In this work, graphene-like ZnO (g-ZnO)-based two-dimensional (2D) heterostructures (ZnO/WS₂ and ZnO/WSe₂) were designed as water-splitting photocatalysts based on the hybrid density functional. The dependence of photocatalytic properties on the rotation angles and biaxial strains were investigated. The bandgaps of ZnO/WS₂ and ZnO/WSe₂ are not obviously affected by rotation angles but by strains. The ZnO/WS₂ heterostructures with appropriate rotation angles and strains are promising visible water-splitting photocatalysts due to their appropriate bandgap for visible absorption, proper band edge alignment, and effective separation of carriers, while the water oxygen process of the ZnO/WSe₂ heterostructures is limited by their band edge positions. The findings pave the way to efficient g-ZnO-based 2D visible water-splitting materials.

Wind speed forecasting is critical to renewable energy generation, agriculture, and disaster prevention. Due to the uncertainty and intermittence of wind, conventional forecasting methods with numerical weather prediction (NWP) models fall short of achieving satisfactorily high accuracy. Post-processing of the predicted results is necessary for enhancing the prediction accuracy. The industry generally employs time-series prediction (TSP) methods for error correction, yet it is time-consuming since repeated modeling is needed if the location changes. Aiming at addressing this problem, this paper discusses the application of a deep learning algorithm in the post-processing period of wind speed prediction. NWP results are utilized as the forecasting basis, and deep learning algorithms are used for minimizing errors. An experimental study is conducted with industrial data. The functionality and performance of TSP-based algorithms including rolling mean, exponential smoothing, and autoregressive integrated moving average algorithms are compared with deep learning-based algorithms, including long-short term memory and convolutional neural network. From the numerical results, both TSP and deep-learning error-correction methods can effectively increase the accuracy of day-level NWP model prediction results, while deep-learning methods are data-driven, and no modeling process is needed. This work also poses an insight into the future development of wind speed prediction in meteorology.

Spatial-temporal sequence prediction is one of the hottest topics in the field of deep learning due to its wide range of potential applications in video-like data processing, specifically weather forecasting. Since most spatial-temporal observations evolve under physical laws, we adopt an attentional gating scheme to leverage the dynamic patterns captured by tailored convolution structures and propose a novel neural network, PastNet, to achieve accurate predictions. By highlighting useful parts of the whole feature map, the gating units help increase the efficiency of the architecture. Extensive experiments conducted on synthetic and real-world datasets reveal that PastNet bears the ability to accomplish this task with better performance than baseline methods.

Based on the “21.7” Henan extreme rainstorm case, this study investigates the influence of updating model state variables in the GSI-EnKF (Gridpoint Statistical Interpolation–ensemble Kalman filter) system with the Thompson microphysics scheme. Six sensitivity experiments are conducted to assess the impact of updating different model state variables on the EnKF analysis and subsequent forecast. The experiments include the Z_ALL experiment (updating all variables), the Z_NoEnv experiment (excluding dynamical and thermodynamical variables), the Z_NoNr experiment (excluding rainwater number concentration), and three additional experiments that examine the removal of updating horizontal wind (U, V), vertical wind (W), and perturbation potential temperature (T), which are marked as Z_NoUV, Z_NoW, and Z_NoT. The results indicate that updating different model state variables leads to various effects on dynamical, thermodynamical, and hydrometeor fields. Specifically, excluding the update of vertical wind or perturbation potential temperature has little effect on the rainwater mixing ratio, whereas excluding the update of the rainwater number concentration causes a significant increase in the rainwater mixing ratio, particularly in the northern region of Zhengzhou. Not updating horizontal wind or environmental variables shifts the rainwater mixing ratio northward, deviating from the observed rainfall center. The analysis of near-surface divergence and vertical wind also reveals that not updating certain variables could result in weaker or less detailed wind structures. Although radar reflectivity, which is mainly influenced by the mixing ratios of hydrometeors, shows consistent spatial distribution across experiments, their intensity varies, with the Z_ALL experiment showing the most accurate prediction. The 4 h deterministic forecasts based on the ensemble mean analysis demonstrate that updating all variables provides the best improvement in predicting the “21.7” Henan extreme rainstorm. These results emphasize the importance of updating all relevant model variables for improving predictions of extreme rainstorms.

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.