Hybrid data assimilation (DA) methods have received extensive attention in the field of numerical weather prediction. In this study, a hybrid gain data assimilation (HGDA) method that combined the gain matrices of ensemble and variational methods was first applied in the mesoscale version of the Global/Regional Assimilation and Prediction System (GRAPES_Meso). To evaluate the performance of the HGDA method in the GRAPES_Meso model, different DA schemes, including the three-dimensional variational (3DVAR), local ensemble transform Kalman filter (LETKF), and HGDA schemes, were compared across eight tropical cyclone (TC) cases, and FY-4A atmospheric motion vectors were assimilated. The results indicated that the HYBRID scheme outperformed the 3DVAR and LETKF schemes in TC position forecasting, and with ensemble forecasting techniques, the HYBRID scheme promoted the accuracy of the prediction TC intensity. The threat score (TS) values for the light and medium precipitation forecasts obtained in the HYBRID experiment were higher than those for the forecasts obtained in the 3DVAR and LETKF experiments, which may be attributed to the forecasting accuracy for the TC position. Regarding heavy and extreme rainfall, the HYBRID scheme achieved a more stable effect than those of the 3DVAR and LETKF schemes. The results demonstrated the superiority of the HGDA scheme in TC prediction with the GRAPES_Meso model.

On 17 July 2019, an unusually intense rainfall occurred in the central-eastern part of Jiangsu Province in China, resulting in a record-breaking daily precipitation of 286.4 mm at the Rugao station, not seen since 1961. A comprehensive analysis was conducted on various multiscale characteristics of the initial rain event, such as the large-scale surroundings, moisture transport, triggering and maintenance mechanisms, and microphysical characteristics. Multi-sources of data were utilized, such as reanalysis data, automatic weather stations, wind-profiling radar, laser-optical Particle Size Velocity instruments, soundings, S-band dual-polarization radar, and a Lagrangian model. The findings suggest that the intense precipitation in Rugao resulted from the convergence of the warm and moist airflow from Typhoon Danas and the cold air moving southward from the north, along with the ample moisture and energy provided by the circulation of Typhoon Danas. Convection, which showed good consistency with the intense precipitation process, was initiated by mesoscale temperature gradients and wind field convergence. This was associated with the intrusion of a near-surface cold pool and the maintenance of a ground convergence line in the Rugao area. This convection exemplified a standard system of warm clouds with high precipitation efficacy. It had a high concentration of raindrops, especially large ones, resulting in record-breaking precipitation in a short amount of time.