This study aims to investigate the effects of Chan-Hom (2015) typhoon-induced variations in enthalpy flux (EF) and moisture flux (MF) on intensity variations and rainfall. Chan-Hom (2015) made landfall at Zhoushan, then changed its direction and moved towards Korea. This analysis used ERA5 reanalyzed data, encompassing daily surface latent and sensible heat flux, along with wind measurements at a height of 10 m. Furthermore, wind components and specific humidity data from the 1000–200 hPa level in ERA5 were utilized to compute the MF and MF convergence, in accordance with the equations outlined in the methodology. This study examines the correlation among typhoon intensity, precipitation, MF, and EF. The mechanism by which Typhoon Chan-Hom has caused a decline in sea surface temperature (SST) was analyzed. Typhoons need a higher EF that can affect them before landfall to maintain their intensity. The highest LHF was observed (340 W/m²) prior to typhoon landfall, indicating that LHF responds to intensity-induced wind during Chan-Hom. Typhoon-induced rainfall is mainly controlled by the MF convergence, rather than the typhoon intensity. The spatial and temporal distributions of MF and MF convergence (MFC) during typhoon formation to landfall reveal that the symmetric MFC is dominated by typhoon intensity; a symmetrical structure is observed when the intensity is high. MFC includes wind convergence and moisture advection. Wind convergence dominates the MFC during typhoons, but moisture advection forms at the eyewall. MF during the typhoon’s landfall can relate to the amount of rainfall that occurred over the land. However, the rainfall pattern changed after landfall, and the typhoon changed its direction. SST cooling observed in the study area is mainly due to the upwelling process with strong cyclonic winds.

Landfalling typhoons frequently cause severe flooding and socioeconomic losses along the eastern coast of China, yet the relative influence of oceanic and storm-related factors on precipitation variability remains insufficiently understood. This study investigates the relationships among sea surface temperature (SST), translation speed, typhoon intensity, and precipitation characteristics for 23 typhoons that made landfall in Zhejiang Province during 2000–2025. Typhoon track, intensity, precipitation, and SST data were analyzed to examine pre-landfall evolution, precipitation variability across intensity categories, and the environmental factors associated with typhoon-induced precipitation. Results showed that SST generally decreased as typhoon approached the coast, with mean values declining from 27.9 °C offshore to 24.5 °C at landfall. Translation speed exhibited a non-linear evolution during the 120 h preceding landfall, with an average speed of 9.8 km h⁻¹ and a weak positive tendency toward the coast. Peak 24 h precipitation varied considerably among intensity categories. Severe typhoons (STY) produced the highest mean precipitation (120.9 ± 92.3 mm), whereas super typhoons (SuperTY) exhibited lower but more consistent rainfall totals. Correlation analysis revealed a weak relationship between translation speed and rainfall (r = −0.18), indicating that storm motion alone was insufficient to explain rainfall variability. SST showed a moderate negative correlation with rainfall (r = −0.53), highlighting the complex relationship between ocean thermal conditions and precipitation variability. These findings show that typhoon precipitation over Zhejiang is associated with the combined variability of storm intensity, ocean thermal conditions, atmospheric moisture availability, and coastal environmental conditions, providing scientific insights for understanding typhoon-related hazards under climate change.

Accurate regional lightning-occurrence prediction is important for operational weather-risk management, but its development is challenged by the severe class imbalance of grid-hour lightning samples. This study proposes a repeated random undersampling (RUS) stacking ensemble that combines heterogeneous machine-learning models and produces probabilistic lightning-occurrence forecasts using atmospheric variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5), together with spatial and temporal predictors. The effects of the undersampling ratio, ensemble size, and positive-class weighting were systematically evaluated, and a configuration with a RUS ratio of 1:15, 20 ensemble members, and a positive-class weight of 2 was selected. Using an operational threshold of 0.591 selected exclusively on the 2024 validation set, the final model achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.945, an area under the precision–recall curve (PR-AUC) of 0.235, a probability of detection (POD) of 0.521, and an F1-score of 0.302 on the independent 2025 test set. Physical-variable-group ablation and aggregated TreeSHAP (tree-based Shapley additive explanations) analyses were further conducted to interpret the predictions. Removing the spatiotemporal predictors produced the largest reduction in performance, followed by removing cloud and microphysical variables. Both the Light Gradient Boosting Machine (LightGBM) and categorical boosting (CatBoost) models consistently identified longitude, latitude, total-column cloud ice water, and convective available potential energy as the leading predictors, while higher cloud-ice-water content and stronger convective-instability indices generally shifted model outputs towards lightning occurrence. Direct transfer of the ERA5-trained ensemble to corresponding ECMWF forecast fields without retraining retained useful predictive skill and substantially outperformed the operational ECMWF lightning product over the collocated evaluation samples, although performance degradation indicated a cross-dataset distribution shift. These results demonstrate the value of combining imbalance-aware ensemble learning with physically interpretable predictors for regional lightning-risk forecasting, while the strong influence of geographic variables indicates that external validation and local recalibration or retraining are required before application to other regions.

