °C due to the arrival of precipitation. Five minutes before precipitation onset (at 04:30:00 UTC), wind direction at A2 changed abruptly from east to north, indicating the arrival of cooler air. This convergence between cooler air brought by northerly winds and warmer air near the ground (around 30 °C) significantly enhanced the convective development. Wind speed at A2 increased sharply to a maximum of approximately 7 ms−1 at the onset of precipitation. Subsequently, about 25 min later (approximately 04:55:00 UTC), the wind direction and speed returned to their levels before precipitation occurrence (about 2 ms−1, easterly).
Similarly, at station A3, temperature continuously decreased over time. Before precipitation arrived (04:45:00 UTC), the ground temperature had already dropped to approximately 30 °C. About 5 min prior to precipitation, wind direction at A3 shifted to northeasterly, and wind speed increased sharply to a maximum of around 6 ms−1. Considering that station A3 is located farther east than A2 and given the eastward movement of the precipitation event, this timing aligns well with the storm’s progression (also seen in Figure 12d). During the precipitation event at A3 (04:45:00–05:00:00 UTC), brief increases in wind speed were also observed, possibly due to precipitation-driven downdrafts reaching the ground.

3.5. Analysis of Shanghai AWR Observational Data at Different Stages of the Precipitation Cell

Based on the high-spatiotemporal-resolution wind field data provided by the Shanghai AWR within its overlapping detection areas and following the convective precipitation cell lifecycle division presented in Section 3.2, this section analyzes the reflectivity field at various altitudes, the horizontal divergence field derived from the wind field, and the vertical vorticity component from the AWR data for the precipitation event of interest.
Figure 13 illustrates the Shanghai AWR data, showing the reflectivity field overlaid with wind fields (left column), the horizontal divergence field (middle column), and the vertical vorticity component (right column) at different altitudes at 04:11:00 UTC during Stage A of the precipitation cell. During the initiation and development stage, positive divergence can be observed at 3.2 and 5.6 km (highlighted by the red ellipse), and positive vorticity appears at 2.4 and 5.6 km (highlighted by the blue ellipse). This indicates that the upper levels of the convective precipitation cell exhibit horizontal divergence, while the middle and upper levels display cyclonic vorticity associated with rotational updrafts. These two physical fields demonstrate that the storm’s dynamic configuration is favorable for the development of a convective precipitation cell. Furthermore, we can see that at the altitudes where the intense precipitation core (with reflectivity > 45 dBZ) appears in the left column, the right column also shows substantial cyclonic vorticity areas.
Similar to Figure 13, Figure 14 shows data at various altitudes captured by the Shanghai AWR at 04:25:00 UTC during the mature stage (Stage B) of the precipitation echo. Figure 14 presents (1) the reflectivity factor overlaid with wind field data (left column), (2) horizontal divergence fields (middle column), and (3) vertical vorticity components (right column).
The left column of Figure 14 reveals that during the mature stage of the precipitation cell, the precipitation echo area significantly expands, indicating an increase in the precipitation area. At an altitude of 3.2 km, the area of strong precipitation cores with reflectivity factors > 45 dBZ also increases, signifying stronger rainfall intensity. The middle column of Figure 14 shows that at a high altitude of 5.6 km, the divergence area (highlighted by the red ellipse) has expanded compared with the same altitude during the initial development stage. In addition, at both the lower altitude of 1.6 km and the higher altitude of 5.6 km, there is a noticeable increase in convergence regions (highlighted by red ellipses), indicating that upward airflow dynamics within the storm persist.
The right column of Figure 14 shows that—aside from the increase in the cyclonic vorticity area at 5.6 km compared with the initial development stage—there is also an increase in the cyclonic vorticity area at the mid-level altitude of 3.2 km. However, the strong positive vorticity center (light coral color) has nearly disappeared, suggesting that while the horizontal rotation area of mid-level airflow within the convective storm has grown, the disappearance of the strong positive vorticity center implies that the rotational updraft dynamics have weakened compared with the initial development stage.
Finally, comparing the left and right columns of Figure 14, a pattern similar to that of Figure 13 emerges: at all altitudes, the strong precipitation cores with reflectivity factors > 45 dBZ in the left column generally correspond to the large cyclonic vorticity area in the right column.
Figure 15 presents data at different altitudes at 04:42:00 UTC during Stage C of the precipitation storm’s lifecycle, as captured by the Shanghai AWR. The data include (1) the reflectivity factor overlaid with wind field data (left column), (2) horizontal divergence (middle column), and (3) vertical vorticity components (right column).
At this stage, a new precipitation cell forms to the southwest of the precipitation cell under study and is expected to merge with it to create a new cell. Our focus is the larger precipitation cell area on the eastern (right) side of Figure 15. While the overall area of the precipitation cell depicted in the left column has not abruptly diminished, the area of strong precipitation cores with reflectivity > 45 dBZ has notably decreased at all altitude levels. The higher the altitude, the smaller the precipitation area, indicating that the height of the precipitation cell has decreased and that it is in the dissipation stage.
The middle column’s divergence fields at various altitudes show that at the low-to-mid-level altitude of 1.6 km, a convergence area remains (light blue negative divergence values enclosed in a red ellipse), indicating that the precipitation cell has not completely dissipated.
The right column’s vorticity fields show that large areas of cyclonic vorticity are no longer present at the upper level (5.6 km). However, at the lower and middle levels (1.6 km and 3.2 km), cyclonic vorticity areas remain relatively large, and positive vorticity centers (light coral color) have reappeared. This indicates that relatively strong horizontal rotation persists in the low-to-mid-level flows, possibly influenced by the new precipitation cell forming to the southwest.
As observed in Figure 13, Figure 14 and Figure 15, the strong precipitation cores with reflectivity >45 dBZ at 1.6 km and 3.2 km in the left column correspond to large cyclonic vorticity areas at the same altitudes in the right column.

