On 22 June 2017, an extreme warm-sector rainfall event hit the western coastal area of South China, with maximum hourly and 12-h rainfall accumulations of 189.4 and 464.8 mm, respectively, which broke local historical records. Multisource observations were used to reveal multiscale processes contributing to the extreme rainfall. The results showed that a marine boundary layer jet (BLJ) coupled with a synoptic low-level jet (LLJ) inland played an important role in the formation of an extremely humid environment with a very low lifting condensation level of near-surface air. Under the favorable pre-convective conditions, convection was initialized at a mesoscale convergence line, aided by topographic lifting in the evening. During the nocturnal hours, the rainstorm developed and was maintained by a quasi-stationary mesoscale outflow boundary, which continuously lifted warm, moist air transported by the enhanced BLJ. When producing the extreme rainfall rates, the storm possessed relatively weak convection, with the 40 dBZ echo top hardly reaching 6 km. The extreme rainfall was produced mainly by the warm rain microphysical processes, mainly because the humid environment and the deep warm cloud layer facilitated the clouds’ condensational growth and collision–coalescence, and also reduced rain evaporation. As the storm evolved, the raindrop concentration increased rapidly from its initial stage and remained high until its weakening stage, but the mean raindrop size changed little. The extreme rain was characterized by the highest concentration of raindrops during the storm’s lifetime with a mean size of raindrops slightly larger than the maritime regime.

The microphysical characteristics of tropical cyclones vary in different rain regions, which affects not only the dynamic and thermodynamic mechanisms of the typhoon system but also the development of tropical cyclones. This study analyzed the raindrop size distribution (DSD) and the gamma DSD parameters associated with Typhoon Mangkhut using three two-dimensional (2D) video disdrometers from the Longmen Field Experiment Base for Cloud Physics, China Meteorological Administration in Guangdong, China during 16–17 September 2018. According to the observed track and radar reflectivity, this process can be divided into three distinct segments: the outer rainband before landfall (S1), the inner core (S2), and the outer rainband after landfall (S3). The outer rainband mainly produces stratiform rains, while the inner core mainly produces convective rains. The temporal and spatial variations in the rain rate, radar reflectivity, and DSD parameters of the different segments were analyzed and compared at three sites. Although the DSD characteristics are distinctly different in the three segments, the DSD characteristics of the same segment were similar at different sites. In the inner core (S2), the precipitation contains smaller drops (around 0.5 mm) and the concentrations are higher within each size bin compared with those of the other segments, resulting in the maximum rain rate (11.66 mm h⁻¹), radar reflectivity (34.53 dBZ), liquid water content (0.65 g m⁻³), and number concentration (4.12 mm⁻¹ m⁻³ on a logarithmic scale) occurring in this segment. The N_w–D_m scatter pairs have maritime-like convection, which increases outward from the inner core (S2). The relationship between the shape (μ) and slope (Λ) was also investigated. The microphysical characteristics determined in this study provide useful information for understanding microphysical precipitation processes and for improving the precision of numerical weather prediction models.

Different turbulent entrainment‐mixing mechanisms between clouds and environment are essential to cloud‐related processes; however, accurate representation of entrainment‐mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment‐mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the “black box” scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment‐mixing. Specifically, number concentration after entrainment‐mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment‐mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment‐mixing is negatively correlated with liquid water content affected by entrainment‐mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment‐mixing in weather/climate models.

Accurate quantitative precipitation estimation (QPE) during typhoon events is critical for flood warning and emergency management. Dual-polarization radar has proven to have better performance for QPE, compared to traditional single-polarization radar. However, polarimetric radar applications have not been extensively investigated in China, especially during extreme events such as typhoons, since the operational dual-polarization system upgrade only happened recently. This paper extends a polarimetric radar rainfall system for local applications during typhoons in southern China and conducts comprehensive studies about QPE and precipitation microphysics. Observations from S-band dual-polarization radar in Guangdong Province during three typhoon events in 2017 are examined to demonstrate the enhanced radar rainfall performance. The microphysical properties of hydrometeors during typhoon events are analyzed through raindrop size distribution (DSD) data and polarimetric radar measurements. The stratiform precipitation in typhoons presents lower mean raindrop diameter and lower raindrop concentration than that of the convection precipitation. The rainfall estimates from the adapted radar rainfall algorithm agree well with rainfall measurements from rain gauges. Using the rain gauge data as references, the maximum normalized mean bias ( $NMB$ ) of the adapted radar rainfall algorithm is 20.27%; the normalized standard error ( $NSE$ ) is less than 40%; and the Pearson’s correlation coefficient ( $CC$ ) is higher than 0.92. For the three typhoon events combined, the $NSE$ and $NMB$ are 36.66% and -15.78%, respectively. Compared with several conventional radar rainfall algorithms, the adapted algorithm based on local rainfall microphysics has the best performance in southern China.