Recently, deep learning has emerged as a promising tool for statistical downscaling, the set of methods for generating high-resolution climate fields from coarse low-resolution variables. Nevertheless, their ability to generalize to climate change conditions remains questionable, mainly due to the stationarity assumption. We propose deep ensembles as a simple method to improve the uncertainty quantification of statistical downscaling models. By better capturing uncertainty, statistical downscaling models allow for superior planning against extreme weather events, a source of various negative social and economic impacts. Since no observational future data exists, we rely on a pseudo reality experiment to assess the suitability of deep ensembles for quantifying the uncertainty of climate change projections. Deep ensembles allow for a better risk assessment, highly demanded by sectoral applications to tackle climate change.
University of Cambridge（剑桥大学）；British Antarctic Survey（英国南极调查局）；The Alan Turing Institute（艾伦·图灵研究所）
A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep learning techniques to be applied to off-the-grid spatio-temporal data. This model has a substantial advantage over existing downscaling methods in that the trained model can be used to generate multisite predictions at an arbitrary set of locations, regardless of the availability of training data. The convCNP model is shown to outperform an ensemble of existing downscaling …
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Instituto de Física de Cantabria (IFCA), CSIC-UC（坎塔布里亚物理研究所（IFCA），西班牙国家研究委员会-坎塔布里亚大学）；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）；Fraunhofer ITWM（弗劳恩霍夫工业数学研究所）；University of Kaiserslautern（凯泽斯劳滕大学）；McGill University（麦吉尔大学）
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
Mila Quebec AI Institute（米拉魁北克人工智能研究所）；New York University（纽约大学）；University of Montreal（蒙特利尔大学）；Fraunhofer ITWM（弗劳恩霍夫工业数学研究所）；IBM Research（IBM研究院）；McGill University（麦吉尔大学）
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）
Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction
Nanjing University of Information Science and Technology（南京信息工程大学）；Japan Agency for Marine-Earth Science and Technology（日本海洋研究开发机构）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；Tokyo Institute of Technology（东京工业大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所革新智能综合研究中心）
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^{\…
Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model
School of Computing, KAIST（韩国科学技术院计算机学院）；Data Science Group, IBS（基础科学研究院数据科学组）；Department of Climate System, PNU（釜山大学气候系统系）；Center for Climate Physics, IBS（基础科学研究院气候物理中心）
A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. Dynamical downscaling requires running expensive numerical models at high resolution which can be prohibitive due to long model runtimes. On the other hand, statistical downscaling techniques present an alternative approach for learning links between the large- and local-scale climate in a more efficient way. A large number of deep neural network-based approaches for statistical downscaling have been proposed in recent years, mostly based on convolutional architectures devel…
University of Colorado, Boulder（科罗拉多大学博尔德分校）；Jupiter Intelligence Inc.（木星智能公司）
Downscaling is a landmark task in climate science and meteorology in which the goal is to use coarse scale, spatio-temporal data to infer values at finer scales. Statistical downscaling aims to approximate this task using statistical patterns gleaned from an existing dataset of downscaled values, often obtained from observations or physical models. In this work, we investigate the application of deep latent variable learning to the task of statistical downscaling. We present ClimAlign, a novel method for unsupervised, generative downscaling using adaptations of recent work in normalizing flows…
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）
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）
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）
Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and natural systems, including critical infrastructures and ecosystems. While impact assessment and adaptation planning rely on high-resolution projections (typically in the order of a few kilometers), state-of-the-art Earth System Models (ESMs) are available at spatial resolutions of few hundreds of kilometers. Current solutions to obtain high-resolution projections of ESMs include downscaling approaches that consider …
Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Machine Learning
Technical University Munich（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；University of Exeter（埃克塞特大学）
Interpretable Time Series Autoregression for Periodicity Quantification
Department of Urban Studies and Planning, Massachusetts Institute of Technology（城市研究与规划系，麻省理工学院）；Questrom School of Business, Boston University（questrom商学院，波士顿大学）；Department of Mathematics at the University of California, San Diego（加州大学圣地亚哥分校数学系）
We present a simple and effective approach for posterior uncertainty quantification in deep operator networks (DeepONets); an emerging paradigm for supervised learning in function spaces. We adopt a frequentist approach based on randomized prior ensembles, and put forth an efficient vectorized implementation for fast parallel inference on accelerated hardware. Through a collection of representative examples in computational mechanics and climate modeling, we show that the merits of the proposed approach are fourfold. (1) It can provide more robust and accurate predictions when compared against…
MAUNet-Light: A Concise MAUNet Architecture for Bias Correction and Downscaling of Precipitation Estimates
Department of Artificial Intelligence, Indian Institute of Technology Kharagpur（人工智能系，印度理工学院科丁格浦分校）；Sustainability and Data Sciences Laboratory (SDS Lab), Civil and Environmental Engineering, Northeastern University（可持续性与数据科学实验室（SDS实验室），土木与环境工程，东北大学）；AI for Climate and Sustainability (AI4CaS), The Institute for Experiential AI (IEAI), Northeastern University（人工智能用于气候与可持续性（AI4CaS），体验式人工智能研究所（IEAI），东北大学）
Instituto de Física de Cantabria (IFCA)（坎塔布里亚物理研究所（IFCA））；CSIC（西班牙国家研究委员会）；Universidad de Cantabria（坎塔布里亚大学）
Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional-to-local scales from large-scale atmospheric fields following the perfect-prognosis (PP) approach. Given their complexity, it is crucial to properly evaluate these methods, especially when applied to changing climatic conditions where the ability to extrapolate/generalise is key. In this work, we intercompare several DL models extracted from the literature for the same challenging use-case (downscaling temperature in the CORDEX North America domain) and expand standard evaluation methods building on…
Investigating the Robustness of Extreme Precipitation Super-Resolution Across Climates
Expertise Center for Climate Extremes, University of Lausanne（极端气候专家中心，洛桑大学）；Faculty of Geosciences and Environment, University of Lausanne（地球科学与环境学院，洛桑大学）；Faculty of Business and Economics (HEC), University of Lausanne（商业经济学院（HEC），洛桑大学）
Argonne National Laboratory（阿贡国家实验室）；Virginia Tech（弗吉尼亚理工学院）
Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over relatively small downscaling factors because of the challenge of recovering fine-scale information from coarse data. This limits their compatibility with many global climate model outputs, often available between $\sim$50--100 km resolution, to scales of interest such as cloud resolving or urban scales. This study systematically examines the capability of convolutional neural networks (CNNs) to downscale surface wind s…