4. Conclusions and Discussion

This study utilized various meteorological observation data to conduct a detailed analysis of a strong summer convective precipitation event that occurred in the Shanghai region on 13 August 2019. A key tool in the analysis was AWR, a novel X-band single-polarization phased-array weather radar system. With a volume scan cycle of 30 s and a high spatial resolution of 0.1 km × 0.1 km × 0.1 km, the AWR provided reflectivity factor data for the precipitation cell of interest, as well as wind field data at different altitudes within the overlapping detection areas. The analysis focused on the evolution of the strong convective precipitation cell from 03:20:00 to 05:00:00 UTC after it entered the overlapping detection areas of the Shanghai AWR.
Data from MICAPS and the Shanghai Baoshan Meteorological Observatory revealed a low-level shear line at 850 hPa, ground convergence wind fields, and ample low-level moisture, all conducive to convection. In addition, the inversion structure at 700 hPa facilitated convective development. Observations from two WPRs and three ground-based AWSs near the convective area showed that, in the 5 min preceding ground precipitation, the convergence of cold air brought by north winds and high temperatures near the ground triggered precipitation. Furthermore, the ground wind field (such as easterly winds east of Station A2) aligned with the movement direction of the precipitation cell.
When comparing weather radar data, we can see that both the Qingpu SA radar and the Shanghai AWR detected the precipitation cell. However, due to the SA radar’s approximately 6-min volume scan cycle, its observations were limited. By contrast, the AWR’s high temporal and spatial resolution captured more details of the precipitation process. Using the unique wind field information within the overlapping detection areas of the Shanghai AWR, we calculated horizontal divergence fields and vertical vorticity component fields at various altitudes. This allowed for a detailed analysis of divergence and vorticity component data across different precipitation stages (development, maturation, and dissipation). The results showed that areas of high reflectivity factors (>45 dBZ) at various altitudes generally corresponded to cyclonic vorticity areas, highlighting the intrinsic link between the precipitation cell and dynamic processes.
We found that during the initial and developing stages of the precipitation cell, upper-level divergence and prominent mid-to-upper-level cyclonic vorticity signaled further convection intensification. During the mature stage, the expansion of upper-level divergence and mid-level cyclonic vorticity areas, along with the disappearance of large positive vorticity centers, suggested a lack of upward energy and hinted at energy dissipation. During the dissipating stage, although the new precipitation cell to the southwest influenced the overall precipitation area, the strong precipitation cores shrank significantly at all altitudes. Nevertheless, lower-to-mid-level convergence and cyclonic vorticity partially persisted, reflecting the attenuation process.
This study presents the AWR system as an innovative approach to phased-array radar networking. Unlike traditional configurations that use multiple independent PAWRs, the AWR utilizes a single radar backend to control at least three phased-array radar frontends. By coordinating their scanning through the SAS strategy, this architecture significantly enhances data consistency across the radar frontends. As the Shanghai AWR is a single-polarization X-band radar, it cannot provide dual-polarization products (e.g., ZDR), and attenuation is unavoidable in heavy precipitation. Radar attenuation correction is handled by a specialized radar data quality control research team, and as such, this study did not address those aspects. Moreover, the limitations of single-polarization phased-array radar in estimating precipitation intensity or identifying precipitation particle types must also be considered.
The primary objective of the AWR design is to provide wind field data within overlapping detection areas of precipitation storms. If the radar frontends are equipped with dual-polarization capability, the AWR can give additional polarimetric products related to precipitation processes, offering distinct advantages for detecting and analyzing hailstorms. For short-lived, localized, intense convective precipitation events—such as those lasting approximately 30 min—the AWR’s high spatiotemporal resolution allows it to deliver more detailed observational data. This capability not only provides critical support for understanding the mechanisms of heavy precipitation processes but also creates opportunities for significant improvements in forecasting and early warning of such events.
Looking forward, we hope to combine the high-spatiotemporal-resolution data from the Shanghai AWR with observations from other equipment to further explore the quantitative relationships between divergence and vorticity fields at different altitudes and the mechanisms of precipitation formation. In addition, the application of phased-array radar in forecast accuracy, hydrological applications, or quantitative precipitation estimation (QPE) could also be considered as one of the future research priorities. This would provide deeper insights into energy conversion and dissipation in convective precipitation processes, offering a more robust scientific foundation for understanding and forecasting strong convective precipitation.

