Precipitation nowcasting is a highly challenging task in weather forecasting and plays a crucial role in protecting lives and property. However, autoregressive methods encounter training difficulties and are prone to error accumulation, whereas non-autoregressive models struggle to effectively utilize temporal information. To address these issues, this paper proposes a novel decoupled spatiotemporal network, TPDTC-Net, specifically for precipitation nowcasting. TPDTC-Net introduces an encoder–temporal predictor–decoder architecture based on a non-autoregressive model to prevent error accumulation, combining a time predictor and an adaptive dynamic weighting module that integrates the strengths of Transformer and convolutional neural networks, thereby improving the accuracy of precipitation nowcasting. From the spatial perspective, TPDTC-Net utilizes a multi-scale self-attention module in the encoder to extract global spatial features and employs a multiplicative convolution module to capture detailed features. In particular, the proposed adaptive dynamic weighting module effectively combines global and local features, allowing the encoder to dynamically adjust the weights based on different inputs and scenarios while learning feature fusion strategies. This enhances the supplementary role of convolution-extracted detail features to the global features extracted by the self-attention mechanism. From the temporal perspective, the time predictor adopts a Fourier self-attention mechanism and reshapes the feature maps passed from the encoder into abstract multivariate time series prediction tasks. By transforming unordered temporal information into ordered sequences, the time predictor effectively captures temporal dependencies, enabling the decoder to generate more accurate predictions. Extensive experiments on benchmark datasets show that TPDTC-Net significantly outperforms state-of-the-art networks in precipitation nowcasting. Specifically, on the KNMI dataset, compared to the second-best baseline model LPT-QPN (r≥ 10), the critical success index (CSI) and Heidke skill score (HSS) of TPDTC-Net increase by 10.35% and 9.37%, respectively. Besides, the balanced mean square error (BMSE) and balanced mean absolute error (BMAE) of TPDTC-Net decrease to 15.4787 and 1.1535. Similarly, on the CIKM AnalytiCup 2017 dataset, TPDTC-Net also delivers the best performance, with comparable performance trends observed. These results demonstrate the superior performance in terms of prediction accuracy of the proposed TPDTC-Net.

Accurate and timely estimation of tropical cyclone (TC) wind radii is significant for characterizing wind structure, disaster prevention, and mitigation. The existing methods have not sufficiently considered and utilized multimodal (i.e., multisource heterogeneous) data for wind radii estimation. Meanwhile, complete modalities (i.e., all used modalities) can hardly be available simultaneously, especially in real-time monitoring scenarios, which restricts the applicability of multimodal estimation models. It is challenging to maintain the accuracy of wind radii estimates when confronted with the issue of missing-modality. Therefore, to address these issues, this article aims to achieve robust TC wind radii estimation under both conditions with complete modalities and missing-modality. We first present a multimodal fusion network, MT-TCNet, for estimating TC wind radii under conditions with complete modalities. MT-TCNet benefits from multimodal data including satellite infrared (IR) images, reanalysis of wind fields, and the physical parameter maximum sustained wind (MSW) speed. MSW, which reflects TC intensity, is incorporated to embed the implicit relationship between TC intensity and wind radii. It is capable of providing superior and robust wind radii estimates in scenarios without time constraints, and can be used to generate long-term historical results. Furthermore, this article proposes MT-TCNet-Distill to alleviate the issue of missing-modality caused by delays in ERA5 reanalysis wind fields through generalized distillation and missing modality imputation. MT-TCNet-Distill broadens the applicability of MT-TCNet, which heavily relies on reanalysis data, enabling robust wind radii estimation in real-time scenarios. Comprehensive experiments demonstrate the superior performance of MT-TCNet and MT-TCNet-Distill compared to state-of-the-art methods.

Predicting motions of surrounding vehicles is critically important to help autonomous driving systems plan a safe path and avoid collisions. Although recent social pooling based LSTM models have achieved significant performance gains by considering the motion interactions between vehicles close to each other, vehicle trajectory prediction still remains as a challenging research issue due to the dynamic and high-order interactions in the real complex driving scenarios. To this end, we propose a wave superposition inspired social pooling (Wave-pooling for short) method for dynamically aggregating the high-order interactions from both local and global neighbor vehicles. Through modeling each vehicle as a wave with the amplitude and phase, Wave-pooling can more effectively represent the dynamic motion states of vehicles and capture their high-order dynamic interactions by wave superposition. By integrating Wave-pooling, an encoder-decoder based learning framework named WSiP is also proposed. Extensive experiments conducted on two public highway datasets NGSIM and highD verify the effectiveness of WSiP by comparison with current state-of-the-art baselines. More importantly, the result of WSiP is more interpretable as the interaction strength between vehicles can be intuitively reflected by their phase difference. The code of the work is publicly available at https://github.com/Chopin0123/WSiP.

Sea Surface Temperature (SST) prediction has attracted increasing attention due to its critical role in climate change. Traditional SST prediction methods can be mainly divided into two types, the physics-based numerical methods and the data-driven methods. However, the above methods have certain limitations, the former type can not perform well when the physical prior information is incomplete, while latter type can not perform well when the training data is insufficient. This paper uses a deep neural network to extract some valuable information from the data, and then introduces the space-time partial differential equation (PDE) to model the prior physical information referring to SST. By incorporating them together, a new Space-Time PDE-guided Neural Network (STPDE-NET), which can better deal with the prior physical information incompleteness and data insufficiency problems mentioned above is proposed. In the experiments, we compare our STPDE-NET with several famous or state-of-the-art SST prediction methods. The experimental results show that STPDE-NET outperforms the compared methods in most SST prediction circumstances, especially when the training data is insufficient.

Summary Hundreds of millions of Chinese people have become social network users in recent years, and aligning the accounts of common Chinese users across multiple social networks is valuable to many inter‐network applications, for example, cross‐network recommendation and cross‐network link prediction. Many methods have explored the proper ways of utilizing account name information into aligning the common English users' accounts. However, how to properly utilize the account name information when aligning the Chinese user accounts remains to be detailedly studied. In this article, we first discuss the available naming behavioral models as well as the related features for different types of Chinese account name matchings. Second, we propose the framework of Multi‐View Cross‐Network User Alignment (MCUA) method, which uses a multi‐view framework to creatively integrate different models to deal with different types of Chinese account name matchings, and can consider all of the studied features when aligning the Chinese user accounts. Finally, we conduct experiments to prove that MCUA can outperform many existing methods on aligning Chinese user accounts between Sina Weibo and Twitter. Besides, we also study the best learning models and the top‐k valuable features of different types of name matchings for MCUA over our experimental datasets.

Reblogging, also known as retweeting in Twitter parlance, is a major type of activities in many online social networks. Although there are many studies on reblogging behaviors and potential applications, whether neighbors who are well connected with each other (called “buddies” in our study) may make a difference in reblog likelihood has not been examined systematically. In this paper, we tackle the problem by conducting a systematic statistical study on a large SINA Weibo data set, which is a sample of 135, 859 users, 10, 129, 028 followers, and 2, 296, 290, 930 reblog messages in total. To the best of our knowledge, this data set has more reblog messages than any data sets reported in literature. We examine a series of hypotheses about how essential neighborhood structures may help to boost the likelihood of reblogging, including buddy neighbors versus buddyless neighbors, traffic between buddy neighbors, activeness (i.e., the total number of blog messages a user sends), and the number of buddy triangles a user participates in. Our empirical study discloses several interesting phenomena that are not reported in literature, which may imply interesting and valuable new applications.