Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations. This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.
Northeastern University（东北大学）；risQ Inc.（risQ公司）；Bay Area Environmental Research Institute（湾区环境研究所）；NASA Ames Research Center（NASA艾姆斯研究中心）；University Corporation（大学公司）；NASA Advanced Supercomputing Division（NASA高级超算部门）
The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. Depending on statistical modeling choices, downscaled projections have been shown to vary significantly terms of accuracy and reliability. The spatio-temporal nature…
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…
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 …
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…
Deep Learning Super Resolution for Satellite Cloud Mask Downscaling
BEYOND EO Centre（BEYOND EO中心）；IAASARS, National Observatory of Athens（雅典国家天文台IAASARS研究所）；National and Kapodistrian University of Athens（雅典国立及卡波迪斯特里亚大学）
University of Southern California（南加州大学）；Baidu Research (USA)（百度研究院（美国））
Normalizing flow models have been used successfully for generative image super-resolution (SR) by approximating complex distribution of natural images to simple tractable distribution in latent space through Invertible Neural Networks (INN). These models can generate multiple realistic SR images from one low-resolution (LR) input using randomly sampled points in the latent space, simulating the ill-posed nature of image upscaling where multiple high-resolution (HR) images correspond to the same LR. Lately, the invertible process in INN has also been used successfully by bidirectional image res…
Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）
Super-Resolution is the technique to improve the quality of a low-resolution photo by boosting its plausible resolution. The computer vision community has extensively explored the area of Super-Resolution. However, previous Super-Resolution methods require vast amounts of data for training which becomes problematic in domains where very few low-resolution, high-resolution pairs might be available. One such area is statistical downscaling, where super-resolution is increasingly being used to obtain high-resolution climate information from low-resolution data. Acquiring high-resolution climate d…
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）
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）
Zero-Shot Statistical Downscaling via Diffusion Posterior Sampling
Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China（复旦大学人工智能创新与孵化院）；School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院）；Institution for Big Data, Fudan University, Shanghai, China（复旦大学大数据研究所）；Shanghai Innovation Institute, Shanghai, China（上海创新研究院）；Shanghai Academy of AI for Science, Shanghai, China（上海人工智能科学研究院）
Generative artificial intelligence improves projections of climate extremes
Artificial Intelligence Innovation and Incubation Institute（人工智能创新与孵化院）；Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Institute for Big Data（大数据研究院）；MOE Laboratory for National Development and Intelligent Governance（教育部国家发展与智能治理实验室）；Shanghai Academy of AI for Science（上海人工智能科学研究院）；FuXi Intelligent Computing Technology Co., Ltd.（复旦智能计算技术有限公司）
Normalizing flow models using invertible neural networks (INN) have been widely investigated for successful generative image super-resolution (SR) by learning the transformation between the normal distribution of latent variable $z$ and the conditional distribution of high-resolution (HR) images gave a low-resolution (LR) input. Recently, image rescaling models like IRN utilize the bidirectional nature of INN to push the performance limit of image upscaling by optimizing the downscaling and upscaling steps jointly. While the random sampling of latent variable $z$ is useful in generating divers…
Computer Vision Lab, ETH Zurich（苏黎世联邦理工学院计算机视觉实验室）；KU Leuven（鲁汶大学）
Normalizing flows have recently demonstrated promising results for low-level vision tasks. For image super-resolution (SR), it learns to predict diverse photo-realistic high-resolution (HR) images from the low-resolution (LR) image rather than learning a deterministic mapping. For image rescaling, it achieves high accuracy by jointly modelling the downscaling and upscaling processes. While existing approaches employ specialized techniques for these two tasks, we set out to unify them in a single formulation. In this paper, we propose the hierarchical conditional flow (HCFlow) as a unified fram…
Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature
Department of Mathematics and Statistics, Faculty of Science and Technology, UiT The Arctic University of Norway（乌塔大学科学与技术学院数学与统计学系）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Technical University of Munich（慕尼黑技术大学）；School of Engineering & Design, Earth System Modelling（工程与设计学院，地球系统建模）；Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research（阿尔弗雷德· Wegener 研究院，极地与海洋研究中心）
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）
RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall Downscaling
Institute for Applied Mathematics and Information Technologies, CNR（意大利国家研究委员会应用数学与信息技术研究所）；Institute of Atmospheric Sciences and Climate, CNR（意大利国家研究委员会大气科学与气候研究所）；Politecnico di Torino（都灵理工大学）
