Rainfall kinetic energy (RKE) constitutes one of the most critical factors that drive rainfall erosivity on surface soil. Direct measurements of RKE are limited, relying instead on the empirical relations between kinetic energy and rainfall intensity ( KE-I relation), which have not been well regionalized for data-scarce regions. Here, we present the first global rainfall microphysics–based RKE ( RKE _MPH ) flux retrieved from radar reflectivity at different frequencies. The results suggest that RKE _MPH flux outperforms the RKE estimates derived from a widely used empirical KE-I relation ( RKE _KE-I ) validated using ground disdrometers. We found a potentially widespread underestimation of RKE _KE-I , which is especially prominent in some low-income countries with ~20% underestimation of RKE and the resultant rainfall erosivity. Given the evidence that these countries are subject to greater rainfall-induced soil erosion, these underestimations would mislead conservation practices for sustainable development of terrestrial ecosystems.

Global warming has increased the occurrence of extreme weather events, causing significant economic losses and casualties from rainfall-induced landslides. China, being highly prone to landslides, requires comprehensive predictions of future rainfall-induced landslide risks. By developing a landslide-prediction model integrated with the CMIP6 GCMs ensemble, we predict the spatiotemporal distribution of future rainfall-induced landslides in China, incorporating antecedent soil-wetness factors. In this study, antecedent soil wetness is represented by the antecedent effective rainfall index (ARI), which accounts for cumulative rainfall, evaporation, and runoff losses. Firstly, we calculated landslide susceptibility using seven geographic factors, such as slope and geology. Then, we constructed landslide threshold models with two antecedent soil-wetness indicators. Compared to the traditional recent cumulative rainfall thresholds, the landslide threshold model based on ARI demonstrated higher hit rates and lower false alarm rates. Ensemble predictions indicate that in the early 21st century, the risk of landslides decreases in the Qinghai–Tibet Plateau, Southwest, and Southeast regions but increases in other regions. Mid-century projections show a 10% to 40% increase in landslide risk across most regions. By the end of the century, the risk is expected to rise by more than 15% nationwide, displaying a spatial distribution pattern that intensifies from east to west.

Current radar techniques focus on rainfall observations, leaving a research gap in rainfall energy ( E ) involving the interaction of raindrops and land surface processes. E is defined as the accumulated kinetic energy per unit rainfall and is a key parameter in the understanding process of the rainfall impact on the land surface. Utilizing the capability of dual-polarization radar to detect the rainfall microphysics characteristics, this study proposes the first computational model for estimating E from radar signals. The model investigates the mechanistic correlation between the radar dual-polarization parameters and E , and finds that specific differential phase ( K_DP ) and horizontal reflectivity ( Z_H ) have the strongest correlation with E . Therefore, the study develops radar-based empirical regression and random forest (RF) models for E estimation, where RF models consider whether the sensitive K_DP is available. The results show that the RF models improve the accuracy of estimating E and has a Pearson coefficient greater than or equal to 0.97 with station measured E , and their spatially extensive capability of the models is further validated. In addition, both TRM and RF-based radar data have underestimated daily E estimates compared to the disdrometer observations, with smaller BIAS and RMSE and higher Pearson correlations for RF. This study contributes to enhancing the understanding of rainfall processes in the context of climate change and have great potential for applications in hydrological modeling, flood forecasting, and agricultural planning.

As rainfall undergoes a series of complex microphysical processes in the atmosphere, studying its vertical profiles is crucial for understanding the mechanisms of rainfall evolution. While previous research has focused on vertical profiles of rainfall intensity and drop size distribution (DSD) parameters, there remains a gap in the study of rainfall energy. This study uses the Dual-frequency Precipitation Radar (DPR) in Global Precipitation Measurement (GPM) to analyze the vertical profile characteristics of rainfall energy (KE_t) for the first time. Using DPR data collected from 2015 to 2022 across the UK, rainfall energy calculations reveal a strong correlation (over 0.99) between the rainfall energy of adjacent 125 m height bins, with stratiform rain showing higher correlation than convective rain. Specifically, below 1500 m, the correlation coefficient for KE_t in stratiform rain is 0.9973, while for convective rain it is 0.9957, showing higher KE_t variability in convective rain. The study also introduces the change ratio (R) to characterize the degree of change from the lower to upper height bins, finding that rainfall energy variability has a larger standard deviation compared to DSD parameters, with standard deviations for R mean values of KE_t reaching up to 28.37% for convective rain and 12.08% for stratiform rain within 1500 m. Additionally, the profiles of rainfall energy exhibit significant seasonal variations, with these variations increasing with height. KE_t is consistently highest in summer and lowest in winter at all same altitudes. This study enhances the understanding of the vertical pattern of rainfall evolution, contributes to providing more accurate surface rainfall energy estimates, analyzing influencing factors and the uncertainty of vertical rainfall variability.

The accurate retrieval of the raindrop size distribution (DSD) is a longstanding objective in meteorology because it underpins reliable quantitative precipitation estimation. Among remote sensors, weather radars are the primary tool for mapping DSD over wide areas, and phased-array systems in particular have demonstrated unique advantages owing to their high temporal and spatial resolution together with agile beam steering. Exploiting the underused high-resolution capability of an X-band phased-array radar, this study induced a Rainfall Regression Model (RRM). The RRM assumes a normalized gamma DSD model and retrieves its three parameters. It was then applied to a rain event influenced by the remnant circulation of Typhoon Haikui that affected Guangzhou on 8 September 2023. First, collocated disdrometer observations and T-matrix scattering simulations are used to build polynomial regressions between DSD parameters (D₀, N_w, μ) and the polarimetric variables. Validation against independent disdrometer samples yields Nash–Sutcliffe efficiencies of 0.93 for D₀ and 0.91 for log₁₀N_w. The RRM is then applied to the full volumetric radar data. Horizontal maps reveal that the surface elevation angle consistently exhibited the largest standard deviation for all three parameters. A vertical profile analysis shows that large-drop cores (D₀ > 2 mm) can reside above 2 km and that iso-value contours tilt rather than align vertically, implying an appreciable horizontal drift of raindrops within the complex remnant typhoon–monsoon wind field. By demonstrating the ability of X-band phased-array radar to resolve the three-dimensional microphysical structure of remnant typhoon precipitation, this study advances our understanding of the vertical characteristics of raindrops and provides high-resolution DSD information that can be directly ingested into severe weather monitoring and nowcasting systems.