Nowcasting of severe convective precipitation is of great importance in meteorological disaster prevention. Radar echo extrapolation is an effective method for short-term precipitation nowcasting. The traditional radar echo extrapolation methods lack the utilization of radar historical data as well as overlooking the nonlinear motion of small- to medium-sized convective systems in radar echoes. To solve this, we propose a deep-learning model combining CNN and RNN. The model T-UNet proposed in this paper uses an efficient convolutional neural network of UNet architecture with a residual network, where the encoder and decoder networks are connected by nested dense skip paths, while a TrajGRU recurrent neural network is added at each layer, to achieve the perceptual capability of time series. The quantitative statistical evaluation showed that the use of T-UNet could improve the nowcasting skill (CSI score, HSS score) by a maximum of 10.57% and 7.80% over a 60 min prediction cycle. Further evaluation showed that T-UNet also improved the prediction accuracy and prediction performance in the strong echo region.

In order to forecast some high intensity and rapidly changing phenomena, such as thunderstorms, heavy rain, and hail within 2 h, and reduce the influence brought by destructive weathers, this paper proposes a weather radar echo extrapolation method based on deep learning. The proposed method includes the design and combination of the data preprocessing, convolutional long short-term memory (Conv-LSTM) neuron and encoder–decoder model. We collect eleven thousand weather radar echo data in high spatiotemporal resolution, these data are then preprocessed before they enter the neural network for training to improve the data’s quality and make the training better. Next, the neuron integrates the structure and the advantages of convolutional neural network (CNN) and long short-term memory (LSTM), called Conv-LSTM, is applied to solve the problem that the full-connection LSTM (FC-LSTM) cannot extract the spatial information of input data. This operation replaced the full-connection structure in the input-to-state and state-to-state parts so that the Conv-LSTM can extract the information from other dimensions. Meanwhile, the encoder–decoder model is adopted due to the size difference of the input and output data to combine with the Conv-LSTM neuron. In the neural network training, mean square error (MSE) loss function weighted according to the rate of rainfall is added. Finally, the matrix “point-to-point” test method, including the probability of detection (POD), critical success index (CSI), false alarm ratio (FAR) and spatial test method contiguous rain areas (CRA), is used to examine the radar echo extrapolation’s results. Under the threshold of 30 dBZ, at the time of 1 h, we achieved 0.60 (POD), 0.42 (CSI) and 0.51 (FAR), compared with 0.42, 0.28 and 0.58 for the CTREC algorithm, and 0.30, 0.24 and 0.71 for the TITAN algorithm. Meanwhile, at the time of 1 h, we achieved 1.35 (total MSE ) compared with 3.26 for the CTREC algorithm and 3.05 for the TITAN algorithm. The results demonstrate that the radar echo extrapolation method based on deep learning is obviously more accurate and stable than traditional radar echo extrapolation methods in near weather forecasting.

Tornadoes are highly destructive small-scale extreme weather processes in the troposphere. The weather radar is one of the most effective remote sensing devices for the monitoring and early warning of tornadoes. The existing tornado detection algorithms based on radar data are unsupervised and have strict multi-altitude constraints, such as the tornado detection algorithm based on tornado vortex signatures (TDA-TVS), which may lead to high false alarm rates, and the performance of the detection algorithm is greatly affected by the radar data quality control algorithm. A novel TDA-RF algorithm based on the random forest (RF) classification algorithm is proposed for real-time tornado identification of the S-band China new generation of Doppler weather radar (CINRAD-SA). The TDA-RF algorithm uses velocity features to identify tornadoes and adds features related to reflectivity and velocity spectrum width in radar level-II data. Historical CINRAD-SA tornado data from 2006–2015 are used to construct the tornado dataset and train the TDA-RF model. The performance of TDA-RF is evaluated using CINRAD-SA data from five tornadoes of 2016–2020 with enhanced Fujita(EF) scale ratings ranging from EF0 to EF4 and distances from 10 to 130 km to the radar. TDA-RF performs well overall with the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of 71%, 29%, and 55%, respectively. Moreover, the TDA-RF improves POD and CSI, and reduces FAR compared to the TDA-TVS. The maximum tornado early-warning time of TDA-RF is 17 min, and the average is 6 min; TDA-RF can provide classification probability according to the tornado generation and development process to facilitate tracking ability.

