Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change. In this context, resilience planning becomes crucial for risk mitigation and coping with these extreme events. Machine learning techniques can play a critical role in resilience planning through the generation of realistic extreme weather event scenarios that can be used to evaluate possible mitigation actions. This paper proposes a modular framework that relies on interchangeable components to produce extreme weather event scenarios. We discuss possible alternatives for each of the components and show initial results comparing two approaches on the task of generating precipitation scenarios.
ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）；The Hong Kong University of Science and Technology（香港科技大学）
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change
National University of Singapore（新加坡国立大学）；Brown University（布朗大学）；NVIDIA AI Technology Centre（英伟达人工智能技术中心）；Centre for Climate Research Singapore（新加坡气候研究中心）
ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics
College of Design and Engineering, National University of Singapore（新加坡国立大学设计与工程学院）；Department of Mathematics, National University of Singapore（新加坡国立大学数学系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Technical University of Munich（慕尼黑工业大学）；Artisense；Audi Electronics Venture（奥迪电子创投）
Even though end-to-end supervised learning has shown promising results for sensorimotor control of self-driving cars, its performance is greatly affected by the weather conditions under which it was trained, showing poor generalization to unseen conditions. In this paper, we show how knowledge can be transferred using semantic maps to new weather conditions without the need to obtain new ground truth data. To this end, we propose to divide the task of vehicle control into two independent modules: a control module which is only trained on one weather condition for which labeled steering data is…
University of Warwick（华威大学）；TUM（慕尼黑工业大学）；LSE（伦敦政治经济学院）；DLR（德国航空航天中心）
Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots or assume 1D time-series; few are able to capture spatio-temporal processes simultaneously. Beyond this, Earth-systems data often exhibit highly irregular and complex patterns, for example caused by extreme weather events. Because of climate change, these phenomena are only increasing in frequency. Here, we proposed a novel GAN-based approach for generating spatio-temporal weather patterns conditioned on detected ext…
TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling
Beihang University（北京航空航天大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）
University of California Davis（加州大学戴维斯分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Intel Corporation（英特尔公司）
Extreme weather is one of the main mechanisms through which climate change will directly impact human society. Coping with such change as a global community requires markedly improved understanding of how global warming drives extreme weather events. While alternative climate scenarios can be simulated using sophisticated models, identifying extreme weather events in these simulations requires automation due to the vast amounts of complex high-dimensional data produced. Atmospheric dynamics, and hydrodynamic flows more generally, are highly structured and largely organize around a lower dimens…
Numerical weather prediction (NWP) models require ever-growing computing time/resources, but still, have difficulties with predicting weather extremes. Here we introduce a data-driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern-recognition technique (capsule neural networks, CapsNets) and impact-based auto-labeling strategy. CapsNets are trained on mid-tropospheric large-scale circulation patterns (Z500) labeled $0-4$ depending on the existence and geographical region of surface temperature extremes over North…
Verisk Analytics（韦里斯克分析公司）；Otto-von-Guericke Universität（奥托·冯·格里克大学）；Massachusetts Institute of Technology（麻省理工学院）
Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year. Under climate change, their impact and intensity are expected to worsen significantly. Unfortunately, general circulation models (GCMs), which are currently the primary tool for climate projections, cannot characterize weather extremes accurately. To address this, we present a multi-resolution deep-learning framework that, firstly, corrects a GCM's biases by matching low-order and tail statistics of its output with observations at coarse scales; and secon…
Extreme Weather Bench: A framework and benchmark for evaluation of high-impact weather
Brightband；School of Meteorology and School of Computer Science（气象学系和计算机科学系）；University of Oklahoma（俄克拉荷马大学）；Bureau of Meteorology（气象局）；National Severe Storms Laboratory（国家严重风暴实验室）；National Oceanic and Atmospheric Administration（国家海洋和大气管理局）；The Weather Company（天气公司）；European Centre for Medium Range Weather Forecasting（欧洲中期天气预报中心）；Department of Statistics（统计学系）；Colorado State University（科罗拉多州立大学）；Earth and Atmospheric Sciences（地球与大气科学系）；Central Michigan University（中央密歇根大学）
Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
1Dept. of Computer Science, Midwestern State University, Dallas, TX, USA 2O\&M Analytics, AES Clean Energy, The AES Corporation, Louisville, CO, USA 3Dept. of Information；Communication Engineering, Pabna University of Science
University of Liverpool（利物浦大学）；Beijing Normal University（北京师范大学）
Given an increasingly volatile climate, the relationship between weather and transit ridership has drawn increasing interest. However, challenges stemming from spatio-temporal dependency and non-stationarity have not been fully addressed in modelling and predicting transit ridership under the influence of weather conditions especially with the traditional statistical approaches. Drawing on three-month smart card data in Brisbane, Australia, this research adopts and assesses a suite of machine-learning algorithms, i.e., random forest, eXtreme Gradient Boosting (XGBoost) and Tweedie XGBoost, to …
RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges
The Hong Kong University of Science and Technology（香港科学与技术大学）；Nanjing University（南京大学）；Southern University of Science and Technology（南方科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai TechWind Technology Co., Ltd.（上海技风科技有限公司）
On the Predictive Skill of Artificial Intelligence-based Weather Models for Extreme Events using Uncertainty Quantification
Applied Machine Learning Group（应用机器学习组）；Fraunhofer Heinrich-Hertz Institute（弗劳恩霍夫海因里希-赫兹研究所）；Signal Theory and Communications Dept.（信号理论与通信系）；Universidad Carlos III de Madrid (UC3M)（马德里卡洛斯三世大学）
Forecasting severe weather conditions is still a very challenging and computationally expensive task due to the enormous amount of data and the complexity of the underlying physics. Machine learning approaches and especially deep learning have however shown huge improvements in many research areas dealing with large datasets in recent years. In this work, we tackle one specific sub-problem of weather forecasting, namely the prediction of thunderstorms and lightning. We propose the use of a convolutional neural network architecture inspired by UNet++ and ResNet to predict thunderstorms as a bin…
Can AI weather models predict out-of-distribution gray swan tropical cyclones?