Author Contributions

Conceptualization, X.Z. and H.C. (Hongbin Chen); methodology, H.C. (Hongbin Chen); software, X.Z.; validation, X.Z., H.C. (Hongbin Chen), H.S. and X.F.; formal analysis, X.Z.; investigation, X.Z.; resources, H.C. (Haojun Chen) and W.W.; data curation, H.C. (Haojun Chen), W.W. and J.F.; writing—original draft preparation, X.Z.; writing—review and editing, H.C. (Hongbin Chen), H.S., X.F., S.M., L.Y. and J.H.; visualization, X.Z.; supervision, H.C. (Hongbin Chen); project administration, H.C. (Hongbin Chen); funding acquisition, H.C. (Hongbin Chen). All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Sichuan Science and Technology Program, grant No. 2024NSFSC1996; the National Natural Science Foundation of China, grant No. NSFC42275091; and the Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province, grant No. SCSF202308.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to legal or ethical reasons.

Acknowledgments

The authors would like to thank the Shanghai Meteorological Information and Technology Support Center and Eastone Washon Science and Technology Ltd. for providing ground observation equipment and radar detection data. This greatly contributed to the success of this research.

Conflicts of Interest

Author Wanyi Wei was employed by the company Eastone Washon Science and Technology Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PAWRphased array weather radar
NWRTNational Weather Radar Testbed
SASSynchronized Azimuthal Scanning
DTDsData Time Differences
MWR-05XPMeteorological Weather Radar 2005 X-band Phased Array
WSR-88DWeather Surveillance Radar-1988 Doppler
CINRADChina Next-Generation Weather Radar
OPERAOperational Program for Exchange of Weather Radar Information
AWRarray weather radar
EDAenhanced detection area
FDAfine detection area
SASsynchronized azimuthal scanning
3DVARthree-dimensional variational
BS AWRBaoshan Array Weather Radar
PD AWRPudong Array Weather Radar
CM AWRChongming Array Weather Radar
T-logPTemperature–Pressure Logarithmic
CAPEConvective Available Potential Energy
CINConvective Inhibition
LCLLifting Condensation Level
MICAPSMeteorological Information Comprehensive Analysis and Processing System
AGRIAdvanced Geostationary Radiation Imager
WPRwind profiler radar
FDAFine Detection Area
EDAEnhanced Detection Area
PPIPlan Position Indicator
ETecho top