This study is the first to use the observation data of a fog monitor, a visibility meter, and an automatic weather station to carry out a comprehensive observation experiment from the perspective of microphysics on a severe sea fog process in Beilun District, China, from 14 to 15 June 2021. The results show the following: (1) Temperature is closely related to nucleation, condensation growth, and other processes. The decrease (increase) in temperature is the main reason for the enhancement (weakening) of nucleation and the growth of condensation (evaporation of droplets), which leads to an increase (or decrease) in microphysical quantities, such as droplet number concentration and liquid water content. (2) The average droplet number spectral distribution roughly conforms to the Gamma distribution, and the spectral distribution of the fog process presents a ”multi-peak” structure, with peak diameters of 6 μm, 12 μm, 16 μm, 24 μm, and 44 μm. Droplets with a diameter of less than 16 μm account for 75% of the droplet size distribution. (3) During this sea fog process, three microphysical parameters, namely, number concentration, liquid water content, and average diameter, are all positively correlated in pairs, but the positive correlation between the number concentration and the average diameter is weak. This shows that the condensation nucleation and the condensation growth of droplets are the main processes in this sea fog process and that the collision process occurs but is not the dominant process. The sea fog comprehensive observation experiment provides an important demonstration of the microphysics research of sea fog in the eastern coastal areas of China and provides more reference information for sea fog research and equipment comparisons between different regions. At the same time, it also provides an essential scientific basis for the short-term forecast of sea fog in the future and for the optimization of the microphysical parameters of related models.

This study is the first in the region to use Global Precipitation Mission Dual-Frequency Precipitation Radar (GPM-DPR) and Fengyun-2G (FY-2G) observations to qualitatively and quantitatively study the Southwest Vortex evolution characteristics during the flood season from 2019 to 2021. Furthermore, vertical characteristics of the two main precipitation types in the Southwest Vortex, stratiform and convective, were statistically analyzed at different life stages, including horizontal and vertical distribution of precipitation particles, droplet spectrum characteristics, and vertically layered precipitation contribution. The results showed that: (1) The typical convective precipitation (CP) in the developing and mature stages has strong reflectivity distribution centers in the upper and lower layers, showing characteristics related to terrain. Additionally, the high-level hydrometeor particles are mainly solid precipitation particles, and particles in the lower layers collide and coalesce in the violent vertical motion of the airflow. (2) For the three stages of CP, the reflectivity below melting layer (ML) first showed a rapid weakening trend toward the surface and then remained unchanged, significantly changing its vertical structure. The main rainfall type of the Southwest Vortex system was stratiform precipitation (SP) in the three stages. (3) In the two types of cloud precipitation, the developing stage is generally composed of large and sparse precipitation particles, the mature stage of large and dense precipitation particles, and the dissipating stage of small and sparse precipitation particles. The findings of this study reveal the three-dimensional refined structure and vertical variation characteristics of different life stages of the Southwest Vortex precipitation cloud system and provide important tools and references for improving the accuracy of numerical models and the forecast level of short-term heavy precipitation under complex terrain.

The study focused on the variations of slosh patterns in three-dimensional rectangular tanks under oblique translational excitations by using an in-house Navier–Stokes model. The model was validated against different slosh patterns and then used to explore how the slosh pattern changed with various excitation frequencies. The excitation frequencies were divided into two categories: multiples of the first-order and third-order natural frequencies. The influences of the excitation frequency, excitation angle, and base ratio on the slosh pattern were identified by examining the free surface displacements and the sloshing forces, as well as their correlations. It was found that excitation frequencies related to the third-order natural frequency resulted in similar slosh patterns with more complicated free surface shapes compared to those related to the first-order natural frequency. Excitation angles had little influence on non-resonant cases but could remarkably affect the local features of other wave patterns and correlated beating periods. The slosh pattern was generally not affected by various base ratios, but the local wave shapes and dominant orientations would change.