Raindrop size distribution (DSD) measurements were taken with an onboard OTT Particle Size Velocity (Parsivel) disdrometer over the western Pacific during a marine survey from June to July 2014. Three subregions named south western Pacific (SWP), west western Pacific, and north western Pacific were separated for a comparative study of the variability of DSD. In addition to disdrometer data, FengYun‐2E, Moderate Resolution Imaging Spectroradiometer, National Centers for Environmental Prediction Global Final Analysis, and radiosonde data sets are used to illustrate the dynamical and microphysical characteristics associated with summer season rainfall of western Pacific. The DSD characteristics of six different rain rates and two rain types (convective and stratiform) were studied. Histograms of normalized intercept parameter log₁₀(N_w) and mass‐weighted mean diameter D_m indicated largest log₁₀(N_w) values in west western Pacific, while largest D_m values in south western Pacific, and the convective clusters in three regions could be identified between maritime like and continental like. The constrained relations between shape μ and slope Λ, N_w, and D_m of gamma DSDs are derived. An inverse relation of the coefficients and exponents of Z‐AR^b for convective rain were found in three regions. The R (Z_H, Z_DR) estimator is proved to be more accurate than Z‐R relation algorithm. And the empirical relations between D_m and radar reflectivity factor in the Ku and Ka bands are also derived to improve the rainfall retrieval algorithms in the open sea of Pacific. Furthermore, the possible causative mechanisms for the significant DSD variability in three regions were investigated with respect to convective intensity, raindrop evaporation, and other meteorological variables.

Using multi‐satellite observations, the cloud dynamic and microphysical characteristics were revealed during the rapid intensification (RI) of super‐typhoon Nanmadol (2022). As the storm intensifies, the eyewall contracts, the upper‐level divergence strengthens, and the cirrus cloud increases, leading to stronger upper‐level radial outflow and the vertical updraft. Meanwhile, it is found that there exists a dynamically attractive area in the outer rainbands, where particles grow effectively and form “a small amount of large particles” around 300 km from the eye. A theory of cloud dynamics‐microphysics interaction, called “tunnel theory,” is further proposed to explain the generation, accumulation, and concentrated downflow of large particles in the outer rainbands during RI. Results suggest the unique feature of particle distribution in the outer rainbands could be a potential indicator for RI.

In July 2021, Typhoon In-Fa attacked eastern China and broke many records for extreme precipitation over the last century. Such an unrivaled impact results from In-Fa’s slow moving speed and long residence time due to atmospheric circulations. With the supports of 66 networked surface disdrometers over eastern China and collaborative observations from the advanced GPM satellite, we are able to reveal the unique precipitation microphysical properties of the record-breaking Typhoon In-Fa (2021). After separating the typhoon precipitation into convective and stratiform types and comparing the drop size distribution (DSD) properties of Typhoon In-Fa with other typhoons from different climate regimes, it is found that typhoon precipitation shows significant internal differences as well as regional differences in terms of DSD-related parameters, such as mass-weighted mean diameter (D_m), normalized intercept parameter (N_w), radar reflectivity (Z), rain rate (R), and intercept, shape, and slope parameters (N₀, µ, Λ). Comparing different rain types inside Typhoon In-Fa, convective rain (N_w ranging from 3.80 to 3.96 mm⁻¹ m⁻³) shows higher raindrop concentration than stratiform rain (N_w ranging from 3.40 to 3.50 mm⁻¹ m⁻³) due to more graupels melting into liquid water while falling. Large raindrops occupy most of the region below the melting layer in convective rain due to a dominant coalescence process of small raindrops (featured by larger Z_Ku, D_m, and smaller N₀, µ, Λ), while small raindrops account for a considerable proportion in stratiform rain, reflecting a significant collisional breakup process of large raindrops (featured by smaller Z_Ku, D_m, and larger N₀, µ, Λ). Compared with other typhoons in Hainan and Taiwan, the convective precipitation of Typhoon In-Fa shows a larger (smaller) raindrop concentration than that of Taiwan (Hainan), while smaller raindrop diameter than both Hainan and Taiwan. Moreover, the typhoon convective precipitation measured in In-Fa is more maritime-like than precipitation in Taiwan. Based on a great number of surface disdrometer observational data, the GPM precipitation products were further validated for both rain types, and a series of native quantitative precipitation estimation relations, such as Z–R and R–D_m relations were derived to improve the typhoon rainfall retrieval for both ground-based radar and spaceborne radar.

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.