Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models
Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Technical University Munich（慕尼黑工业大学）；Eberhard Karls Universität Tübingen（蒂宾根大学）；University of Exeter（埃克塞特大学）
Philipp Hess, Markus Drüke, Stefan Petri, Felix M. Strnad, Niklas Boers
Precipitation results from complex processes across many scales, making its accurate simulation in Earth system models (ESMs) challenging. Existing post-processing methods can improve ESM simulations locally, but cannot correct errors in modelled spatial patterns. Here we propose a framework based on physically constrained generative adversarial networks (GANs) to improve local distributions and spatial structure simultaneously. We apply our approach to the computationally efficient ESM CM2Mc-LPJmL. Our method outperforms existing ones in correcting local distributions, and leads to strongly improved spatial patterns especially regarding the intermittency of daily precipitation. Notably, a double-peaked Intertropical Convergence Zone, a common problem in ESMs, is removed. Enforcing a physical constraint to preserve global precipitation sums, the GAN can generalize to future climate scenarios unseen during training. Feature attribution shows that the GAN identifies regions where the ESM exhibits strong biases. Our method constitutes a general framework for correcting ESM variables and enables realistic simulations at a fraction of the computational costs.
Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models
Western Washington University（西华盛顿大学）；Utah State University（犹他州立大学）；Joint Global Change Research Institute（全球变化联合研究所）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；AI and Data Analytics Division, Pacific Northwest National Laboratory（太平洋西北国家实验室数据分析与人工智能分部）
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）
DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models
Utah State University（犹他州立大学）；Joint Global Change Research Institute（全球变化联合研究所）；Pacific Northwest National Laboratory（西北太平洋国家实验室）；Western Washington University（西华盛顿大学）；Indiana University（印第安纳大学）
On the modern deep learning approaches for precipitation downscaling
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）
Generating realistic global precipitation fields from modelled atmospheric circulation
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（TUM工程与设计学院）；Department of Aerospace and Geodesy（航空航天与大地测量系）；Earth System Modelling Group（地球系统建模组）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Global Systems Institute（全球系统研究所）；Department of Mathematics（数学系）；University of Exeter（埃克塞特大学）
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）
Winter Precipitation Type Diagnosis and Uncertainty Quantification with a Physically Consistent Machine Learning Method
NSF National Center for Atmospheric Research（美国国家科学基金会国家大气研究中心）；Department of Computer Science, University of Colorado, Boulder, CO, USA（科罗拉多大学博尔德分校计算机科学系）；Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA（科罗拉多州立大学大气温室联合研究所）；Department of Computer Science, University of Wisconsin-Madison, Madison, WI, USA（威斯康星大学麦迪逊分校计算机科学系）；Agricultural Leadership, Education, and Communication, University of Illinois Urbana-Champaign, Urbana, IL, USA（伊利诺伊大学厄巴纳-香槟分校农业领导力、教育与传播学院）
The impact of electron precipitation on Earth's thermospheric NO production and the drag of LEO satellites
Space Research Institute, Austrian Academy of Sciences（奥地利科学院空间研究所）；Technical University, Graz（格拉茨工业大学）；Institute of Astronomy, Russian Academy of Sciences（俄罗斯科学院天文研究所）；National Center of Physics and Mathematics（国家物理数学中心）；Department of Astrophysics, University of Vienna（维也纳大学天体物理系）；Austrian Space Weather Office, GeoSphere Austria（奥地利空间天气办公室，地球系统奥地利）
PrecipCorrDiff: A Deep Learning Framework for Generating 0.0625$^\circ$ High-Resolution IMD Gridded Precipitation Data
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Government of India（印度热带气象研究所）；Vishwakarma Institute of Technology（维斯瓦卡尔马理工学院）；India Meteorological Department, Ministry of Earth Sciences, Government of India（印度气象局）；NVIDIA Graphics India Pvt. Ltd.（英伟达图形印度私人有限公司）
