In this study, an objective algorithm to identify meso-γ-scale vortexes (MVs) using radial velocity observations from an S-band radar is developed. Then, for the 237 extreme hourly precipitation (EXHP; ＞75 mm) records at the surface weather stations in the Pearl River Delta (PRD) during five warm seasons, the properties and environmental conditions of the EXHP-associated MVs are analyzed. Further, the spatiotemporal distributions of the MV, instantaneous rain rate, and EXHP are illustrated for three events with the most abundant EXHP records. The major findings are as follows. About 42% EXHP records are accompanied by 57 MVs, including 84% of weak shear intensity, 12% of weak mesocyclone intensity, and 4% of moderate mesocyclone intensity, with the rotational speeds between 5 and 12 m/s, 12 and 16 m/s, and 16 and 21 m/s, respectively. The duration and core depth of the MVs are highly correlated (coefficient of 0.67) with averages of 39 min and 699 m, respectively. The hourly rainfall accumulation of an EXHP tends to increase with the influencing duration of MVs on the EXHP, while a majority of MVs might result from the EXHP-associated forcing such as condensational latent heating. Relative to the EXHP events with MVs in the United States, those in the PRD feature smaller environmental 0—3 km storm relative helicity (SRH) and 0—1 km vertical wind shear (VWS). However, compared to the non-rotational EXHP in the PRD, the rotational EXHP events are associated with significantly higher 0—1 km VWS, 0—3 km SRH, humidity, and larger convective available potential energy. In the three selected events, rainstorms exhibit an irregular shape, a quasi-circular shape, and a quasi-banded shape of strong echo, respectively. The MVs are often located inside the strong radar reflectivity region, and some are next to its bow-shaped portion. Those longer-lived MVs with stronger rotation are collocated with the extreme 6 min rainfall accumulation (≥10 mm) in space and time, suggesting presence of positive feedback between low-level rotation and short-term rain rate. In the event influenced by a tropical storm, four MVs appear at almost the same location in succession and move along roughly the same path, forming an MV back-building process.

In this study, an objective algorithm to identify meso-γ-scale vortexes (MVs) using radial velocity observations from an S-band radar is developed. Then, for the 237 Extreme Hourly Precipitation (EXHP; > 75 mm) records at the surface weather stations in the Pearl River Delta (PRD) during five warm seasons, the properties and environmental conditions of the EXHP-associated MVs are analyzed. Further, the spatiotemporal distributions of the MV, instantaneous rain rate, and EXHP are illustrated for three events with the most abundant EXHP records. The major findings are as follows. About 42% EXHP records are accompanied by 57 MVs, including 84% of weak shear intensity, 12% of weak mesocyclone intensity, and 4% of moderate mesocyclone intensity, with the rotational speeds between 5 and 12 m s⁻¹, 12 and 16 m s⁻¹, and 16 and 21 m s⁻¹, respectively. The duration and core depth of the MVs are highly correlated (coefficient of 0.67) with averages of 39 min and 699 m, respectively. The hourly rainfall accumulation of an EXHP tends to increase with the influencing duration of MVs on the EXHP, while a majority of MVs might result from the EXHP-associated forcing such as condensational latent heating. Relative to the EXHP events with MVs in the United States, those in the PRD feature smaller environmental 0–3-km storm relative helicity (SRH) and 0–1-km vertical wind shear (VWS). However, compared to the non-rotational EXHP in the PRD, the rotational EXHP events are associated with significantly higher 0–1-km VWS, 0–3-km SRH, humidity, and larger convective available potential energy. In the three selected events, rainstorms exhibit an irregular shape, a quasi-circular shape, and a quasi-banded shape of strong echo, respectively. The MVs are often located inside the strong radar reflectivity region, and some are next to its bow-shaped portion. Those longer-lived MVs with stronger rotation are collocated with the extreme 6-min rainfall accumulation (≥ 10 mm) in space and time, suggesting presence of positive feedback between low-level rotation and short-term rain rate. In the event influenced by a tropical storm, four MVs appear at almost the same location in succession and move along roughly the same path, forming an MV back-building process.

In this study, an objective algorithm to identify convection initiation (CI) based on weather radar observations is developed. In this algorithm, a convective cell is defined as a continuous area of strong radar echo (reflectivity factor reaching 35 dBz). The presence of a convective cell is identified as CI if two criteria are met: (1) no convective cells exist around the convective cell in a 30 min period prior to its presence; (2) this convective cell lasts for at least 20 min. This algorithm is applied to the urban agglomeration region over Jiangsu-and-Shanghai during the post-Meiyu period (from the end of the Meiyu season to 31 August, and excluding days influenced by tropical cyclones). Based on the results, A CI database with high spatiotemporal resolutions (10 min, 1 km) over the region for the 2019—2022 post-Meiyu periods is constructed. The sensitivity of the CI identification to two parameters in the algorithm is investigated by comparing three experiments in a case study and also based on statistics analysis. When the radius of the area (R), which is used to determine whether the convective echo is the first occurrence, is set to 10 km, whether the minimum area of the convective echo (A_min) is set to 4 km² or 2 km² (Test1, Test2) does not significantly influence the CI identification results. Both experiments can identify the complex situations with continuous and adjacent CI events, with Test1 being able to identify 83% of Test2's CI events. The statistical characteristics of CI events identified by the two experiments are essentially the same, including the number of CI days, daily CI numbers, the distribution of CI high-occurrence frequency areas, and the diurnal variation of CI occurrence frequency. When R is set to 30 km (with A_min of 4 km², Test3), only 36% of the CI events identified in Test1 can be identified in Test3. The insufficient number of identified CI events results in significant differences in spatial distribution and diurnal variation of CI occurrence compared to those from Test1 and Test2. The results indicate that there are three subregions of high occurrence frequency of CI in the analysis region, i.e., the "Shanghai-Suzhou-Wuxi-Changzhou-Nanjing" city belt along the Yangtze river, from northern Nantong to the offshore of the Yellow sea, and part of the Tianmu mountain in the southwest of the analysis region. The diurnal variation of CI occurrence frequency is characterized by a major peak around noon (11:00—14:00 BT).