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The annual average rainfall of summer monsoon rainstorms in Yangjiang is highest in the southeast, decreasing towards the wings. Rainfall most frequently occurs in the early morning and around noon in June, with the western region experiencing the peak slightly earlier than other areas. Short-duration intense rainfall in the south occurs most frequently between 02:00 and 08:00, while in the north, it is more common between 08:00 and 14:00; the 6-hour accumulated rainfall ≥50mm most frequently occurs between 02:00 and 08:00. The weather situation conducive to monsoon rainstorms involves the stability of the South Asian high pressure at 200hPa over the Indochinese Peninsula, with Yangjiang at 500hPa ahead of the southern trough. During the early morning, the southwest monsoon strengthens from 925hPa over the northern Bay of Bengal to the northern South China Sea, pushing northward to Yangjiang’s land, creating the atmospheric conditions for monsoon rainstorms. Moisture primarily enters from the Indochinese Peninsula through the South China Sea, gradually increasing at night and reaching its peak from late night to early morning. The moisture channel and convergence height are below 700hPa. Atmospheric upward motion extends from the mid-to-upper levels to near the surface in the early morning, with the strong center moving from south to north.

The frequent occurrence of rainstorms poses a serious threat to the safety of transmission poles and towers. This paper aims to build a refined mechanical model by using multi-source data fusion technology and combining the structure, material, and other parameters of transmission poles and towers, to simulate the wind and rain loads on them under rainstorm conditions and accurately analyze their mechanical response and associated risks. By analyzing the stress, strain, displacement, and other mechanical indicators of the key parts of the tower, the stability of the tower under different rainstorm intensities can be accurately evaluated. The results show that the multi-source data fusion method can effectively improve the accuracy of simulation, identify the weak links of poles and towers in specific rainstorm scenarios, provide a scientific basis for the power sector to formulate reinforcement, operation, and maintenance strategies for poles and towers in advance, help improve the resilience of the transmission network during rainstorm disasters, and reduce the risk of power supply interruptions caused by pole and tower failures.

During the transformer operation, it is a common condition that the oil temperature decreases due to sudden rainstorms. Because the nitrogen layer is set to contact the transformer oil in the nitrogen blanket transformer directly, the influence of nitrogen dissolution and precipitation on the insulation characteristics is very important. In this paper, the effect of nitrogen is analyzed. First, the electrical field under the nitrogen bubble effect is simulated. Second, the sudden temperature drop situation is developed based on the reformed transformer. Last, the insulation changes are analyzed by partial discharge data. According to the analysis, the sudden temperature drop won’t influence the transformer operation.

To enhance the adaptability of millimeter wave (MMV) fuzes in rainy conditions, the attenuation and scattering impacts of rainfall was conducted. Furthermore, a mathematical model was formulated to study the characteristics of MMV fuze echo signals under rainfall environments, and the MMV fuzes echo signals are simulated based on the Monte Carlo method. The results show that rainfall leads to a substantial increase in the bottom noise of the echo signal, and the rainfall backward scattering signal mainly exists in the low-frequency region, but even in the heavy rain and rainstorm environment, the MMV fuze proximity ranging effect is basically unaffected. The results of the study can provide a reference basis for the adaptability of MMV fuze to complex environments.

Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

This study examined the rainfall characteristics and related synoptic processes of two extreme rainfall events that affected North China during 29 July–1 August 2023 (“23·7” rainstorm) and 3–5 August 1996 (“96·8” rainstorm), respectively. A stable dual-typhoon circulation pattern was observed in both rainstorm events. The surviving vortex of a landed typhoon, slowly approaching the rainstorm region, was blocked by a high-pressure system as it moved northwestward. Meanwhile, the second typhoon over the western Pacific Ocean facilitated remote northward transport of moisture. The low-level jet between the surviving vortex and the western Pacific subtropical high relayed moist warm air from the area of the South China Sea and western Pacific into the rainstorm region. Although the circulation patterns are similar, the stratification conditions, driving factors, and moisture budget of the two rainstorms differed during the main period of rainfall. The “23·7” rainstorm was categorized as warm-sector rainfall, as a result of the lifting of warm moist air over the eastern foothills of Taihang Mountains. In comparison with the situation of the “96·8” rainstorm, the surviving vortex of the “23·7” rainstorm traveled further northeastward and directly impacted the occurrence and progression of the rainfall, leading to relative northward displacement of the rainfall center, while the stronger net inward moisture flux caused greater regional average rainfall. The “96·8” rainstorm was broadly analogous to precipitation of a cold front, and the rainfall center was observed in the convergence area of warm and cold air masses before the mountains; the surviving vortex did not exert direct impact on the rainfall; and the more unstable stratification led to stronger hourly rainfall. The results derived through comparison of the two rainstorms could serve as valuable scientific reference for operational forecasting of heavy rainfall under similar environmental conditions over North China.

