A heavy-rainfall event that occurred in North China during 19–20 July 2016, resulting in severe flooding, was investigated in this study. In this event, high-value total deformation overlapped the precipitation region, implying a close relationship between them. By deriving the non-geostrophic ω equation in a non-uniformly saturated moist atmosphere, the relation between vertical velocity and deformation was diagnosed. The Q-vector divergence on the right-hand side of the new ω equation was divided into three compositions, associated with horizontal divergence, vertical vorticity, and horizontal-wind deformation, respectively. It was found that the deformation component of Q-vector divergence contributed most to the negative Q-vector divergence in the precipitation region, implying an important role of deformation forcing in facilitating the vertical motion. In order to track the precipitation on the basis of deformation, potential deformation was proposed by virtue of the generalized potential temperature. The high-value potential deformation and precipitation were always overlapping, and shared an analogous temporal trend. This means that potential deformation can reflect the variation of heavy precipitation to a certain extent, and can serve as a tracker of the precipitation region. Graphical Abstract

A simulation of an extreme precipitation event in southern Xinjiang, which is the driest area in China, seizes the whole initiation process of the intense convective cell responsible for the high hourly rainfall amount. Considering the inner connection between convection and vertical motions, the characteristics and mechanisms of the vertical accelerations during this initial development of the deep convection are studied. It is shown that three key accelerations are responsible for the development from the nascent cumuli to a precipitating deep cumulonimbus, including sub-cloud boundary-layer acceleration, in-cloud deceleration, and cloud-top acceleration. By analyzing the right-hand terms of the vertical velocity equation in the framework of the WRF model, together with a diagnosed relation of perturbation pressure to perturbation potential temperature, perturbation-specific volume (or density), and moisture, the physical processes associated with the corresponding accelerations are revealed. It is found that sub-cloud acceleration is associated with three-dimensional divergence, indicating that the amount of upward transported air must be larger than that of horizontally convergent air. This is favorable for the persistent accumulation of water vapor into the accelerated area. In-cloud deceleration is caused by the intrusion or entrainment of mid-level cold air, which cools down the developing cloud and delays the deep convection formation. Cloud-top acceleration is responsible for the rapid upward extension of the cloud top, which is highly correlated with the convergence and upward transport of moisture.

Vorticity, divergence and deformation are three differentiation properties of the air flow. In this paper, by deriving the tendency equations of squared vorticity (SV), squared divergence (SD) and squared deformation (SE), it is found that the three properties can interact with each other through the terms with opposite signs in the equations. These opposite-sign terms permit a quantitative discussion on the interactions between vorticity, divergence and deformation, and the role of these interactions on weather developments. The corresponding framework is first applied to investigate the development of an extratropical meso-α-scale cyclonic vortex in China. Through diagnosing the SV equation and the interactive terms in the vortex event, it is found that the interaction between SV and SD was the controlling factor for the evolution of the vortex. The transition of SD–SV can intensify the vortex, while the transition of SV–SD causes the vortex to die out. This is consistent with the relation between vorticity and divergence that has been obtained from the vertical vorticity equation. Apart from this, a new finding is that deformation also plays a role in influencing the vortex development. On one hand, deformation can interact with vorticity to affect the vortex directly. On the other hand, deformation interacts with divergence and influence the vortex development in an indirect way. These results provide a new insight on the vortex development which is worth further investigating in the future.

Hailfall is a severe local weather event that can cause great economic losses as well as the loss of people’s property; however, it is still difficult for domestic meteorological stations to comprehensively observe hail, and domestic independently developed hail observation instruments are still scarce. To help enable better automatic hail observations, a new independently developed hailstone disdrometer based on the acoustic principle, which can be used to measure the hailstone number and particle size and to calculate the corresponding equivalent liquid precipitation of hailstones, is proposed in this paper. The characteristics of hailstones were preliminarily analyzed using observation data from two hailstone disdrometers installed in Aksu, Xinjiang, where three hail events were observed via the hailstone disdrometer in the summer of 2023. By analyzing the development of deep convection clouds using the Fengyun 4A satellite-based cloud-top brightness temperature, and synoptic conditions based on the fifth-generation global climate reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (the ECMWF ERA5 dataset), the hail formation mechanism was investigated in detail for one hailfall event. Accurate hail observations are an important basis for understanding spatiotemporal hail variation. The hailstone disdrometer proposed in this study offers a useful approach for domestic hail observation to provide first-hand hail information for the inspection of weather modification effects and disaster prevention and reduction.

Thunderstorms are the most frequent type of severe convective weather, which pose great threats to buildings, power transmission, communication facilities, and air transportation. Their analysis and forecasting have long been challenges in meteorological operations. Currently, deep learning-based lightning forecasting has a short valid period, mostly relying on satellite imagery, radar echoes, and lightning location data, focusing on very-short-range forecasting. The longest valid period does not exceed 6 h, and the forecasting accuracy is not high. Based on the physical quantities of the ECMWF numerical prediction model and the actual observations of single-point thunderstorms, this paper constructs a single-point thunderstorm forecasting model with a long validity period (>6 h). The study integrates multi-dimensional parameters such as thermal, dynamic, water vapor, and stratification instability, introduces the second-order moist potential vorticity S as a comprehensive predictor, systematically compares the forecasting performance of eight models, such as 1D PreRNN and ConvLSTM, and verifies the actual operational capability of the model through independent cases. The results show that the 1D PreRNN model has the best overall performance in all periods, which can effectively capture the temporal evolution characteristics of meteorological physical quantities and still has stable generalization performance under unbalanced samples. The model performs well in the 1st, 2nd, and 4th periods, and especially still has significant operational reference value in the 4th period with the longest forecasting validity period; only the 3rd period is weakly affected by the small number of samples. The effect of second-order moist potential vorticity has significant time-dependent differences. Its overall improvement effect is limited in short-term forecasting, but it can provide key disturbance signals in the 4th period with the longest forecasting validity period, and the model forecasting performance drops significantly after removal. The original binary cross-entropy loss is most suitable for the unbalanced sample scenario in this study, and weighted losses are prone to overcorrection. The method in this paper can achieve stable and reliable single-point thunderstorm forecasting for more than 6 h, and can provide long-term fixed-point meteorological support for key scenarios such as aerospace and new energy stations.