Institut de Recherche Technologique Saint-Exupéry（圣埃克苏佩里技术研究院）；Institut de Recherche en Informatique de Toulouse（图卢兹计算机科学研究院）；Météo-France（法国气象局）；Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique（欧洲科学计算高级研究与培训中心）；Climate, Environment, Coupling and Uncertainties（气候、环境、耦合与不确定性研究所）
Precipitation nowcasting is of great importance for weather forecast users, for activities ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally obtained from numerical models, precipitation nowcasting needs to be very fast. It is therefore more challenging to obtain because of this time constraint. Recently, many machine learning based methods had been proposed. We propose the use three popular deep learning models (U-net, ConvLSTM and SVG-LP) trained on two-dimensional precipitation maps for precipitation nowcasting. We proposed an algorithm for patch extraction to obtain high resolution precipitation maps. We proposed a loss function to solve the blurry image issue and to reduce the influence of zero value pixels in precipitation maps.
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）
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）
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）
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）
When Does Advection-Aware Graph Nowcasting Help? A Controlled Study of Distributed Solar Ramp Forecasting with a Self-Supervised Cloud-Motion Estimator
Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP
Korea Financial Telecommunications and Clearings Institute（韩国金融电信与清算院）；Yonsei University（延世大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Korea Institute for International Economic Policy（韩国国际经济政策研究院）；Korea Institute of Public Finance（韩国公共财政研究院）；Electronics and Telecommunications Research Institute（电子通信研究院）
Skillful Precipitation Nowcasting using Deep Generative Models of Radar
Met Office（英国气象局）
降水临近预报，即提前两小时对降水进行高分辨率预报，支持许多依赖天气决策的部门的现实社会经济需求。国家的最先进的业务临近预报方法通常平流降水场与雷达为基础的风估计，并努力捕捉重要的非线性事件，如对流启动。最近引入的深度学习方法使用雷达直接预测未来的降雨率，不受物理限制。虽然它们准确地预测了低强度降雨，但它们的业务效用有限，因为它们缺乏约束，在较长的提前时间内产生模糊的临近预报，在更罕见的中到大雨事件中表现不佳。为了解决这些挑战，我们提出了一个深生成模型的概率临近预报雷达降水。我们的模型在长达1536 km x 1280 km的区域内产生现实和时空一致的预测，提前5 - 90分钟。在英国气象局50多位专家预测员的系统评估中，我们的生成模型在88%的情况下与两种竞争性方法相比，其准确性和实用性排名第一，证明了其决策价值和为现实世界专家提供物理见解的能力。当定量验证时，这些临近预报是熟练的，而不是诉诸模糊。我们表明，生成式临近预报可以提供概率预测，提高预测值和支持业务效用，并在决议和交货期的替代方法的斗争。
FusionCast: Enhancing Precipitation Nowcasting with Asymmetric Cross-Modal Fusion and Future Radar Priors
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）；School of Earth and Space Science and Technology, Wuhan University（武汉大学地球和空间科学与技术学院）
Biocomplexity Institute and Initiative, University of Virginia（弗吉尼亚大学生物复杂性研究所与计划）；Computer Science Dept., University of Virginia（弗吉尼亚大学计算机科学系）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Santa Fe Institute（圣菲研究所）；Indiana University（印第安纳大学）
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）
Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting
University of Virginia（弗吉尼亚大学）；University of California, Davis（加州大学戴维斯分校）；Santa Fe Institute（圣达菲研究所）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；University of California, Irvine（加州大学欧文分校）
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）
