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）
Satellite-derived data products and climate model simulations of geophysical variables like precipitation, often exhibit systematic biases compared to in-situ measurements. Bias correction and spatial downscaling are fundamental components to develop operational weather forecast systems, as they seek to improve the consistency between coarse-resolution climate model simulations or satellite-based estimates and ground-based observations. In recent years, deep learning-based models have been increasingly replaced traditional statistical methods to generate high-resolution, bias free projections of climate variables. For example, Max-Average U-Net (MAUNet) architecture has been demonstrated for its ability to downscale precipitation estimates. The versatility and adaptability of these neural models make them highly effective across a range of applications, though this often come at the cost of high computational and memory requirements. The aim of this research is to develop light-weight neural network architectures for both bias correction and downscaling of precipitation, for which the teacher-student based learning paradigm is explored. This research demonstrates the adaptability of MAUNet to the task of bias correction, and further introduces a compact, lightweight neural network architecture termed MAUNet-Light.The proposed MAUNet-Light model is developed by transferring knowledge from the trained MAUNet, and it is designed to perform both downscaling and bias correction with reduced computational requirements without any significant loss in accuracy compared to state-of-the-art.
Wasserstein GAN-Based Precipitation Downscaling with Optimal Transport for Enhancing Perceptual Realism
Graduate School of Science and Engineering, Chiba University（千叶大学科学技术研究生院）；Center for Environmental Remote Sensing, Chiba University（千叶大学环境遥感中心）；Institute for Advanced Academic Research, Chiba University（千叶大学高级学术研究所）；Research Institute of Disaster Medicine, Chiba University（千叶大学灾害医学研究所）
RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling
IVADO；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；connAIx Research School, Tübingen AI Center, University of Tübingen（蒂宾根大学connAIx研究学院，蒂宾根AI中心）；Cluster of Excellence Machine Learning, University of Tübingen（蒂宾根大学机器学习卓越集群）；Mila - Quebec AI Institute（Mila-魁北克人工智能研究所）
Indian Institute of Tropical Meteorology（印度热带气象研究所）；Indian Institute of Science Education and Research（印度科学教育与研究学院）；Indian Meteorological Department（印度气象局）；CIRES（环境科学合作研究所）；University of Colorado Boulder（科罗拉多大学博尔德分校）；NOAA/Physical Sciences Laboratory（美国国家海洋和大气管理局物理科学实验室）；Center for Atmospheric and Ocean Sciences, Indian Institute of Science（印度科学学院大气与海洋科学中心）
Deep Learning (DL) based downscaling has become a popular tool in earth sciences recently. Increasingly, different DL approaches are being adopted to downscale coarser precipitation data and generate more accurate and reliable estimates at local (~few km or even smaller) scales. Despite several studies adopting dynamical or statistical downscaling of precipitation, the accuracy is limited by the availability of ground truth. A key challenge to gauge the accuracy of such methods is to compare the downscaled data to point-scale observations which are often unavailable at such small scales. In th…
Kumamoto University（熊本大学）；University of California, Davis（加州大学戴维斯分校）；Sun Yat-Sen University（中山大学）
Deep learning has been utilized for the statistical downscaling of climate data. Specifically, a two-dimensional (2D) convolutional neural network (CNN) has been successfully applied to precipitation estimation. This study implements a three-dimensional (3D) CNN to estimate watershed-scale daily precipitation from 3D atmospheric data and compares the results with those for a 2D CNN. The 2D CNN is extended along the time direction (3D-CNN-Time) and the vertical direction (3D-CNN-Vert). The precipitation estimates of these extended CNNs are compared with those of the 2D CNN in terms of the root-…
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 …
Evaluating Cross-region Generalization for Wavelet-Diffusion Precipitation Downscaling
University of Florida（佛罗里达大学）；Stony Brook University（石溪大学）；University of Maryland（马里兰大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
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…
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）
Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa
Laboratoire d’Océanographie et des Sciences de l’Environnement et du Climat (LOSEC)Université Assane Seck de Ziguinchor（西古尔省阿萨内·塞克大学海洋与环境气候学实验室）；Institut Pierre-Simon Laplace (IPSL), Sorbonne Université, IRD（皮埃尔-西蒙·拉普拉斯研究所，索邦大学，法国国家科学研究中心）
Argonne National Laboratory（阿贡国家实验室）；University of Cincinnati（辛辛那提大学）
This study develops a neural network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computational cost. The key idea is to use combination of low- and high- resolution simulations to train a neural network to map from the former to the latter. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and we train each CNN type both with a conventional loss function, such as mean square error (MSE), and with a conditional generative adver…
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）
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）
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）
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）
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…
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）
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 …
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…
A Differentiable Framework for Global Circulation Model Precipitation Bias Correction
Department of Civil and Environmental Engineering, The Pennsylvania State University（宾夕法尼亚州立大学土木与环境工程系）；Computational & Information Systems Laboratory, National Center for Atmospheric Research（国家大气研究中心计算与信息系统实验室）
Interpretable AI-Driven Discovery of Terrain-Precipitation Relationships for Enhanced Climate Insights
Peking University（北京大学）；Eastern Institute of Technology（东方理工大学）；South University of Science and Technology of China（南方科技大学）；China Meteorological Administration（中国气象局）；Chinese Academy of Meteorological Sciences（中国气象科学研究院）；Peng Cheng Laboratory（鹏城实验室）
PCSDiff: Diffusion-Based Bias Correction and Super Resolution Toward Practical Operational Medium-Term Precipitation Forecast
Tsinghua University（清华大学）；Huawei Technologies Co., Ltd（华为技术有限公司）；ETH Zürich（苏黎世联邦理工学院）；Beijing Forestry University（北京林业大学）；National Climate Centre, China Meteorological Administration（中国气象局国家气候中心）；National Institute of Natural Hazards, Ministry of Emergency Management of China（中国应急管理部国家自然灾害防治研究院）
