Precipitation nowcasting is crucial for mitigating the impacts of severe weather events and supporting daily activities. Conventional models predominantly relying on radar data have limited performance in predicting cases with complex temporal features such as convection initiation, highlighting the need to integrate data from other sources for more comprehensive nowcasting. Unlike physics-based models, machine learning (ML)-based models offer promising solutions for efficiently integrating large volumes of diverse data. We present EF4INCA, a spatiotemporal Transformer model for precipitation nowcasting that integrates satellite- and ground-based observations with numerical weather prediction outputs. EF4INCA provides high-resolution forecasts over Austria, accurately predicting the location and shape of precipitation fields with a spatial resolution of 1 kilometre and a temporal resolution of 5 minutes, up to 90 minutes ahead. Our evaluation shows that EF4INCA outperforms conventional nowcasting models, including the operational model of Austria, particularly in scenarios with complex temporal features such as convective initiation and rapid weather changes. EF4INCA maintains higher accuracy in location forecasting but generates smoother fields at later prediction times compared to traditional models. Interpretation of our model showed that precipitation products and SEVIRI infrared channels CH7 and CH9 are the most important data streams. These results underscore the importance of combining data from different domains, including physics-based model products, with ML approaches. Our study highlights the robustness of EF4INCA and its potential for improved precipitation nowcasting. We provide access to our code repository, model weights, and the dataset curated for benchmarking, facilitating further development and application.
MAG-Net: Radar-Satellite Fusion for Severe Convective Nowcasting with Matched Evaluation of Satellite Contributions
Chinese Academy of Meteorological Sciences（中国气象局）；National Meteorological Information Center（国家气象信息中心）；Xiong’an Institute of Meteorological Artificial Intelligence（雄安气象人工智能研究院）
StormDiT: A generative AI model bridges the 2-6 hour 'gray zone' in precipitation nowcasting
Shanghai Typhoon Institute（上海台风研究所）；Key Laboratory of Numerical Modeling for Tropical Cyclone of the China Meteorological Administration（中国气象局热带气旋数值模拟重点实验室）；State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所大气边界层物理与大气化学国家重点实验室）；College of Earth and Planetary Sciences, University of Chinese Academy of Sciences（中国科学院大学地球与行星科学学院）；Earth System Modeling and Prediction Center, China Meteorological Administration（中国气象局地球系统模式与预测中心）；State Key Laboratory of Severe Weather Meteorological Science and Technology（severe weather meteorological science and technology 国家重点实验室）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology（南京信息工程大学气象灾害教育部重点实验室/气候变化与环境国际合作联合实验室/气象灾害预报评估与协同创新中心）
How Effective Are Time-Series Models for Precipitation Nowcasting? A Comprehensive Benchmark for GNSS-based Precipitation Nowcasting
Sanya Science and Education Innovation Park, Wuhan University of Technology（武汉理工大学三亚科技创新园）；Wuhan University of Technology（武汉理工大学）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；Interdisciplinary Artificial Intelligence Research Institute, Wuhan College（武汉学院交叉人工智能研究院）；Shanghai Artificial Intelligence Laboratory, Shanghai（上海人工智能实验室）；School of Earth and Space Science and Technology, Wuhan University（武汉大学地球空间科学与技术学院）；School of Water Resources and Hydropower Engineering, Wuhan University（武汉大学水利水电学院）；GNSS Research Center, Wuhan University（武汉大学GNSS研究中心）
Exploring Geometric Deep Learning For Precipitation Nowcasting
Technical University of Munich (TUM)（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Indian Institute of Technology Delhi（印度理工学院德里分校）
Nowcast3D: Reliable precipitation nowcasting via gray-box learning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学光明学院人工智能学院）；Earth System Modeling and Prediction Center, China Meteorological Administration（中国气象局地球系统建模与预测中心）；Shanghai Typhoon Institute, China Meteorological Administration（中国气象局上海台风研究所）；School of Atmospheric Physics, Nanjing University of Information Science and Technology（南京信息工程大学大气物理学院）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；College of Earth and Planetary Sciences, University of Chinese Academy of Sciences（中国科学院大学地球与行星科学学院）；Chinese Academy of Meteorological Sciences, China Meteorological Administration（中国气象科学研究院）；School of Engineering Science, University of Chinese Academy of Sciences（中国科学院大学工程科学学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大型模型与智能治理研究重点实验室）；State Key Laboratory of Severe Weather Meteorological Science and Technology（severe weather meteorological science and technology state key laboratory）；Laboratory of Climate Change Mitigation and Carbon Neutrality, Henan University（河南省气候变化缓解与碳中和实验室）
Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania（气象学与大气科学系，宾夕法尼亚州立大学，大学公园，宾夕法尼亚州）；National Center for Atmospheric Research, Boulder, Colorado（国家大气研究中心，伯尔尼，科罗拉多州）
Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas
Instituto de Física de Cantabria (IFCA), CSIC-UC（坎塔布里亚物理研究所（CSIC-UC联合机构））；MicroStep-MIS（MicroStep-MIS公司）；Scientific Computing Center (SCC), Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院科学计算中心）
Bayesian Deep Learning for Convective Initiation Nowcasting Uncertainty Estimation
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania（气象学与大气科学系，宾夕法尼亚州立大学，大学公园，宾夕法尼亚州）；National Center for Atmospheric Research, Boulder, Colorado（国家大气研究中心，博尔德，科罗拉多）；Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, Pennsylvania（计算与数据科学研究所，宾夕法尼亚州立大学，大学公园，宾夕法尼亚州）；Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, Pennsylvania（土木与环境工程系，宾夕法尼亚州立大学，大学公园，宾夕法尼亚州）
Three-Dimensional Variational Data Assimilation with Rapid Update Cycling for Short-Range Precipitation Forecasting: A Case Study of Heavy Rainfall in Bali, Indonesia
Bandung Institute of Technology (ITB)（万隆理工学院）；Ronin Institute for Independent Scholarship（龙人独立学术研究学院）；School of Systems Science and Industrial Engineering, State University of New York (SUNY)（纽约州立大学系统科学与工业工程学院）
Super-Resolution of Radar/Raingauge-Analyzed Precipitation Using Gaussian Process Regression with a Steering Kernel
Graduate School of Life and Environmental Sciences, University of Tsukuba（筑波大学生命环境科学研究科）；Meteorological Research Institute, Japan Meteorological Agency（日本气象厅气象研究所）；Faculty of Life and Environmental Sciences, University of Tsukuba（筑波大学生命环境科学 faculties）
A Machine Learning Nowcasting Method based on Real-time Reanalysis Data
College of Information Science and Engineering, Ocean University of China（中国海洋大学信息科学与工程学院）；Institute of Urban Meteorology, China Meteorological Administration（中国气象局城市气象研究所）；National Center for Atmospheric Research（国家大气研究中心）；National Meteorological Center, China Meteorological Administration（中国气象局国家气象中心）