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Figure 1. The three phased-array radar frontends of the Shanghai single-polarization X-band AWR are installed on towers located in Baoshan District (a), Pudong District (b), and Chongming District (c), Shanghai.
Figure 1. The three phased-array radar frontends of the Shanghai single-polarization X-band AWR are installed on towers located in Baoshan District (a), Pudong District (b), and Chongming District (c), Shanghai.
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Figure 2. Layout of various meteorological observational equipment in Shanghai. The three red stars mark the locations of the three Shanghai AWR radar frontends. The red circles (with a radius of approximately 44 km) indicate the horizontal projection of each AWR radar front end’s detection range. The position of the Qingpu SA radar is also shown. The two L-band boundary-layer wind profiler radars (WPRs) are located at WPR1 (yellow star) and WPR2 (green star). A1, A2, and A3 represent the three ground-based automatic weather stations (AWSs) within the AWR’s FDA: 5469, 5474, and 5475, respectively.
Figure 2. Layout of various meteorological observational equipment in Shanghai. The three red stars mark the locations of the three Shanghai AWR radar frontends. The red circles (with a radius of approximately 44 km) indicate the horizontal projection of each AWR radar front end’s detection range. The position of the Qingpu SA radar is also shown. The two L-band boundary-layer wind profiler radars (WPRs) are located at WPR1 (yellow star) and WPR2 (green star). A1, A2, and A3 represent the three ground-based automatic weather stations (AWSs) within the AWR’s FDA: 5469, 5474, and 5475, respectively.
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Figure 3. Upper-level sounding data at 500 hPa (a), 700 hPa (b), and 850 hPa (c) from 00:00 UTC on 13 August 2019. The red solid lines within the green circles represent the Shanghai urban boundary. In (a), the blue solid line indicates the upper-level trough and the blue ellipse marks the location of Typhoon Lekima’s core. In (c), the red double solid line represents the shear line, while the black number 2 inside the green circle shows the dew point depression.
Figure 3. Upper-level sounding data at 500 hPa (a), 700 hPa (b), and 850 hPa (c) from 00:00 UTC on 13 August 2019. The red solid lines within the green circles represent the Shanghai urban boundary. In (a), the blue solid line indicates the upper-level trough and the blue ellipse marks the location of Typhoon Lekima’s core. In (c), the red double solid line represents the shear line, while the black number 2 inside the green circle shows the dew point depression.
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Figure 4. T-logP diagram from Baoshan Radiosonde Station at 00:00 UTC on August 13, displaying temperature (°C) on the horizontal axis and pressure (hPa) on the vertical axis. Wind direction and wind speed at various altitudes are shown on the right, with the highlighted data area marked by red rectangles.
Figure 4. T-logP diagram from Baoshan Radiosonde Station at 00:00 UTC on August 13, displaying temperature (°C) on the horizontal axis and pressure (hPa) on the vertical axis. Wind direction and wind speed at various altitudes are shown on the right, with the highlighted data area marked by red rectangles.
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Figure 5. Ground observation data from 13 August 2019. Subfigures (a–d) show the data at 03:00, 05:00, 06:00, and 08:00 UTC, respectively. The red solid line represents Shanghai’s administrative boundary, while the green numbers indicate the total 6 h precipitation amounts (in mm) and purple circles highlight the regions with relatively heavy precipitation. The dashed black lines in subfigures (b,c) indicate low-level ground wind field convergence lines.
Figure 5. Ground observation data from 13 August 2019. Subfigures (a–d) show the data at 03:00, 05:00, 06:00, and 08:00 UTC, respectively. The red solid line represents Shanghai’s administrative boundary, while the green numbers indicate the total 6 h precipitation amounts (in mm) and purple circles highlight the regions with relatively heavy precipitation. The dashed black lines in subfigures (b,c) indicate low-level ground wind field convergence lines.
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Figure 6. Visible cloud imagery from channel 2 (0.65 μm) of the Advanced Geosynchronous Radiation Imager onboard the FY-4 geostationary satellite, taken at 03:38:34 (a) and 04:38:34 UTC (b) on 13 August 2019. The regions highlighted by red circles represent the cloud cluster above the Shanghai region of interest in this study.
Figure 6. Visible cloud imagery from channel 2 (0.65 μm) of the Advanced Geosynchronous Radiation Imager onboard the FY-4 geostationary satellite, taken at 03:38:34 (a) and 04:38:34 UTC (b) on 13 August 2019. The regions highlighted by red circles represent the cloud cluster above the Shanghai region of interest in this study.
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Figure 7. Reflectivity of the initial precipitation echo detected by the Qingpu SA radar at a 2.37° elevation angle at 02:30:16 UTC on 13 August 2019. The red ellipse highlights the echo analyzed in detail in this study.
Figure 7. Reflectivity of the initial precipitation echo detected by the Qingpu SA radar at a 2.37° elevation angle at 02:30:16 UTC on 13 August 2019. The red ellipse highlights the echo analyzed in detail in this study.
