Radar Echo Extrapolation (REE) can facilitate accurate and expeditious nowcasting of precipitation, reducing the reliance on complex Numerical Weather Prediction (NWP) models. Spatial-temporal forecasting methods dominate this task because they can fully exploit the spatio-temporal dependencies and complex dynamic patterns inherent in radar echo data. However, they struggle with handling uncertainty and incorporating domain-specific knowledge, often resulting in blurry or unrealistic predictions. We propose a Hypergraph-enhanced Latent Diffusion Model (HyDiff) to address these limitations. The EchoDiff has been utilized to aid in accurate extrapolation. To accurately describe precipitation microphysics while adhering to the hydro-microphysical and multiscale coupling principles, the method integrates additional semantic information into the model. Specifically, the Differential Reflectivity Factor (ZDR) and Differential Propagation Phase Shift (KDP) are incorporated into the model as additional semantic information. Furthermore, we introduce a Hypergraph Neural Network (HGNN) into the extrapolation method to capture correlation information across regions. Experiments show that HyDiff effectively handles uncertainty, incorporates domain-specific prior knowledge, and generates forecasts with high operational utility.

As deep learning technology continues to advance in the field of meteorological forecasting, accurate radar echo extrapolation technology is crucial for two-hour precipitation prediction. Recent approaches commonly utilize 2D CNN and 3D CNN for feature extraction. However, the absence of high-level temporal sequence features prevents the model from acquiring sufficient information for accurate precipitation forecasting. To this end, we introduce a MIPRNet for precise precipitation forecasting. The Multi-Information Extractor (MIE), based on graph convolution and Fourier transforms, captures high-level complex temporal features of extrapolation evolution and frequency characteristics of precipitation dynamics. Meanwhile, MHPA, which utilizes the multi-head attention mechanism, aggregates and captures potential precipitation evolution patterns. In the extrapolation process, MIPRNet uses the potential evolution patterns of extrapolation obtained from the information extraction part to perform extrapolation. Experimental results on two radar meteorological datasets demonstrate that MIPRNet outperforms existing models in terms of multiple authoritative metrics.

Weather forecasting, compared to other multivariate time-series prediction tasks, exhibits notably non-uniform distribution of observation sites. The prevailing approaches primarily leverage graph convolutional neural networks to extract spatial features. However, some studies suggest that the poor performance on uneven graphs is primarily due to the fact that traditional graph neural networks (GNNs) are essentially low-pass filters, discarding information beyond low-frequency information on the graph. From another perspective, since the essence of graph convolution is the smoothing of node features, for uneven graphs, there are noticeable differences in the smoothing rates of node features, leading to the coexistence of overfitting and underfitting phenomena. This issue is further exacerbated in higher-order graph structures, such as hypergraphs, due to the irregular and complex nature of hyperedges. To address this issue, we propose a filtered hypergraph neural network. Building on the calculation of hypergraph node smoothing rates, we balance the low-pass and high-pass filter convolutions’ feature extraction through a dual-stream architecture. On uneven graphs, it can be observed that neglecting high-frequency information and concentrating solely on low-frequency information impede the learning of node representations, thereby significantly affecting the performance of downstream prediction tasks. We conducted multi-dimensional time-series prediction experiments using meteorological data, and the results demonstrate that our model performs with high accuracy in node regression tasks across multiple channels.

Satellite image sequence prediction is a fundamental yet challenging task in weather forecasting. Existing approaches often combine neural network models (e.g., RNN, CNN, transformer, and GAN) to capture spatial features and temporal state transitions. While expressive, these models frequently overlook higher order motion correlations, leading to ambiguities in motion estimation and loss of appearance details. To address this, we propose HyperMM, a novel method for satellite image sequence prediction using a hypergraph-enhanced motion matrix. Unlike prior methods that compute pairwise feature similarities between pixels or patches within the same module, HyperMM constructs an appearance-independent motion matrix enhanced by hypergraph neural networks (HGNNs) to model high-order spatial correlations among pixel groups more effectively. Furthermore, we introduce interframe dynamic attention in the trunk network to improve temporal feature extraction, and intraframe static attention to reduce appearance information loss. Experiments on the Fengyun-4B (FY-4B) satellite dataset show that HyperMM achieves state-of-the-art performance by retaining fine-grained appearance detail while having the superior ability to accurately predict motion trends.

Weather forecasting is inextricably linked to human lives and represents a quintessential task of spatiotemporal modeling, necessitated by the spatial and temporal dependencies inherent in meteorological data. Recent studies have consistently shown the excellent performance of graph-based neural networks in accurately modeling spatiotemporal data across various applications. Yet, traditional graph neural networks (GNNs) are unable to handle the high-order diffusion and aggregation phenomena between meteorological data caused by advection. Moreover, the impacts of spatial correlation among multisource information and the presence of noise in meteorological data are often overlooked. This study proposes a novel approach for modeling the spatiotemporal dependencies in meteorological data using the multi-information spatiotemporal aggregation and estrangement hypergraph convolution network. This method employs a novel representation of meteorological data using hypergraphs to address the aforementioned challenges. Specifically, we construct adjacency and semantic hypergraphs to represent spatial correlations and then introduce aggregation and estrangement hypergraph convolution networks to effectively capture multi-information spatial correlations. A new reconstruction feature attention module has been developed to fuse aggregation and estrangement semantic spatial information across various subspaces. In addition, the hypergraph convolution is embedded within a recurrent neural network architecture to model the temporal correlations. Extensive experiments have been conducted on four weather datasets, and state-of-the-art performance has been achieved in comparison to mainstream baseline methods.

Precipitation nowcasting within 2 hours is an important and hard issue in weather research area. Benefiting from the outstanding nonlinear relationship modeling capability, methods based on deep learning have achieved significant success in the task of precipitation nowcasting compared to the others. However, existing deep learning based methods always disregard the intricate high-order correlations and lack substantial connections with the evolution of the precipitation system, which would lead blurred forecasts and implausible predictions. To address these issues, we proposed a new Precipitation Nowcasting Network within 2 hours model based on Hypergraph Neural Network (PN-HGNN). In this work, Hypergraph Neural Network is firstly adopted for extracting spatio-temporal dynamic echo features. Secondly, regulation evolution is in charge of capturing the memory features to guide the extrapolation. Finally, we design a dual branch module to extrapolate the radar echoes. The proposed model has been assessed on the dataset HKO-7. The experimental results demonstrate that PN-HGNN achieved better prediction performance than the six representative echo extrapolation models.

A multi-band multi-antenna system has become an important trend in the development of mobile communication systems. However, strong mutual coupling tends to occur between antenna elements with a small space, distorting array antennas’ performance. Therefore, in the multiple-input multiple-output (MIMO) antenna system, high isolation based on miniaturization of the antenna array has been pursued. We study in depth the methods of decoupling between antenna elements. Reasons for the existence of mutual coupling and advantages of mutual coupling reduction are analyzed. Then the decoupling methods proposed in recent works are compared and analyzed. Finally, we propose a metasurface consisting of double-layer short wires, which can be applied to improve the port isolation of antennas arranged along the H-plane and E-plane. Results show that the proposed metasurface has good decoupling effect on a closely placed antenna array.