Accurate forecasting of future meteorological elements is critical and has profoundly affected human life in many aspects from rainstorm warning to flight safety. The conventional numerical weather prediction (NWP) sometimes leads to unsatisfactory performance due to inappropriate initial state settings. In this paper, a short-term weather forecasting model based on wavelet packet denoising and Catboost is proposed, which takes advantage of the fusion information combining the historical observation data with the prior knowledge from NWP. The feature selection and spatiotemporal feather addition are also explored to further improve performance. The proposed method is evaluated on the datasets provided by Beijing weather stations. Experimental results demonstrate that compared with many deep-learning or machine-learning methods such as LSTM, Seq2Seq, and random forest, the proposed Catboost model incorporated with wavelet packet denoising can achieve shorter convergence time and higher prediction accuracy.

Precipitation nowcasting refers to the prediction of small-scale precipitation events at minute and kilometer scales within the upcoming 0 to 2 hours, which significantly impacts both human activities and daily life. However, prevailing deep learning models have primarily focused on a single radar echo data source, limiting their ability to effectively capture intricate and rapidly evolving precipitation patterns. Thus, meteorological satellite is considered to supplement radar echo data. To achieve a comprehensive integration of multi-source data with enhanced details, a two-stage fusion satellite and radar GAN-based prediction network (named FsrGAN) is proposed. In the first stage, we design a satellite-radar fusion prediction network known as FsrNet. This network employs an encoder-fusion-decoder architecture, where a novel spatial-channel attention (SCA) is proposed to enhance the filtering and fusion of multi-source and multi-scale features. In the second stage, we introduce a GAN-based network (FusionGAN) that also mines the complementary information of satellite images to sharpen the first-stage predicted radar maps with more details. Experiments are conducted on meteorological dataset in the Yangtze River Delta (YRD) region. The test results exhibit the notably superior performance of our model in terms of image quality and precipitation forecasting metrics in comparison to traditional optical flow-based methods and some well-known deep learning methods (ConvLSTM, ConvGRU, TrajGRU and PredRNN++). More importantly, our fusion model using satellite and radar data demonstrates the ability to predict convective initiation.

Precipitation nowcasting is quite important and fundamental. It underlies various public services ranging from rainstorm warnings to flight safety. In order to further improve the prediction accuracy for the spatiotemporal sequence forecasting problem, we propose an encoder–decoder deep residual attention prediction network, which adaptively rescales the multiscale sequence- and spatial-wise features and achieves very deep trainable residual prediction by integrating global residual learning and local deep residual sequence and spatial attention blocks (RSSABs). Experiments in a real-world radar echo map dataset of South China show that compared with the ingenious PredRNN++, TrajGRU methods, and newly proposed Unet-based methods, our ED-DRAP network performs better on the precipitation nowcasting metrics, as well as occupies small GPU memory.

Short-term rainfall prediction by radar echo map extrapolation has been a very hot area of research in recent years, which is also an area worth studying owing to its importance for precipitation disaster prevention. Existing methods have some shortcomings. In terms of image indicators, the predicted images are not clear enough and lack small-scale details, while in terms of precipitation accuracy indicators, the prediction is not accurate enough. In this paper, we proposed a two-stage model (two-stage UA-GAN) to achieve more accurate prediction echo images with more details. For the first stage, we used the Trajectory Gated Recurrent Unit (TrajGRU) model to carry out a pre-prediction, which proved to have a good ability to capture spatiotemporal movement of rain field. In the second stage, we proposed a spatiotemporal attention enhanced Generative Adversarial Networks (GAN) model with a U-Net structure and a new deep residual attention module in order to carry out the refinement and improvement of the first-stage prediction. Experimental results showed that our model outperforms the optical-flow based method Real-Time Optical Flow by Variational Methods for Echoes of Radar (ROVER), and some well-known Recurrent Neural Network (RNN)-based models (TrajGRU, PredRNN++, ConvGRU, Convolutional Long Short-Term Memory (ConvLSTM)) in terms of both image detail indexes and precipitation accuracy indexes, and is visible to the naked eye to have better accuracy and more details.

Crane systems generally operate in challenging environments (e.g., harsh weather conditions and high-altitude work), which heightens the requirements of control systems for the safety and disturbances rejection. However, underactuated nature poses difficulties in achieving the efficient positioning and swing elimination under these factors. To this end, we propose a method using a quadratic program (QP) formulation that combines an enhanced-coupling control Lyapunov function (ECCLF) with a new composite state control barrier function (CSCBF). Additionally, disturbance observers (DOBs) are employed to handle matched and unmatched disturbances effectively. The ECCLF introduces a new coupled control variable, where its tracking error ultimately exhibits an exponential convergence, elegantly overcoming the inability of full-state feedback linearization in underactuated systems. The CSCBF imposes time-varying safety constraints on the unilateral swing distance (USD), ensuring swing safety and meeting industrial payload positioning accuracy requirements. Especially, the traditional control barrier function (CBF) approach is not applicable for the proposed problem due to the infeasibility when the control coefficient of the CBF tends to zero, which is addressed by the proposed CSCBF approach. The safety of the CSCBF and the effectiveness of the controller synthesis are rigorously proven. Experimental validation demonstrates the effectiveness, safety, and disturbance rejection performance under practical working conditions.

Accurate atmospheric visibility prediction is of great significance to public transport safety. However, since it is affected by multiple factors, there still remains difficulties in predicting its heterogenous spatial distribution and rapid temporal variation. In this paper, a recursive neural network (RNN) prediction model modified with the frame-hopping transmission gate (FHTG), feature fusion module (FFM) and reverse scheduled sampling (RSS), named SwiftRNN, is developed. The new FHTG is used to accelerate training, the FFM is used for extraction and fusion of global and local features, and the RSS is employed to learn spatial details and improve prediction accuracy. Based on the ground-based monitoring data of atmospheric visibility from the China Meteorological Information Center during 1 January 2018 to 31 December 2020, the SwiftRNN model and two traditional ConvLSTM and PredRNN models are performed to predict hourly atmospheric visibility in central and eastern China at a lead of 12 h. The results show that the SwiftRNN model has better performance in the skill scores of visibility prediction than those of the ConvLSTM and PredRNN model. The averaged structural similarity (SSIM) of predictions at a lead up to 12 h is 0.444, 0.425 and 0.399 for the SwiftRNN, PredRNN and ConvLSTM model, respectively, and the averaged image perception similarity (LPIPS) is 0.289, 0.315 and 0.328, respectively. The averaged critical success index (CSI) of predictions over 1000 m fog area is 0.221, 0.205 and 0.194, respectively. Moreover, the training speed of the SwiftRNN model is 14.3% faster than the PredRNN model. It is also found that the prediction effect of the SwiftRNN model over 1000 m medium grade fog area is significantly improved along with lead times compared with the ConvLSTM and PredRNN model. All above results demonstrate the SwiftRNN model is a powerful tool in predicting atmospheric visibility.