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Figure 8. Time series of the number of reflectivity factor data points exceeding different thresholds (25 dBZ, 35 dBZ, 55 dBZ, and 60 dBZ) for the convective precipitation echo observed by the Shanghai AWR. The results are used to divide the lifecycle stages of the precipitation echo, three green boxes labeled A, B, and C represent the initial development stage, the maturity stage, and the dissipation stage of the precipitation cell of interest in this study, respectively.
Figure 8. Time series of the number of reflectivity factor data points exceeding different thresholds (25 dBZ, 35 dBZ, 55 dBZ, and 60 dBZ) for the convective precipitation echo observed by the Shanghai AWR. The results are used to divide the lifecycle stages of the precipitation echo, three green boxes labeled A, B, and C represent the initial development stage, the maturity stage, and the dissipation stage of the precipitation cell of interest in this study, respectively.
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Figure 9. Observations from Shanghai AWR and Qingpu SA, capturing the convective precipitation cell of interest at three lifecycle stages on 13 August 2019: (a) initial and development stage, (b) mature stage, and (c) dissipation stage. The PPI reflectivity data from Qingpu SA radar are displayed with a black background at the center, while reflectivity data at corresponding heights from the Shanghai AWR, overlaid with wind fields, are shown with white backgrounds around the periphery. Starting from the Qingpu SA observations at the central as the start time, the Shanghai AWR data are presented sequentially in a clockwise direction from the top-right corner at 1 min intervals. When available, additional elevation scan data from Qingpu SA are provided. The three subfigures in Figure 9 clearly demonstrate the Shanghai AWR’s ability to offer higher spatiotemporal resolution observations. Compared with Qingpu SA, reflectivity data from Shanghai AWR show more detail, complemented by high-spatiotemporal-resolution wind fields.
Figure 9. Observations from Shanghai AWR and Qingpu SA, capturing the convective precipitation cell of interest at three lifecycle stages on 13 August 2019: (a) initial and development stage, (b) mature stage, and (c) dissipation stage. The PPI reflectivity data from Qingpu SA radar are displayed with a black background at the center, while reflectivity data at corresponding heights from the Shanghai AWR, overlaid with wind fields, are shown with white backgrounds around the periphery. Starting from the Qingpu SA observations at the central as the start time, the Shanghai AWR data are presented sequentially in a clockwise direction from the top-right corner at 1 min intervals. When available, additional elevation scan data from Qingpu SA are provided. The three subfigures in Figure 9 clearly demonstrate the Shanghai AWR’s ability to offer higher spatiotemporal resolution observations. Compared with Qingpu SA, reflectivity data from Shanghai AWR show more detail, complemented by high-spatiotemporal-resolution wind fields.
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Figure 10. Comparative analysis of data observed by Qingpu SA and Shanghai AWR for the precipitation event of interest on 13 August 2019. (a) Number of reflectivity data points exceeding 30 dBZ, with black dots representing Shanghai AWR and red triangles representing Qingpu SA. The left vertical axis corresponds to the number of data points from AWR, while the right vertical axis corresponds to data points from SA radar. (b) Similar to (a), but for reflectivity data points exceeding 45 dBZ, with identical vertical axes as (a). (c) Comparison of echo top products from the two radars, with black dots for Shanghai AWR and red dots for Qingpu SA; vertical axes represent height (km). The horizontal axis for all three subfigures represents time.
Figure 10. Comparative analysis of data observed by Qingpu SA and Shanghai AWR for the precipitation event of interest on 13 August 2019. (a) Number of reflectivity data points exceeding 30 dBZ, with black dots representing Shanghai AWR and red triangles representing Qingpu SA. The left vertical axis corresponds to the number of data points from AWR, while the right vertical axis corresponds to data points from SA radar. (b) Similar to (a), but for reflectivity data points exceeding 45 dBZ, with identical vertical axes as (a). (c) Comparison of echo top products from the two radars, with black dots for Shanghai AWR and red dots for Qingpu SA; vertical axes represent height (km). The horizontal axis for all three subfigures represents time.
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Figure 11. Horizontal wind field data at different altitudes from two L-band WPRs, WPR1 at the Baoshan Meteorological Observatory (a) and WPR2 at the Shanghai Expo Park (b), collected from 03:00:00 to 05:30:00 UTC on 13 August 2019. The regions highlighted by red rectangles denote areas of particular interest.
Figure 11. Horizontal wind field data at different altitudes from two L-band WPRs, WPR1 at the Baoshan Meteorological Observatory (a) and WPR2 at the Shanghai Expo Park (b), collected from 03:00:00 to 05:30:00 UTC on 13 August 2019. The regions highlighted by red rectangles denote areas of particular interest.
