The advent of data-driven weather forecasting models, which learn from hundreds of terabytes (TB) of reanalysis data, has significantly advanced forecasting capabilities. However, the substantial costs associated with data storage and transmission present a major challenge for data providers and users, affecting resource-constrained researchers and limiting their accessibility to participate in AI-based meteorological research. To mitigate this issue, we introduce an efficient neural codec, the Variational Autoencoder Transformer (VAEformer), for extreme compression of climate data to significantly reduce data storage cost, making AI-based meteorological research portable to researchers. Our approach diverges from recent complex neural codecs by utilizing a low-complexity Auto-Encoder transformer. This encoder produces a quantized latent representation through variance inference, which reparameterizes the latent space as a Gaussian distribution. This method improves the estimation of distributions for cross-entropy coding. Extensive experiments demonstrate that our VAEformer outperforms existing state-of-the-art compression methods in the context of climate data. By applying our VAEformer, we compressed the most popular ERA5 climate dataset (226 TB) into a new dataset, CRA5 (0.7 TB). This translates to a compression ratio of over 300 while retaining the dataset's utility for accurate scientific analysis. Further, downstream experiments show that global weather forecasting models trained on the compact CRA5 dataset achieve forecasting accuracy comparable to the model trained on the original dataset. Code, the CRA5 dataset, and the pre-trained model are available at https://github.com/taohan10200/CRA5.
National Energy Research Scientific Computing Center（国家能源研究科学计算中心）；Lawrence Berkeley Lab（劳伦斯伯克利实验室）；Nervana Systems（Nervana系统公司）；Scripps Institution of Oceanography（斯克里普斯海洋研究所）；National Oceanic and Atmospheric Administration（美国国家海洋和大气管理局）
Detecting extreme events in large datasets is a major challenge in climate science research. Current algorithms for extreme event detection are build upon human expertise in defining events based on subjective thresholds of relevant physical variables. Often, multiple competing methods produce vastly different results on the same dataset. Accurate characterization of extreme events in climate simulations and observational data archives is critical for understanding the trends and potential impacts of such events in a climate change content. This study presents the first application of Deep Lea…
MILA, Université de Montréal（蒙特利尔大学 MILA）；Lawrence Berkeley National Lab（劳伦斯伯克利国家实验室）；École Polytechnique de Montréal（蒙特利尔理工学院）；Microsoft Maluuba（微软 Maluuba）
Then detection and identification of extreme weather events in large-scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme weather events when large amounts of labeled data are available. However, many different types of spatially localized climate patterns are of interest including hurricanes, extra-tropical cyclones, weathe…
WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks
The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；Stanford University（斯坦福大学）；Colorado State University（科罗拉多州立大学）；NASA Marshall Space Flight Center（美国国家航空航天局马歇尔太空飞行中心）
Max-Planck-Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；University of Jena（耶拿大学）；German Aerospace Center (DLR)（德国航空航天中心）
Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (> 1 km). For more targeted mitigation and adaptation, modelling impacts to < 100 m is needed. Yet, the relationship between driving variables and Earth's surface at such local scales remains unresolved by current physical models. Large Earth observation datasets now enable us to create machine learning models capable of translating coarse weather information into high-resolution Earth surface forecasts. Here, we defi…
Generative assimilation and prediction for weather and climate
Chinese Academy of Sciences（中国科学院）；Nanjing University（南京大学）；Technical University of Munich（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Chinese Academy of Meteorological Sciences（中国气象科学研究院）；National University of Defense Technology（国防科技大学）；Peking University（北京大学）；National Climate Center（国家气候中心）；Xiong’an Institute of Meteorological Artificial Intelligence（雄安气象人工智能研究院）
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）
Neural General Circulation Models for Weather and Climate
Google Research（谷歌研究院）；Massachusetts Institute of Technology（麻省理工学院）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）；Google DeepMind（谷歌DeepMind）；Harvard University（哈佛大学）
Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements (including natural disasters, climate and weather conditions) and stock prices, using historical stock market data, historical climate data, and various climate indicators such as carbon dioxide emissions. We have conducted our study based on the US financial market, global climate trends, and daily weather records to demonstrate a sig…
Rice University（莱斯大学）；University of Pennsylvania（宾夕法尼亚大学）
Convolutional neural networks (CNNs) can potentially provide powerful tools for classifying and identifying patterns in climate and environmental data. However, because of the inherent complexities of such data, which are often spatio-temporal, chaotic, and non-stationary, the CNN algorithms must be designed/evaluated for each specific dataset and application. Yet to start, CNN, a supervised technique, requires a large labeled dataset. Labeling demands (human) expert time, which combined with the limited number of relevant examples in this area, can discourage using CNNs for new problems. To a…
TU Darmstadt（达姆施塔特工业大学）；Mila（米拉人工智能研究所）；Université de Montréal（蒙特利尔大学）；Environment and Climate Change Canada（加拿大环境与气候变化部）；McGill University（麦吉尔大学）
Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in replacing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time. Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive. This has made them a popular target for neural network-based emulators. However, prior work is hard to compare due to the lack of a comprehensive dataset and standardized best practices for ML benchmarking. To …
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…
Augmenting The Weather: A Hybrid Counterfactual-SMOTE Algorithm for Improving Crop Growth Prediction When Climate Changes
College of Computer & Cyber Sciences, University of Prince Mugrin（穆格林王子大学计算机与网络科学学院）；School of Computer Science, University College Dublin（都柏林大学学院计算机科学学院）；Insight Centre for Data Analytics, University College Dublin（都柏林大学学院洞察数据分析中心）；VistaMilk SFI Research Centre, University College Dublin（都柏林大学学院VistaMilk SFI研究中心）
Interpretable Machine Learning for Weather and Climate Prediction: A Survey
Public Meteorological Service Center, China Meteorological Administration（中国气象局公共气象服务中心）；University of Bristol（布里斯托大学）；Zhejiang University（浙江大学）；Institute of Atmospheric Physics, Chinese Academy of Science（中国科学院大气物理研究所）；Baidu Inc.（百度公司）；New Jersey Institute of Technology（新泽西理工学院）
Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) and transfer learning to forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. We apply state-of-the-art interpretation techniques from the machine learning literature to attribute particular regions of int…
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…
FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）；School of Mathematical Sciences, Lancaster University（兰卡斯特大学数学科学学院）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）；Met Office Hadley Centre（英国气象局哈德利中心）
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…
Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）；School of Mathematical Sciences, Lancaster University（兰卡斯特大学数学科学学院）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）；Met Office Hadley Centre（英国气象局哈德利中心）
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Amazon Sustainability Science and Innovation（亚马逊可持续性科学与创新部门）
Learning-based agricultural management in partially observable environments subject to climate variability
Department of Mechanical Engineering, Iowa Technology Institute, University of Iowa（机械工程系、爱荷华技术研究所、爱荷华大学）；Department of Chemical and Biochemical Engineering, Iowa Technology Institute, University of Iowa（化学与生物化学工程系、爱荷华技术研究所、爱荷华大学）
University of Hawaii Manoa（夏威夷大学马诺阿分校）；Nimbus AI LLC（Nimbus人工智能有限责任公司）
Forecasting future weather and climate is inherently difficult. Machine learning offers new approaches to increase the accuracy and computational efficiency of forecasts, but current methods are unable to accurately model uncertainty in high-dimensional predictions. Score-based diffusion models offer a new approach to modeling probability distributions over many dependent variables, and in this work, we demonstrate how they provide probabilistic forecasts of weather and climate variables at unprecedented resolution, speed, and accuracy. We apply the technique to day-ahead solar irradiance fore…
Artificial intelligence pathways from weather to climate
University of California, Los Angeles（加州大学洛杉矶分校）；Hong Kong University of Science and Technology（香港科技大学）；University of Lausanne（洛桑大学）；Allen Institute for AI（艾伦人工智能研究所）；University of Colorado Boulder（科罗拉多大学博尔德分校）；California Institute of Technology（加州理工学院）；NOAA/Geophysical Fluid Dynamics Laboratory（NOAA地球物理流体动力学实验室）；Google Research（谷歌研究院）；New York University（纽约大学）
The advent of digital computing in the 1950s sparked a revolution in the science of weather and climate. Meteorology, long based on extrapolating patterns in space and time, gave way to computational methods in a decade of advances in numerical weather forecasting. Those same methods also gave rise to computational climate science, studying the behaviour of those same numerical equations over intervals much longer than weather events, and changes in external boundary conditions. Several subsequent decades of exponential growth in computational power have brought us to the present day, where mo…
Prithvi WxC: Foundation Model for Weather and Climate
NASA Marshall Space Flight Center（NASA马歇尔太空飞行中心）；IBM Research（IBM研究院）；Earth System Science Center, The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校地球系统科学中心）；Department of Earth System Science, Stanford University（斯坦福大学地球系统科学系）；Department of Atmospheric Science, Colorado State University（科罗拉多州立大学大气科学系）；National Center for Computational Sciences / Oak Ridge National Laboratory（橡树岭国家实验室国家计算科学中心）；NASA/Goddard Space Flight Center（NASA戈达德太空飞行中心）；NASA Headquarters（NASA总部）
ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling
Oak Ridge National Laboratory（奥克岭国家实验室）；AMD Research and Advanced Development（AMD研究与高级开发）；RIKEN Center for Computational Science（理化学研究所计算科学中心）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Hard-Constrained Deep Learning for Climate Downscaling
Fraunhofer ITWM（弗劳恩霍夫信息技术与数学研究所）；Mila Quebec AI Institute（米拉魁北克人工智能研究所）；University of Montreal（蒙特利尔大学）；New York University（纽约大学）；IBM Research（IBM研究院）；McGill University（麦吉尔大学）
Constructing Extreme Heatwave Storylines with Differentiable Climate Models
Centre Étude et simulation du climat à l’échelle régionale (ESCER), Département des Sciences de la Terre et de l’Atmosphère, Université du Québec à Montréal（区域气候研究与模拟中心（ESCER）、地球科学与大气科学系、魁北克蒙特利尔大学）
Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models if trained on observations can mitigate certain biases in current state-of-the-art weather models, some of which stem from inaccurate representation of subgrid-scale processes. However, these data-driven models, being over-parameterized, require a lot of training data which may not be available from reanalysis (observational data) products. Moreover, an accurate, noise-free, initial condition to start forecasting with a data-driven weather model …