Effective management of rainstorm risk is essential for reducing regional rainstorm disaster risks and losses. In this paper, we discussed the influencing factors of urban rainstorm disaster (URSD) risk from four aspects and then constructed the index system of URSD risk assessment which includes 16 influencing factors. Furtherly, important indexes were extracted as the input of deep belief nets (DBN) model after analyzing the types and risk characteristics of URSD. As well as a coupling risk assessment model of URSD based on random forest and deep belief nets (RF–DBN) was established due to the capacity of high-dimensional data processing of RF and robustness of DBN. To test the validity of this risk assessment model, it was applied to evaluate the rainstorm disaster risk in 11 districts of Nanjing, China, from May to September during 2009 and 2017. Finally, the risk grade map of rainstorm disaster in Nanjing was drawn and the corresponding countermeasures for the regulation and control of URSD were put forward. The results show that the rainstorm risk in Nanjing is generally high during the period of rainy season and the risk of rainstorm disaster has egional features during the flood season.

Extremely heavy rainfall has posed a significant hazard to urban growth as the most common and disaster-prone natural calamity. Due to its unique geographical location, the metro system is more vulnerable to waterlogging caused by rainstorm disaster. Research on resilience to natural disasters has attracted extensive attention in recent years. However, few studies have focused on the resilience of the metro system against rainstorms. Therefore, this paper aims to develop an assessment model for evaluating metro stations’ resilience levels. Twenty factors are carried out from dimensions of resistance, recovery and adaptation. The methods of ordered binary comparison, entropy weight and cloud model are proposed to build the assessment model. Then, taking Chongqing metro system in china as a case study, the resilience level of 13 metro stations is calculated. Radar charts from dimensions of resistance, recovery, and adaptation are created to propose recommendations for improving metro stations’ resilience against rainstorms, providing a reference for the sustainable development of the metro system. The case study of the Chongqing metro system in china demonstrates that the assessment model can effectively evaluate the resilience level of metro stations and can be used in other infrastructures under natural disasters for resilience assessment.

The warming climate-induced intensification of hydrological cycle is amplifying extreme precipitation and increasing flood risk at regional and global scales. The evaluation of flood risk, which depends on assessment indicators, weights, as well as data quality, is the first step toward mitigation flood disasters. In this study, we accepted ten risk assessment indicators concerning hazard of disaster-causing factors, sensitivity of hazard-forming environments, and vulnerability of disaster-bearing bodies. We used a combined weighting method based on the analytic hierarchy process and entropy weight (AHP-EW) technique to evaluate rainstorm-induced flood risks across the Yellow River Basin (YRB) from 2000 to 2018. We observed flood hazards are intensifying across the YRB. Specifically, areas with medium flood hazards expanded from the lower to the middle and upper YRB. The sensitivity to floods exhibited a spatial pattern of increasing from southeast to northwest (lower to upper YRB). The increase in vegetation coverage in the middle and upper reaches of the YRB reduces the sensitivity to flood disasters. Flood vulnerability shows an increasing trend, with higher vulnerability mainly observed in the middle and lower YRB. The overall flood risk in the YRB shows an increasing trend, with a 9-fold increase in flood risk from 2000 to 2018. Medium to high flood risk and vulnerability can mainly be identified in the middle and lower YRB, where population and gross domestic product are concentrated. The intensifying rainstorm-induced flood risks over urban areas in these regions should arouse public concern.

An intensified hydrological cycle with global warming is expected to increase the intensity and frequency of extreme precipitation events. However, whether and to what extent the enhanced extreme precipitation translates into changes in river floods remains controversial. Here we demonstrate that previously reported unapparent or even negative responses of river flood discharge (defined as annual maximum discharge) to extreme precipitation increases are largely caused by mixing the signals of floods with different generating mechanisms. Stratifying by flood type, we show a positive response of rainstorm-induced floods to extreme precipitation increases. However, this response is almost entirely offset by concurrent decreases in snow-related floods, leading to an overall unapparent change in total global floods in both historical observations and future climate projections. Our findings highlight an increasing rainstorm-induced flood risk under warming and the importance of distinguishing flood-generating mechanisms in assessing flood changes and associated social-economic and environmental risks.Climate change is expected to intensify the hydrological cycle, but how this translates into changes in river floods is not clear. Here, the authors show that changes in river flood discharge differ between flood types, with increases in rainfall-induced floods and decreases in snow-related floods.