University of Chicago, Department of the Geophysical Sciences（芝加哥大学地质科学系）；University of Chicago, Committee on Computational and Applied Mathematics（芝加哥大学计算与应用数学委员会）；University of Chicago, Research Computing Center（芝加哥大学研究计算中心）；University of California, Department of Applied Mathematics（加州大学应用数学系）；New York University, Courant Institute of Mathematical Sciences（纽约大学Courant数学科学研究所）
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy due to high computational cost and strict time-to-solution limits. We report that a data-driven deep learning Earth system emulator, FourCastNet, can predict global weather and generate medium-range forecasts five orders-of-magnitude faster than NWP while approaching state-of-the-art accuracy. FourCast-Net is optimized and scales efficiently on three supercomputing systems: Selene, Perlmutter, and JUWELS Bo…
Google Research（谷歌研究院）；California Institute of Technology（加州理工学院）；University of Oklahoma（俄克拉荷马大学）
Heat waves are projected to increase in frequency and severity with global warming. Improved warning systems would help reduce the associated loss of lives, wildfires, power disruptions, and reduction in crop yields. In this work, we explore the potential for deep learning systems trained on historical data to forecast extreme heat on short, medium and subseasonal timescales. To this purpose, we train a set of neural weather models (NWMs) with convolutional architectures to forecast surface temperature anomalies globally, 1 to 28 days ahead, at $\sim200~\mathrm{km}$ resolution and on the cubed…
Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
Research Institute for Statistics and Information Science, University of Geneva（日内瓦大学统计与信息科学研究所）；Helmholtz Centre for Environmental Research – UFZ（亥姆霍兹环境研究中心（UFZ））；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据分析与人工智能中心（ScaDS.AI））；TUD Dresden University of Technology（德累斯顿工业大学）
UniGIO: Unified Generative Global In-situ Weather Modeling from Spatiotemporal Incomplete Observations
Beihang University（北京航空航天大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）
AI Models Still Lag Behind Traditional Numerical Models in Predicting Sudden-Turning Typhoons
Ocean University of China（中国海洋大学）；National University of Defense Technology（国防科技大学）；Guangdong Meteorological Observatory（广东省气象台）；Lanzhou University（兰州大学）；National Meteorological Centre, China Meteorological Administration（中国气象局国家气象中心）；Institute of Tibetan Plateau Research, Chinese Academy of Sciences（中国科学院青藏高原研究所）
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organization
Pacific Northwest National Laboratory（太平洋西北国家实验室）；Center For Nonlinear Studies, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室非线性研究中心）；NVIDIA Corporation（英伟达公司）；NERSC, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室国家能源研究科学计算中心）；Intel Corporation（英特尔公司）；Complexity Sciences Center, Department of Physics and Astronomy, University of California Davis（加州大学戴维斯分校物理与天文系复杂性科学中心）
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or trajectories, run in parallel. These systems are associated with a high computational cost and often involve statistical post-processing steps to inexpensively improve their raw prediction qualities. We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. These enab…
Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
Lawrence Berkeley National Laboratory (LBNL)（劳伦斯伯克利国家实验室）；University of California, Berkeley（加州大学伯克利分校）；NVIDIA Corporation（英伟达公司）；Indiana University（印第安纳大学）；National Energy Research Scientific Computing Center (NERSC)（国家能源研究科学计算中心）；University of California, Irvine（加州大学欧文分校）
In this paper, we predict severity of extreme weather events (tropical storms, hurricanes, etc.) using buoy data time series variables such as wind speed and air temperature. The prediction/forecasting method is based on various forecasting and machine learning models. The following steps are used. Data sources for the buoys and weather events are identified, aggregated and merged. For missing data imputation, we use Kalman filters as well as splines for multivariate time series. Then, statistical tests are run to ascertain increasing trends in weather event severity. Next, we use machine lear…
One key requirement for effective supply chain management is the quality of its inventory management. Various inventory management methods are typically employed for different types of products based on their demand patterns, product attributes, and supply network. In this paper, our goal is to develop robust demand prediction methods for weather sensitive products at retail stores. We employ historical datasets from Walmart, whose customers and markets are often exposed to extreme weather events which can have a huge impact on sales regarding the affected stores and products. We want to accur…
OneForecast: A Universal Framework for Global and Regional Weather Forecasting
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University（地球系统科学系，地球系统建模教育部重点实验室，全球变化研究院，清华大学）；TEG, Tencent（腾讯科技集团）；Department and Computer and Science, University of Science and Technology of China（计算机与科学系，中国科学技术大学）；Institute for Interdisciplinary Information Sciences, Tsinghua University（交叉信息研究院，清华大学）；Department of Computer Science and Engineering, The HongKong University of Science and Technology（计算机科学与工程系，香港科学理工大学）；AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（人工智能方向，香港科学理工大学（广州））；School of Computer Science and Engineering, Nanyang Technological University（计算机科学与工程学院，南洋理工大学）；Department of Computing, The Hong Kong Polytechnic University（计算系，香港理工大学）
