In this study, we evaluated the performance of rain-retrieval algorithms for the Version 6 Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) products, against disdrometer observations and improved their retrieval algorithms by using a revised shape parameter µ derived from long-term Particle Size Velocity (Parsivel) disdrometer observations in Jianghuai region from 2014 to 2018. To obtain the optimized shape parameter, raindrop size distribution (DSD) characteristics of summer and winter seasons over Jianghuai region are analyzed, in terms of six rain rate classes and two rain categories (convective and stratiform). The results suggest that the GPM DPR may have better performance for winter rain than summer rain over Jianghuai region with biases of 40% (80%) in winter (summer). The retrieval errors of rain category-based µ (3–5%) were proved to be the smallest in comparison with rain rate-based µ (11–13%) or a constant µ (20–22%) in rain-retrieval algorithms, with a possible application to rainfall estimations over Jianghuai region. Empirical D_m–Z_e and N_w–D_m relationships were also derived preliminarily to improve the GPM rainfall estimates over Jianghuai region.

Modifications are made to the calculation of warm‐rain hydrometeor number concentrations of the Weather Research and Forecasting double‐moment six‐class (WDM6) microphysics scheme. These modifications are related with cloud condensation nuclei activation, cloud droplet evaporation, and snow and graupel melting. The effects of the modifications are tested in a North China heavy rain event (Case01) as well as a short‐duration rain event in Hebei Province of China (Case02). The results show that the WDM6 scheme underestimates the cloud mixing ratios (Qc) and number concentrations (Nc), while it overestimates the rain mixing ratios (Qr) and number concentrations (Nr). Meanwhile, the WDM6 scheme overestimates the mass mean diameter of cloud droplets (Dmc) and underestimates that of raindrops (Dmr). Through microphysical budget analysis, it is found that the inappropriate calculations of cloud condensation nuclei activation and cloud evaporation are responsible for the underestimation of Nc and larger Dmc. The overestimation of the autoconversion rate resulting from larger Dmc plays a decisive role in the overestimation of Qr and Nr, as well as in the underestimation of Qc. The overestimation of raindrops produced by the melting of snow and graupel is another reason for higher Qr and Nr. By comparing with the airborne observations in Case02, it is found that the Nc and Dmc simulation are notably improved with the direct modifications of Nc. The Nr simulation is closer to the observation after the melting of snow and graupel are modified.

The number concentrations and drop size distributions (DSDs) of warm-rain hydrometeors play an important role in the simulation of microphysical processes. To evaluate the performance of the WDM6 scheme, which predicts the cloud number concentration (N_c) explicitly in aspects of warm-rain hydrometeors number concentrations and DSDs, the simulation of the WDM6 scheme is compared with airborne observations of a flight trial, as well as with the simulations of the Thompson scheme and Morrison scheme. Results show that the WDM6 scheme produces smaller (larger) cloud (rain) number concentrations and wider cloud DSDs compared to the observations, with the largest biases at upper levels of stratiform cloud (SC). The Thompson scheme and the Morrison scheme, both of which set the Nc as a constant, compare better to the observations than the WDM6 scheme in aspects of N_c and DSD. Sensitivity tests of the initial cloud condensation nuclei (CCN) number concentration (CCN₀) of the WDM6 scheme show that a better choice of the initial CCN₀ may improve the simulation of convective cloud but helps little in terms of SC. The simulation of rain number concentration and DSD is not sensitive to the CCN₀ in the WDM6 scheme. GRAPHICAL ABSTRACT

Cloud drop diffusion growth is a fundamental microphysical process in warm clouds. In the present work, a new Lagrangian advection scheme (LAS) is proposed for solving this process. The LAS discretizes cloud drop size distribution (CDSD) with movable bins. Two types of prognostic variable, namely, bin radius and bin width, are included in the LAS. Bin radius is tracked by the well-known cloud drop diffusion growth equation, while bin width is solved by a derived equation. CDSD is then calculated with the information of bin radius, bin width, and prescribed droplet number concentration. The reliability of the new scheme is validated by the reference analytical solutions in a parcel cloud model. Artificial broadening of CDSD, understood as a by-product of numerical diffusion in advection algorithm, is strictly prohibited by the new scheme. The authors further coupled the LAS into a one-and-half dimensional (1.5D) Eulerian cloud model to evaluate its performance. An individual deep cumulus cloud studied in the Cooperative Convective Precipitation Experiment (CCOPE) campaign was simulated with the LAS-coupled 1.5D model and the original 1.5D model. Simulation results of CDSD and microphysical properties were compared with observational data. Improvements, namely, narrower CDSD and accurate reproduction of particle mean diameter, were achieved with the LAS-coupled 1.5D model.

A detailed analysis of the dynamic frequency spectrum characteristics of gravity waves (GWs) during a local heavy rainfall event on 20–21 November 2016 in Foshan, China, is presented. The results of this analysis, which was based on high-precision microbarograph data, indicate that GWs played a key role in generating the rainstorm. The GWs experienced two intermittent periods of amplitude enhancement and period widening. The largest amplitudes of the GWs were 80–160 Pa, with a corresponding period range of 140–270 min, which were approximately 4 h ahead of the rainstorm. The severe storms appeared to affect the GWs by augmenting the wave amplitudes with center amplitudes of approximately 80–100 Pa and periods ranging between 210 and 270 min; in particular, the amplitudes increased to approximately 10 Pa for GWs with shorter periods (less than 36 min). The pre-existing large-amplitude GWs may be precursors to severe storms; that is, these GWs occurred approximately 4 h earlier than the time radars and satellites identified convections. Thus, these results indicate that large-amplitude GWs constitute a possible mechanism for severe-storm warning. Graphical abstract