Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition
University of Oxford（牛津大学）；University of Reading（雷丁大学）；IUCAA（校际天文学与天体物理学中心）；University of New Hampshire（新罕布什尔大学）；NVIDIA Corporation（英伟达公司）；ASTRA LLC（ASTRA有限责任公司）；Lockheed Martin（洛克希德·马丁公司）；SRI International（SRI国际研究所）
Panagiotis Tigas, Téo Bloch, Vishal Upendran, Banafsheh Ferdoushi, Mark C. M. Cheung, Siddha Ganju, Ryan M. McGranaghan, Yarin Gal, Asti Bhatt
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Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada
Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge. Current approaches depend on simulations of computationally demanding models like the Magnetohydrodynamics (MHD) model or sampling spatially and temporally through sparse ground-based stations (SuperMAG). In this paper, we develop a Deep Learning model that forecasts in Spherical Harmonics space 2, replacing reliance on MHD models and providing global coverage at one minute cadence, improving over the current state-of-the-art which relies on feature engineering. We evaluate the performance in SuperMAG dataset (improved by 14.53%) and MHD simulations (improved by 24.35%). Additionally, we evaluate the extrapolation performance of the spherical harmonics reconstruction based on sparse ground-based stations (SuperMAG), showing that spherical harmonics can reliably reconstruct the global magnetic field as evaluated on MHD simulation.
Simple and Robust Forecasting of Spatiotemporally Correlated Small Earth Data with A Tabular Foundation Model
School of Engineering and Technology, China University of Geosciences (Beijing)（工程与技术学院，中国地质大学（北京））；School of Geological Engineering and Geomatics, Chang'an University（地质工程与测绘学院，长安大学）
Integrating Artificial Intelligence and Geophysical Insights for Earthquake Forecasting: A Cross-Disciplinary Review
University of Science and Technology Beijing（北京科技大学）；Beijing Engineering Research Center of Industrial Spectrum Imaging（北京市工业光谱成像工程研究中心）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Southern University of Science and Technology（南方科技大学）；China University of Geosciences (Beijing)（中国地质大学（北京））
Institute of Earthquake Forecasting, China Earthquake Administration（中国地震局地震预测研究所）；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）；University of Leicester（莱斯特大学）；Nanyang Technological University（南洋理工大学）；University of Trento（特伦托大学）；National Institute for Nuclear Physics, the Trento Institute for Fundamental Physics and Applications（意大利国家核物理研究院特伦托基础物理与应用研究所）；Chapman University（查普曼大学）；Queen's University Belfast（贝尔法斯特女王大学）；Nantong University（南通大学）
Amongst the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being …
S$^3$N: A Spherical Spiral Scanning Network for Weather Forecasting
Nanjing University of Information Science and Technology（南京信息工程大学）；Nanyang Technological University（南洋理工大学）；Harbin Institute of Technology（哈尔滨工业大学）；Princeton University（普林斯顿大学）；Karolinska Institutet（卡罗林斯卡学院）
Geomagnetic storms, disturbances of Earth's magnetosphere caused by masses of charged particles being emitted from the Sun, are an uncontrollable threat to modern technology. Notably, they have the potential to damage satellites and cause instability in power grids on Earth, among other disasters. They result from high sun activity, which are induced from cool areas on the Sun known as sunspots. Forecasting the storms to prevent disasters requires an understanding of how and when they will occur. However, current prediction methods at the National Oceanic and Atmospheric Administration (NOAA) …
Discovering Spatial Correlations of Earth Observations for weather forecasting by using Graph Structure Learning
Data Assimilation Group\ Institute of Atmospheric Prediction Systems 35, 5-gil, Boramae-ro Seoul Republic of Korea 07071；Network Science Lab\ Catholic University of Korea 43 Jibong-ro Bucheon Republic of Korea 14662；Data Assimilation Group\ Institute of Atmospheric Prediction Systems；Network Science Lab\ Catholic University of Korea
Recommendations for Comprehensive and Independent Evaluation of Machine Learning-Based Earth System Models
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；University of California Davis（加州大学戴维斯分校）；Colorado State University（科罗拉多州立大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；National Center for Atmospheric Research（国家大气研究中心）；Indiana University Bloomington（印第安纳大学伯明顿分校）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of Maryland（马里兰大学）
Explainable Earth Surface Forecasting under Extreme Events
Image Processing Laboratory (IPL), Universitat de València（瓦伦西亚大学图像处理实验室）；Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University（莱比锡大学地球系统研究遥感中心）；Institute for Earth System Science and Remote Sensing, Leipzig University（莱比锡大学地球系统科学与遥感研究所）；Image and Signal Processing Group, Leipzig University（莱比锡大学图像与信号处理组）；Helmholtz Centre for Environmental Research – UFZ（亥姆霍兹环境研究中心）
