Extreme precipitation wreaks havoc throughout the world, causing billions of dollars in damage and uprooting communities, ecosystems, and economies. Accurate extreme precipitation prediction allows more time for preparation and disaster risk management for such extreme events. In this paper, we focus on short-term extreme precipitation forecasting (up to a 12-hour ahead-of-time prediction) from a sequence of sea level pressure and zonal wind anomalies. Although existing machine learning approaches have shown promising results, the associated model and climate uncertainties may reduce their reliability. To address this issue, we propose a self-attention augmented convolution mechanism for extreme precipitation forecasting, systematically combining attention scores with traditional convolutions to enrich feature data and reduce the expected errors of the results. The proposed network architecture is further fused with a highway neural network layer to gain the benefits of unimpeded information flow across several layers. Our experimental results show that the framework outperforms classical convolutional models by 12%. The proposed method increases machine learning as a tool for gaining insights into the physical causes of changing extremes, lowering uncertainty in future forecasts.
Research Institute for Infrastructure Paradigm Shift, Yachiyo Engineering Co., Ltd.（基础设施范式转换研究所，八千代工程株式会社）
最近，由于气候变化引起的前所未有的天气条件，洪水灾害已成为一个社会问题。对暴雨立即作出反应对于减轻经济损失和迅速恢复都很重要。时空降水预报可以提高大坝入库流量预测的准确性，提前6小时以上减轻洪水损失。然而，由于目标预测值与地面实况值之间存在不可约的偏差，普通的ConvLSTM在实际降水预报中存在着可预报范围大于3个时间步的局限性。本文提出了一种雨码的时空降水代码到代码的预测方法。我们提出了一种新的雨的功能，表示一个时间雨的过程，使用多帧融合的时间步长减少。我们基于标准ConvLSTM对各种术语范围进行了雨码研究。我们应用了一个坝区内的日本雨季小时降水数据，2006年至2019年下约12.7万小时，每年5月至10月。我们将雷达分析每小时的数据应用于面积为136 x 148 km 2的中央更广泛的区域。最后，我们在几个预报范围内提供了雨码大小和每小时准确度之间的敏感性研究。
Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers
Department of Science and Engineering, Sorbonne University, Abu Dhabi, United Arab Emirates（Sorbonne大学科学与工程系）；Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India（印度统计研究院应用数学与物理单元）；Sorbonne Center for Artificial Intelligence, Sorbonne University, Paris, 75005, France（Sorbonne大学人工智能中心）；Indian Institute of Technology Gandhinagar, Gandhinagar 382355, India（印度理工学院冈丁加尔分校）；National Centre for Polar and Ocean Research, Ministry of Earth Sciences, Vasco da Gama, 403804, India（国家极地与海洋研究中心）；Center for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Gachibowli, Hyderabad-500032, India（国际信息技术研究所计算自然科学与生物信息中心）；Department of Dynamics, Lodz University of Technology, Stefanowskiego 1/15, 90–537 Lodz, Poland（Lodz技术大学动力学系）；Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata 700032, India（贾瓦德普大学数学生物学与生态中心）
Hybrid Scheme of Kinematic Analysis and Lagrangian Koopman Operator Analysis for Short-term Precipitation Forecasting
Graduate School of Engineering（工学研究生院）；University of Yamanashi（山梨大学）；Department of Civil and Environmental Engineering（土木与环境工程系）；National Research Institute for Earth Science and Disaster Resilience (NIED)（日本国立地球科学与防灾研究所（NIED））
Diffusion Precipitation Generation with Medium-range Weather Forecasting
Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of S & T（香港科技大学）；Sun Yat-Sen University（中山大学）；NSCC-SZ（国家超级计算深圳中心）；China Meteorological Administration（中国气象局）；GBA Weather Research Center（粤港澳大湾区天气研究中心）；Huawei Technologies Co., Ltd（华为技术有限公司）
Advanced Torrential Loss Function for Precipitation Forecasting
AI/ML Innovation Research Center, Chung-Ang University, Seoul 06974, Republic of Korea（AI/ML创新研究中心， Chung-Ang大学）；Advanced Radar Research Center, University of Oklahoma, Norman, OK 73019, USA（先进雷达研究中心，俄克拉荷马大学）；School of Meteorology, University of Oklahoma, Norman, OK 73072, USA（气象学院，俄克拉荷马大学）；Weather Radar Center, Korea Meteorological Administration, Seoul 07062, Republic of Korea（天气雷达中心，韩国气象厅）；Department of Artificial Intelligence, Chung-Ang University, Seoul 06974, Republic of Korea（人工智能系，Chung-Ang大学）
Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations
National Meteorological Center of China Meteorological Administration（中国气象局国家气象中心）；Xiong’an Institute of Meteorological Artificial Intelligence（雄安气象人工智能研究所）；China Meteorological Administration Training Center（中国气象局培训中心）
Adaptive Bias Correction for Improved Subseasonal Forecasting
University of Toronto（多伦多大学）；Instituto de Matemática Pura e Aplicada（纯数学与应用数学研究所）；nLine Inc.（nLine公司）；Massachusetts Institute of Technology（麻省理工学院）；Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）；Atmospheric and Environmental Research（大气与环境研究公司）；Cornell University（康奈尔大学）；Microsoft Research New England（微软研究院新英格兰分部）
Towards Location-Specific Precipitation Projections Using Deep Neural Networks
Indian Institute of Tropical Meteorology（印度热带气象研究所）；National Institute of Technology Rourkela（鲁尔克拉国家理工学院）；Indian Institute of Science Education and Research, Pune（浦那印度科学教育与研究学院）；India Meteorology Department（印度气象部门）
PCSDiff: Diffusion-Based Bias Correction and Super Resolution Toward Practical Operational Medium-Term Precipitation Forecast
Tsinghua University（清华大学）；Huawei Technologies Co., Ltd（华为技术有限公司）；ETH Zürich（苏黎世联邦理工学院）；Beijing Forestry University（北京林业大学）；National Climate Centre, China Meteorological Administration（中国气象局国家气候中心）；National Institute of Natural Hazards, Ministry of Emergency Management of China（中国应急管理部国家自然灾害防治研究院）
Diffusion-Based Rollouts as a Stabilization Mechanism for Long-Horizon Environmental Forecasting
University of California Irvine（加州大学尔湾分校）；San Diego State University（圣地亚哥州立大学）；University of Oklahoma（俄克拉荷马大学）；NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography（NSF可信AI天气、气候与海岸海洋研究所）
Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting
Global spatio-temporal downscaling of ERA5 precipitation through generative AI
Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU)（气象与气候研究所——大气环境研究（IMK-IFU））；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Institute of Meteorology and Climate Research - Atmospheric Trace Gases and Remote Sensing (IMK-ASF)（气象与气候研究所——大气痕量气体与遥感（IMK-ASF））；University of Augsburg（奥格斯堡大学）
Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables
Pontifical Catholic University of Parana (PUCPR)（巴拉那天主教大学）；Federal Technological University of Parana (UTFPR)（巴拉那联邦理工大学）；Federal University of Parana (UFPR)（巴拉那联邦大学）
RainBalance: Alleviating Dual Imbalance in GNSS-based Precipitation Nowcasting via Continuous Probability Modeling
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）；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）；Wuhan University（武汉大学）；School of Water Resources and Hydropower Engineering, Wuhan University（武汉大学水利水电工程学院）；GNSS Research Center, Wuhan University（武汉大学GNSS研究中心）
