In this study, we applied an explainable machine learning technique based on the LightGBM method, a category of gradient boosting decision tree algorithm, to conduct a quantitative radar precipitation estimation and move to understand the underlying reasons for excellent estimations. By introducing 3D grid radar reflectivity data into the LightGBM algorithm, we constructed three LightGBM models, including 2D and 3D LightGBM models. Ten groups of experiments were carried out to compare the performances of the LightGBM models with traditional Z–R relationship methods. To further assess the performances of the LightGBM models, rainfall events with 11,483 total samples during August-September of 2022 were used for statistical analysis, and two heavy rainfall events were specifically chosen for the spatial distribution evaluation. The results from both the statistical analysis and spatial distribution demonstrate that the performance of the LightGBM 3D model with nine points is the best method for quantitative precipitation estimation in this study. Through analyzing the explainability of the LightGBM models from Shapley additive explanations (SHAP) regression values, it can be inferred that the superior performance of the LightGBM 3D model is mainly attributed to its consideration of the rain gauge station attributes, diurnal variation characteristics, and the influence of spatial offset.

In recent years, due to the influence of global warming, extreme weather events occur frequently, such as the continuous heavy precipitation, regional high temperature, super typhoon, etc. Tropical cyclones make frequent landfall, heavy rains and flood disasters caused by landfall typhoons have a huge impact, and typhoon rainstorms are often closely related to mesoscale and small-scale system activities. The application 2020 NCEP (National Centers for Environmental Prediction) final operational global analysis data and WRF (Weather Research and Forecasting model, version 3.9) mesoscale numerical prediction model successfully simulates the evolution characteristics of the mesoscale convective complex (MCC) that caused an extreme rainstorm in the Yangtze River delta region behind a northwards typhoon in this article. The results show that a meso-β-scale vortex existed in the mid- to upper troposphere in the region where the MCC occurred; accompanied by the occurrence of the meso-β-scale vortex, the convective cloud clusters developed violently, and its shape is a typical vortex structure. The simulation-sensitive experiment shows that the development of the meso-β-scale cyclonic vortex is the main reason for the enhancement of MCC. The occurrence and development of the MCC is manifested as a vertical positive vorticity column and a strong vertical ascending motion region in the dynamic field. In the development and maturity stage of the MCC, the vorticity and vertical rising velocity in the MCC area are significantly greater than those in the weakened typhoon circulation, which shows significant mesoscale convective system characteristics. The diagnostic analysis of the vorticity equation shows that the positive vorticity advection caused by the meso-β-scale cyclonic vortex in the mid- to upper troposphere plays important roles in the development of the MCC. Enhanced low-level convergence enhances vertical ascending motion. The convective latent heat release also plays an important role on the development of the MCC, changes the atmospheric instability by heating, enhances the upward movement, and delivers positive vorticity to the upper level, making the convection develop higher, forming a positive feedback mechanism between low-level convergence and high-level divergence. The simulation-sensitive experiment also shows that the meso-β-scale cyclonic vortex formation in this process is related to convective latent heat release.

In order to improve conventional rainfall nowcasting, radar extrapolation and high-resolution numerical weather prediction (NWP) were blended to get a 6-h quantitative precipitation forecast (QPF) over the Yangtze River Delta region of China. Modifications and calibrations were done to both the extrapolation and NWP in order to get an integrated result from the two, which mainly included the extension for the extrapolation time and region, intensity and position calibration for the NWP, weighted blending of extrapolation and NWP based on scale and time, and a final real-time Z-R relation conversion. Forecast experiments were done, and results show that the blending technique could effectively extend forecast time compared with conventional radar extrapolation, meanwhile applying a positive calibration to the NWP. The overall CSI score of 0–6 h reflectivity forecast was better than either single forecast.

Organized mesoscale convective systems (MCSs), such as squall lines, are often poorly forecasted in numerical weather prediction models. In this study, experiments are performed to show that the vertical distribution of latent heating (LH) plays an important role in organizing a trailing-stratiform (TS) squall line over South China. We investigated the impact of modifying the altitude of LH peaking around 2–5 km on the squall line. It is found that increasing LH peaking at a lower vertical level (around 2–3 km) is crucial for the simulation of the TS squall line by influencing the evolution of the front-to-rear tilted upward flow and its associated mesoscale rear-to-front flow below. The influence of different LH profiles on the structure of the simulated squall line is explained using the Rotunno–Klemp–Weisman (RKW) theory considering the effects of different heights of the vertical wind center. Stronger LH at lower heights results in a vertical wind core centered lower in the convection region. Behind the core, at the mid-to-low level, is a region of descending negative horizontal vorticity. Such negative vorticity region favors a descending flow below it. When this mesoscale flow with low equivalent potential temperature (${\theta }_e)$ descends and catches up with the convection at near-surface, it enhances both the strength and moving speed of the convection system. Results of this study highlight the sensitivities of the MCS structure to the vertical distribution of the thermodynamical field besides traditional cold pool aspects and provide insights for the study of squall line through shear convection interaction.