Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model
Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）；Shanghai Normal University（上海师范大学）；Jiangsu Meteorological Observatory（江苏省气象台）；Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Nanjing Meteorological Observatory（南京市气象台）
The rapid advancement of artificial intelligence (AI) in weather research has been driven by the ability to learn from large, high-dimensional datasets. However, this progress also poses significant challenges, particularly regarding the substantial costs associated with processing extensive data and the limitations of computational resources. Inspired by the Neural Image Compression (NIC) task in computer vision, this study seeks to compress weather data to address these challenges and enhance the efficiency of downstream applications. Specifically, we propose a variational autoencoder (VAE) framework tailored for compressing high-resolution datasets, specifically the High Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) with a spatial resolution of 1 km. Our framework successfully reduced the storage size of 3 years of HRCLDAS data from 8.61 TB to just 204 GB, while preserving essential information. In addition, we demonstrated the utility of the compressed data through a downscaling task, where the model trained on the compressed dataset achieved accuracy comparable to that of the model trained on the original data. These results highlight the effectiveness and potential of the compressed data for future weather research.
China Regional 3km Downscaling Based on Residual Corrective Diffusion Model
State Key Laboratory of Severe Weather Meteorological Science and Technology（severe weather meteorological science and technology state key laboratory）；CMA Earth System Modeling and Prediction Centre；NVIDIA；Tsinghua University
University of Oxford（牛津大学）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）
Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, i.e., learning to add fine-scale structure to coarse images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of reconstruct…
Conditional diffusion models for downscaling and bias correction of Earth system model precipitation
Technical University of Munich（慕尼黑技术大学）；Munich Climate Center（慕尼黑气候中心）；TUM School of Engineering and Design（慕尼黑技术大学工程与设计学院）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Institute of Atmospheric Physics（大气物理研究所）；University of Exeter（埃克塞特大学）
École polytechnique fédérale de Lausanne（洛桑联邦理工学院）；Federal Office of Meteorology and Climatology MeteoSwiss（联邦气象和气候学办公室 MeteoSwiss）
Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling" in the atmospheric sciences: improving the spatial resolution of low-resolution images. The ability of conditional GANs to generate an ensemble of solutions for a given input lends itself naturally to stochastic downscaling, but the stochastic nature of GANs is not usually considered in super-resolution applications. Here, we introduce a recurrent, stochastic super-resolution GAN that can generate ensembles of time-evolving high-resoluti…
Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution
Fudan University（复旦大学）；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）；Beihang University（北京航空航天大学）；Columbia University（哥伦比亚大学）；Nanjing University of Information Science and Technology（南京信息工程大学）
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）
Modeling the risk of extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Although the available low-resolution climate models capture different scenarios, accurate risk assessment for mitigation and adaption often demands detail that they typically cannot resolve. Here, we develop a dynamic data-driven downscaling (super-resolution) method that incorporates physics and statistics in a generative framework to learn the fine-scale spatial details of rainfall. Our method transforms coarse-resolution ($0.25^{\circ} \times 0.25^{\…
Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion
Department of Electrical and Computer Engineering, Rice University（电气与计算机工程系， Rice大学）；Department of Civil and Environmental Engineering, Rice University（土木与环境工程系， Rice大学）；Ken Kennedy Institute, Rice University（肯尼迪研究所， Rice大学）；Department of Computer Science, Rice University（计算机科学系， Rice大学）
Generative 3D Gaussian Splatting for Arbitrary-ResolutionAtmospheric Downscaling and Forecasting
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）；School of Computer and Information Sciences, University of Newcastle（纽卡斯尔大学计算机与信息科学学院）；School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学学院（iOPEN））
Jackson School of Geosciences（杰克逊地球科学学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；CIRES, University of Colorado Boulder（科罗拉多大学博尔德分校环境科学研究合作研究所）；NOAA/Physical Sciences Laboratory（美国国家海洋和大气管理局物理科学实验室）；Purdue University（普渡大学）；Cockrell School of Engineering（科克雷尔工程学院）
Urban downscaling is a link to transfer the knowledge from coarser climate information to city scale assessments. These high-resolution assessments need multiyear climatology of past data and future projections, which are complex and computationally expensive to generate using traditional numerical weather prediction models. The city of Austin, Texas, USA has seen tremendous growth in the past decade. Systematic planning for the future requires the availability of fine resolution city-scale datasets. In this study, we demonstrate a novel approach generating a general purpose operator using dee…
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）
University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；ClimateAI
Accurately forecasting extreme rainfall is notoriously difficult, but is also ever more crucial for society as climate change increases the frequency of such extremes. Global numerical weather prediction models often fail to capture extremes, and are produced at too low a resolution to be actionable, while regional, high-resolution models are hugely expensive both in computation and labour. In this paper we explore the use of deep generative models to simultaneously correct and downscale (super-resolve) global ensemble forecasts over the Continental US. Specifically, using fine-grained radar o…
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）
Deep learning, particularly convolutional neural networks for image recognition, has been recently used in meteorology. One of the promising applications is developing a statistical surrogate model that converts the output images of low-resolution dynamic models to high-resolution images. Our study exhibits a preliminary experiment that evaluates the performance of a model that downscales synoptic temperature fields to mesoscale temperature fields every 6 hours. The deep learning model was trained with operational 22-km gridded global analysis surface winds and temperatures as the input, opera…
High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator
Information Sciences and Technology, The Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术系）；Department of Applied Mathematics, University of California, Santa Cruz（加州大学圣克鲁兹分校应用数学系）；Allen Institute for Artificial Intelligence (AI2)（人工智能研究所）；School of Mechanical Engineering, Purdue University（普渡大学机械工程学院）
HRRRCast: a data-driven emulator for regional weather forecasting at convection allowing scales
Cooperative Institute for Research in Environmental Sciences（环境科学研究院）；Cooperative Institute for Research in the Atmosphere（大气研究院）；Cooperative Institute for Severe and High-Impact Weather Research and Operations（严重和高影响天气研究与运作研究院）；NOAA Global Systems Laboratory（国家海洋和大气管理局全球系统实验室）；NOAA Physical Sciences Laboratory（国家海洋和大气管理局物理科学实验室）；NOAA National Severe Storms Laboratory（国家海洋和大气管理局严重风暴实验室）
DIRESA, a distance-preserving nonlinear dimension reduction technique based on regularized autoencoders
Department of Electronics and Informatics, Vrije Universiteit Brussel（比利时布鲁塞尔自由大学电子与信息学院）；Observations Scientific Service, Royal Meteorological Institute（皇家气象研究所观测科学部门）
