Spatiotemporal time series nowcasting should preserve temporal and spatial dynamics in the sense that generated new sequences from models respect the covariance relationship from history. Conventional feature extractors are built with deep convolutional neural networks (CNN). However, CNN models have limits to image-like applications where data can be formed with high-dimensional arrays. In contrast, applications in social networks, road traffic, physics, and chemical property prediction where data features can be organized with nodes and edges of graphs. Transformer architecture is an emerging method for predictive models, bringing high accuracy and efficiency due to attention mechanism design. This paper proposes a spatiotemporal model, namely GTrans, that transforms data features into graph embeddings and predicts temporal dynamics with a transformer model. According to our experiments, we demonstrate that GTrans can model spatial and temporal dynamics and nowcasts extreme events for datasets. Furthermore, in all the experiments, GTrans can achieve the highest F1 and F2 scores in binary-class prediction tests than the baseline models.
When Does Advection-Aware Graph Nowcasting Help? A Controlled Study of Distributed Solar Ramp Forecasting with a Self-Supervised Cloud-Motion Estimator
Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks
Department of Geoscience and Remote Sensing（地质科学与遥感系）；Delft University of Technology（代尔夫特理工大学）；School of Engineering and Computer Science（工程与计算机科学学院）；Bern University of Applied Sciences（伯恩应用科学大学）
FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Guiyang Academy of Information Technology（贵阳信息技术研究院）；NTT DATA Corporation（NTT DATA株式会社）；NTT DATA Institute of Management Consulting Inc.（NTT DATA经营研究所）
Four-hour thunderstorm nowcasting using a deep diffusion model of satellite data
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Institute of Tropical and Marine Meteorology, China Meteorological Administration（中国气象局热带海洋气象研究所）；Hong Kong University of Science and Technology（香港科技大学）；National Satellite Meteorological Center, China Meteorological Administration（中国气象局国家卫星气象中心）
IMPA-Net: Meteorology-Aware Multi-Scale Attention and Dynamic Loss for Extreme Convective Radar Nowcasting
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME) / International Joint Research Laboratory on Climate and Environment Change (ILCEC) / Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters（气象灾害重点实验室（教育部） / 国际气候与环境变化联合研究实验室（ILCEC） / 气象灾害预报与评估协同创新中心）；Nanchang Meteorological Bureau（南昌气象局）；National Center for Atmospheric Research（国家大气研究中心）；Department of Electrical and Computer Engineering, Colorado State University（科罗拉多州立大学电气与计算机工程系）；Jiangsu Meteorological Observatory（江苏省气象台）；Institute of Urban Meteorology, China Meteorological Administration（中国气象局城市气象研究所）
