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The near-surface temperature prediction (NTP) is an important spatial–temporal forecast problem, which can be used to prevent temperature crises. Most of the previous approaches fail to explicitly model the long- and short-range spatial correlations simultaneously, which is critical to making an accurate temperature prediction. In this study, both long- and short-range spatial correlations are captured to fill this gap by a novel convolution operator named Long- and Short-range Convolution (LS-Conv). The proposed LS-Conv operator includes three key components, namely, Node-based Spatial Attention (NSA), Long-range Adaptive Graph Constructor (LAGC), and Long- and Short-range Integrator (LSI). To capture long-range spatial correlations, NSA and LAGC are proposed to evaluate node importance aiming at auto-constructing long-range spatial correlations, which is named as Long-range aware Graph Convolution Network (LR-GCN). After that, the Short-range aware Convolution Neural Network (SR-CNN) accounts for the short-range spatial correlations. Finally, LSI is proposed to capture both long- and short-range spatial correlations by intra-unifying LR-GCN and SR-CNN. Upon the proposed LS-Conv operator, a new model called Long- and Short-range for NPT (LS-NTP) is developed. Extensive experiments are conducted on two real-world datasets and the results demonstrate that the proposed method outperforms state-of-the-art techniques. The source code is available on GitHub: https://github.com/xuguangning1218/LS_NTP. • We jointly incorporate long- and short-range spatial correlations in NTP prediction. • Theoretical findings proved that the proposed LS-Conv is a general version of CNN. • We develop a new spatial–temporal model named LS-NTP for the NTP task. • Extensive experiments are conducted on two real-world datasets. [ABSTRACT FROM AUTHOR]

Typhoons are one of the most destructive types of disasters. Several statistical models have been designed to predict their paths to reduce damage, casualties, and economic loss. To further increase prediction accuracy, two key challenges are (1) to extract better nonlinear 3D features of typhoons, which is hard due to their complex high-dimensional properties, and (2) to combine suitable 2D and 3D features in a proper way to improve predictions. To address these challenges, this paper presents a novel spatio-temporal deep learning model named Attention-based Multi ConvGRU (AM-ConvGRU). To automatically select high response isobaric planes of typhoons when considering their whole 3D structures, AM-ConvGRU leverages the Residual Channel Attention Block (RCAB). Furthermore, it integrates a novel model named Multi-ConvGRU to extract large-scale nonlinear spatial features of typhoons. Moreover, the approach relies on a Wide & Deep framework to fuse the traditional Generalized Linear Model (GLM) with the proposed AM-ConvGRU model. To evaluate the designed approach, extensive experiments have been conducted using real-world typhoons data from the Western North Pacific (WNP) basin obtained from both the China Meteorological Administration (CMA) dataset and the EAR-Interim dataset maintained by the European Centre for Medium-Range Weather Forecasts (ECMWF). Results show that the proposed method outperforms state-of-the-art deep learning typhoon prediction methods. The source code is available on GitHub with the following link:https://github.com/xuguangning1218/Typhoon_Path. [ABSTRACT FROM AUTHOR]