Waterlogging induced by rain in urban areas has a potential risk impact on property and safety. This paper focuses on the impact of rain on waterlogging and evaluates the waterlogging risk in the central city of Shanghai. A simplified waterlogging depth model is developed in different areas with different drainage capacity and rainfall in consumption of simplifying the effect of complex terrain characteristics and hydrological situation. Based on urban waterlogging depth and its classification collection, a Rain-induced Urban Waterlogging Risk Model (RUWRM) is further established to evaluate waterlogging risk in the central city. The results show that waterlogging depth is closely linked with rainfall and drainage, with a linear relationship between them. More rainfall leads to higher waterlogging risk, especially in the central city with imperfect drainage facilities. Rain-induced urban waterlogging risk model can rapidly gives the waterlogging rank caused by rainfall with a clear classification collection. The results of waterlogging risk prediction indicate that it is confident to get the urban waterlogging risk rank well and truly in advance with more accurate rainfall prediction. This general study is a contribution that allows the public, policy makers and relevant departments of urban operation to assess the appropriate management to reduce traffic intensity and personal safety or strategy to lead to less waterlogging risk.

Organized mesoscale convective systems (MCSs), such as squall lines, are often poorly forecasted in numerical weather prediction models. In this study, experiments are performed to show that the vertical distribution of latent heating (LH) plays an important role in organizing a trailing-stratiform (TS) squall line over South China. We investigated the impact of modifying the altitude of LH peaking around 2–5 km on the squall line. It is found that increasing LH peaking at a lower vertical level (around 2–3 km) is crucial for the simulation of the TS squall line by influencing the evolution of the front-to-rear tilted upward flow and its associated mesoscale rear-to-front flow below. The influence of different LH profiles on the structure of the simulated squall line is explained using the Rotunno–Klemp–Weisman (RKW) theory considering the effects of different heights of the vertical wind center. Stronger LH at lower heights results in a vertical wind core centered lower in the convection region. Behind the core, at the mid-to-low level, is a region of descending negative horizontal vorticity. Such negative vorticity region favors a descending flow below it. When this mesoscale flow with low equivalent potential temperature (${\theta }_e)$ descends and catches up with the convection at near-surface, it enhances both the strength and moving speed of the convection system. Results of this study highlight the sensitivities of the MCS structure to the vertical distribution of the thermodynamical field besides traditional cold pool aspects and provide insights for the study of squall line through shear convection interaction.

Waterlogging induced by rain in urban areas has a potential risk impact on property and safety. This paper focuses on the impact of rain on waterlogging and evaluates the waterlogging risk in the central city of Shanghai. A simplified waterlogging depth model is developed in different areas with different drainage capacity and rainfall in consumption of simplifying the effect of complex terrain characteristics and hydrological situation. Based on urban waterlogging depth and its classification collection, a Rain-induced Urban Waterlogging Risk Model(RUWRM) is further established to evaluate waterlogging risk in the central city. The results show that waterlogging depth is closely linked with rainfall and drainage, with a linear relationship between them. More rainfall leads to higher waterlogging risk, especially in the central city with imperfect drainage facilities. Rain-induced urban waterlogging risk model can rapidly gives the waterlogging rank caused by rainfall with a clear classification collection. The results of waterlogging risk prediction indicate that it is confident to get the urban waterlogging risk rank well and truly in advance with more accurate rainfall prediction. This general study is a contribution that allows the public, policy makers and relevant departments of urban operation to assess the appropriate management to reduce traffic intensity and personal safety or strategy to lead to less waterlogging risk.

Influenced by the quasi-stationary Mei-yu front and the development of mesoscale low vortices on the Mei-yu front, a torrential rain event occurred in Suzhou on July 6, 2020. The 24-hour rainfall recorded by the automatic station exceeded 200 millimeters, with the highest hourly intensity surpassing 70 millimeters, accompanied by thunderstorms and strong winds. This heavy precipitation process was characterized by suddenness, extremity, long duration, high precipitation efficiency, and backward propagation. Using various data sources such as the European ERA5 reanalysis data, ground-based automatic stations, sounding, Doppler weather radar, and wind profile radar, this paper analyzed in detail the circulation characteristics and radar echo structures of this torrential rain event, and further studied the triggering, feature evolution, and maintenance mechanisms of the heavy precipitation.The results show: (1) The continuously strengthening low-level jet stream propagates downward forming an ultra-low-level jet stream. Heavy precipitation is located on the left front side of the jet exit area, where strong convergence and lifting in the boundary layer overlap with the ascending branch of the secondary circulation above the shear line, resulting in heavy precipitation. (2) A supercell storm with intense precipitation produced this heavy rainfall event, characterized by a low centroid of strong echo; echo-free vault, bounded weak echo region, and other supercell structural features. During this event, the high reflectivity factor areas corresponded to high KDP value areas, indicating a large amount of liquid water content. Vertically, the KDP column can track the rainwater mixing ratio well, and its location corresponds to the vicinity of the short-term heavy precipitation occurrence. The high KDP value area mainly consists of numerous precipitation particles, and the vertical vortex wind field structure causes rapid growth of precipitation particles through continuous rolling. Furthermore, it can be found through correlation coefficients (CC) that there are always high-value areas for CC since they correspond to areas of intense precipitation. (3) Continuous triggering of mesoscale convective cloud clusters on the east-west ground convergence line leads to new development and forms a "train effect." Strong frontogenesis in both horizontal and vertical directions triggers and sustains this heavy precipitation event. The significant increase in liquid water and water vapor density has indicative significance for the occurrence of heavy precipitation.

Photovoltaic grid-connected applications pose challenges to the stable and safe operation of power systems. Solar power predicted by a hybrid model based on numerical weather prediction (NWP) and random forests (RF) in order to enable grid-connected planning to balance fluctuations. Using downward solar short-wave radiation flux, temperature, wind speed, and other meteorological parameters obtained from the numerical weather forecast model, PV power forecast models with different initial time and durations under various weather conditions by RF are established. The correlations between observed and predicted power in daily, monthly and seasonal change are analyzed and both two powers are compared under three different detailed weather types. The results show that compared with NWP-principle method, this hybrid NWP-RF method can simulate the change of solar power under different weather conditions in the middle-lower plain of the Yangtze River more accurately. This model has an acceptable amount of data computation and high prediction accuracy, so that it can apply to engineering.