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Figure 12. Precipitation, wind direction, wind speed, and temperature data collected from three AWSs within the FDA of Shanghai AWR—stations A1 (a), A2 (b), and A3 (c)—between 03:00:00 and 05:00:00 UTC on 13 August 2019. The relative positions of AWS A1, A2, A3, and other observational equipment are shown in Figure 2. In subfigures (a–c), temperature, wind direction, wind speed, and precipitation are represented by red, purple, and blue curves and black histograms, respectively, with vertical axes from left to right corresponding to each parameter. (d) illustrates the distances between the analyzed precipitation echo center and the three AWSs, with the vertical axis indicating distance (km).
Figure 12. Precipitation, wind direction, wind speed, and temperature data collected from three AWSs within the FDA of Shanghai AWR—stations A1 (a), A2 (b), and A3 (c)—between 03:00:00 and 05:00:00 UTC on 13 August 2019. The relative positions of AWS A1, A2, A3, and other observational equipment are shown in Figure 2. In subfigures (a–c), temperature, wind direction, wind speed, and precipitation are represented by red, purple, and blue curves and black histograms, respectively, with vertical axes from left to right corresponding to each parameter. (d) illustrates the distances between the analyzed precipitation echo center and the three AWSs, with the vertical axis indicating distance (km).
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Figure 13. The convective precipitation cell observed by the Shanghai AWR on 13 August 2019, at 04:11:00 UTC, during the initial development stage, is shown at various altitudes: (1) reflectivity factor overlaid with wind field data (left column); (2) horizontal divergence (middle column); and (3) vertical vorticity component (right column).
Figure 13. The convective precipitation cell observed by the Shanghai AWR on 13 August 2019, at 04:11:00 UTC, during the initial development stage, is shown at various altitudes: (1) reflectivity factor overlaid with wind field data (left column); (2) horizontal divergence (middle column); and (3) vertical vorticity component (right column).
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Figure 14. The convective precipitation cell observed by the Shanghai AWR on 13 August 2019, at 04:25:00 UTC, during the mature stage, is shown at various altitudes: (1) reflectivity factor overlaid with wind field data (left column); (2) horizontal divergence (middle column); and (3) vertical vorticity component (right column).
Figure 14. The convective precipitation cell observed by the Shanghai AWR on 13 August 2019, at 04:25:00 UTC, during the mature stage, is shown at various altitudes: (1) reflectivity factor overlaid with wind field data (left column); (2) horizontal divergence (middle column); and (3) vertical vorticity component (right column).
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Figure 15. The convective precipitation cell observed by the Shanghai AWR on 13 August 2019, at 04:42:00 UTC, during the mature stage, is shown at various altitudes: (1) reflectivity factor overlaid with wind field data (left column); (2) horizontal divergence (middle column); and (3) vertical vorticity component (right column).
Figure 15. The convective precipitation cell observed by the Shanghai AWR on 13 August 2019, at 04:42:00 UTC, during the mature stage, is shown at various altitudes: (1) reflectivity factor overlaid with wind field data (left column); (2) horizontal divergence (middle column); and (3) vertical vorticity component (right column).
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Table 1. Summary of resolution information of high-spatiotemporal-resolution radars.
Table 1. Summary of resolution information of high-spatiotemporal-resolution radars.
NationNameBandPolarizationSpace Resolution
(Unit: m)		Volume Scan Update Intervals
(Unit: s)
United StatesNWRT PARS/~15030~60
RS-DOW [28]XSingle117/14
MWR-05XP [29]XSingle1507~62
MPAR [17]SDual150/
AIR [30]XSingle37.55.5 (90° × 20°)
PAIR [31]CDual106
ChinaS-PARSSingle100/
X-PARXSingle37.530~150
AWRXSingle10012
APARXDual3060
C-PARCSingle75116
JapanPAWRXSingle10010~30
MP-PAWR [32]
(DP-PAWR)		XDual75/15030/60
Table 2. Specifications of the Shanghai Array Weather Radar (AWR).
Table 2. Specifications of the Shanghai Array Weather Radar (AWR).
Array Weather Radar (AWR)Technical Specifications
TechnologyDistributed and active phased array,
One dimensional, Doppler, single-polarized
Frequency9.3–9.5 GHz
Number of frontends3
Volume-scan update time30 s
Each frontend scanning modeMechanical scan horizontally and electronic scan vertically
Maximum detection range of one radar frontend~44 km
Range resolution of one radar frontend0.05 km
Grid size of outputs0.1 km × 0.1 km × 0.1 km
Table 3. Comparison of spatiotemporal resolution between data from two weather radars.
Table 3. Comparison of spatiotemporal resolution between data from two weather radars.
Shanghai AWR
(X-Band)		Qingpu SA Radar
(S-Band)
Distance resolution/1 km
Azimuthal resolution/~1°
Product grid size0.1 km × 0.1 km × 0.1 km/
Volume-scan update intervals30 s6 min
ProductsReflectivity factor, wind fieldReflectivity factor, Vr
Table 4. Comparison of product resolutions between WPR and GFE(L) secondary wind radar.
Table 4. Comparison of product resolutions between WPR and GFE(L) secondary wind radar.
L-Band Boundary Layer WPRGFE(L) Secondary Wind Radar
DataWind Speed and Direction
Displayed Using Wind Barb Plots		Temperature, Pressure, Humidity, Wind Speed and Wind Direction
Displayed Using Temperature–Log Pressure Diagrams
Maximum Detection
Altitude		3–5 km>30 km
Time Resolution5 minConducted at 00 and 12 UTC Daily with Radiosondes and Weather Balloons
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MDPI and ACS Style