Typhoon Yagi (2024) was a rapidly moving storm that lasted for eight days and made landfall in three locations, producing heavy rainfall over Hainan and Vietnam. This study aims to investigate the dynamical processes contributing to the heavy rainfall, concentrating on enthalpy flux (EF) and moisture flux (MF). The results indicate that both EF and MF increased significantly during the typhoon’s intensification stage and were high at the time of landfall. Before landfalling at Hainan, latent heat flux (LHF) reached 600 W/m², while sensible heat flux (SHF) was recorded as 80 W/m². Landfall at Hainan resulted in a decrease in LHF and SHF. LHF and SHF subsequently increased to 700 W/m² and 100 W/m², respectively, as noted prior to the landfall in Vietnam. The increased LHF led to higher evaporation, which subsequently elevated moisture flux (MF) following the landfall in Vietnam, while the region’s topography further intensified the rainfall. The mean daily rainfall observed over Philippines is 75 mm on 2 September (landfall and passing through), 100 mm over Hainan (landfall and passing through) on 6 September, and 95 mm at over Vietnam on 7 September (landfall and after), respectively. Heavy rainfall was observed over the land while the typhoon was passing and during the landfall. This research reveals that Typhoon Yagi’s intensity was maintained by a well-organized and extensive circulation system, supported by favorable weather conditions, including high sea surface temperatures (SST) exceeding 30.5 °C, substantial low-level moisture convergence, and elevated EF during the landfall in Vietnam.

Hurricanes cause significant destruction, disrupting transportation, and resulting in loss of life and property. High-precision marine meteorological data are essential for understanding hurricanes. ERA5 provides high temporal resolution and global coverage of analytical data; however, the accuracy of the data during hurricanes is uncertain. To investigate the applicability of ERA5 during hurricanes, this study used buoy data as reference values and assessed the applicability of ERA5 sea-surface wind speed (WS), sea-surface temperature (SST), and sea-surface pressure (SSP) during the 2020 Atlantic hurricane “ISAIAS” through spatial distribution and error analysis. The results indicate that there is a positive correlation and consistency between the trends of ERA5 and reference values. The average correlation coefficients for SSP, WS, and SST are 0.953, 0.822, and 0.607, respectively. Nearshore topography has a significant impact on data accuracy, resulting in greater errors compared to open-water areas. This study provides a theoretical basis for the application of ERA5 data during hurricanes.

This study investigates the atmospheric and upper-ocean responses associated with Super Typhoon (TY) Lekima (2019) during 4–12 August, including its landfall over Zhejiang Province, China. Variations in sea surface temperature (SST), latent heat flux (LHF), water vapor flux (WVF), total column water vapor (TCWV), vertical integral moisture divergence (VIMD), mean sea level pressure (MSLP), wind circulation, precipitation, subsurface temperature and salinity, and Ekman pumping velocity (W_E) were analyzed throughout the typhoon life cycle. Before intensification, SSTs of 29.5–31.0 °C indicated favorable ocean-surface conditions. During and after the storm passage, SST decreased to approximately 26.0–27.5 °C along portions of the track and below 25.0 °C near the Zhejiang coast, with stronger cooling on the right-hand side of the track. Surface salinity decreased by approximately 0.3–0.8 PSU during 8–10 August, with freshening extending through the upper 30–40 m. The Ekman pumping field identified regions where wind-stress curl favored upwelling and downwelling, with stronger positive signals occurring on the right side of the track. During the active maritime stage, LHF values of approximately −300 to −200 W m⁻² indicated enhanced upward latent heat transfer. WVF reached 1800–2200 kg m⁻¹ s⁻¹, TCWV exceeded 70 kg m⁻², and VIMD decreased below approximately −100 × 10⁻⁵ kg m⁻² s⁻¹, indicating enhanced moisture transport and convergence. These conditions coincided with daily precipitation exceeding 120 mm and locally reaching approximately 160 mm over northern and northwestern coastal Zhejiang. The minimum daily mean MSLP decreased from 1000 to 972–976 hPa during peak intensity and subsequently increased as Lekima approached landfall and weakened inland. While these responses are qualitatively consistent with previous TY case studies, our study provides new quantitative benchmarks and process attribution through heat budget analysis. This integrated, stage-based analysis provides a comprehensive quantitative reference for model validation and future comparative studies of landfalling typhoons in the western North Pacific.