Zhen, X.; Chen, H.; Fan, X.; Shi, H.; Chen, H.; Wei, W.; Fu, J.; Ma, S.; Yang, L.; He, J. Analysis of a Summer Convective Precipitation Event in the Shanghai Region Using Data from a Novel Single-Polarization X-Band Phased-Array Radar and Other Meteorological Observations. Remote Sens. 2025, 17, 1403. https://doi.org/10.3390/rs17081403

AMA Style

Zhen X, Chen H, Fan X, Shi H, Chen H, Wei W, Fu J, Ma S, Yang L, He J. Analysis of a Summer Convective Precipitation Event in the Shanghai Region Using Data from a Novel Single-Polarization X-Band Phased-Array Radar and Other Meteorological Observations. Remote Sensing. 2025; 17(8):1403. https://doi.org/10.3390/rs17081403

Chicago/Turabian Style

Zhen, Xiaoqiong, Hongbin Chen, Xuehua Fan, Hongrong Shi, Haojun Chen, Wanyi Wei, Jie Fu, Shuqing Ma, Ling Yang, and Jianxin He. 2025. "Analysis of a Summer Convective Precipitation Event in the Shanghai Region Using Data from a Novel Single-Polarization X-Band Phased-Array Radar and Other Meteorological Observations" Remote Sensing 17, no. 8: 1403. https://doi.org/10.3390/rs17081403

APA Style

Zhen, X., Chen, H., Fan, X., Shi, H., Chen, H., Wei, W., Fu, J., Ma, S., Yang, L., & He, J. (2025). Analysis of a Summer Convective Precipitation Event in the Shanghai Region Using Data from a Novel Single-Polarization X-Band Phased-Array Radar and Other Meteorological Observations. Remote Sensing, 17(8), 1403. https://doi.org/10.3390/rs17081403

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