Frontier Development Lab（前沿发展实验室）；Inter University Centre for Astronomy and Astrophysics（校际天文与天体物理中心）；University of Oxford（牛津大学）；University of New Hampshire（新罕布什尔大学）；University of Reading（雷丁大学）；Lockheed Martin Advanced Technology Center（洛克希德·马丁先进技术中心）；NVIDIA Corporation（英伟达公司）；SRI International（斯坦福国际研究院）；ASTRA LLC（阿斯特拉有限责任公司）
Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs…
Tsinghua University（清华大学）；Tencent（腾讯）；Ocean University of China（中国海洋大学）；West China Biomedical Big Data Center（西部生物医药大数据中心）；Johannes Kepler University (JKU) Linz（约翰内斯·开普勒大学林茨分校）；Emmi AI；University of Science and Technology of China（中国科学技术大学）；Nanyang Technological University（南洋理工大学）；University of Missouri-Kansas City（密苏里大学堪萨斯城分校）；The Chinese University of Hong Kong（香港中文大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Boson AI；Southeast University（东南大学）；Cornell University（康奈尔大学）；The Hong Kong Polytechnic University（香港理工大学）；City University of Hong Kong（香港城市大学）；Eastern Institute of Technology, Ningbo（宁波东方理工大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Squirrel Ai Learning（松鼠Ai学习）；Shenzhen Loop Area Institute（深圳河套学院）；Technical University of Munich（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）
A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting
Southwest Research Institute（西南研究院）；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Space Weather Prediction Center, NOAA（美国国家海洋和大气管理局空间天气预测中心）；Cooperative Institute for Research in Environmental Sciences, University of Boulder（科罗拉多大学环境科学合作研究所）
A Geometry-Aware AI Emulator for the Coupled Whole Atmosphere from Earth Surface to the Ionosphere and Thermosphere
Center for Space and Atmospheric Research, Embry-Riddle Aeronautical University, Daytona Beach, Florida, USA（空间与大气研究中心，埃姆布里-瑞德航空大学，佛罗里达州达科他海滩）；Global Atmospheric Technologies and Sciences, Boulder, Colorado, USA（全球大气科技与科学，科罗拉多州博尔德）
Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields
CMA Earth system Modeling and Prediction Center (CEMC)（中国气象局地球系统数值预报中心）；State Key Laboratory of Severe Weather (LaSW)（灾害天气国家重点实验室）；School of Computer Science and Engineering, Sichuan University of Science and Engineering（四川轻化工大学计算机科学与工程学院）；School of Electronic and Communication Engineering, Guangzhou University（广州大学电子与通信工程学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Assessing the predicting power of GPS data for aftershocks forecasting
The Institute of Statistical Mathematics, Research Organization of Information and Systems（信息与系统研究机构统计数学研究所）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables f…
Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting
Swiss Data Science Center (SDSC), ETH Zurich & EPFL（瑞士数据科学中心（SDSC）、苏黎世联邦理工学院与洛桑联邦理工学院）；ETH AI Center, ETH Zurich（苏黎世联邦理工学院人工智能中心）；Federal Office for Meteorology and Climatology MeteoSwiss（联邦气象与气候办公室MeteoSwiss）；Institute of Geodesy and Photogrammetry, ETH Zurich（苏黎世联邦理工学院测绘与摄影测量研究所）；Computational and Applied Mathematics Laboratory, ETH Zurich（苏黎世联邦理工学院计算与应用数学实验室）；Institute for Atmospheric and Climate Science (IAC), ETH Zurich（大气与气候科学研究所（IAC）、苏黎世联邦理工学院）；Scalable Parallel Computing Lab, ETH Zurich（可扩展并行计算实验室、苏黎世联邦理工学院）；Dep. of Applied Mathematics and Theoretical Physics, University of Cambridge（应用数学与理论物理系、剑桥大学）
Multi-modal learning for geospatial vegetation forecasting
Max-Planck-Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；ELLIS Unit Jena（耶拿ELLIS单元）；ETH Zürich（苏黎世联邦理工学院）；Hong Kong University of Science and Technology（香港科技大学）
McGill University（麦吉尔大学）；Mila - Quebec AI Institute（魁北克人工智能研究所Mila）
Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth. Biases identified with the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol optical depth (AOD) dataset, against the Aerosol Robotic Network (AERONET) ground measurements in previous studies, motivated the development of a deep learning based AOD prediction model globally. This study combines a convolutional neural network (CNN) with MERRA-2, tested against a…
Following N.Kozyrev's idea about the influence of the gravitational fields of the Sun and the Moon on the Earth's crust, we consider a low-frequency resonance of the Earth's crust blocks is happening before the occurrence of the earthquake. The resonance affects several geophysical parameters and is associated with many short-term precursory phenomena; the most notable are the release of underground gases (radon), the air temperature rises, change of thermal radiation in the troposphere, and an increase in the electron density in the ionosphere. During the final stage of the earthquake genesis…
Earthquake Aftershock Forecasting using Conditional Generative Models
Department of Earth and Planetary Science, University of California, Berkeley（加州大学伯克利分校地球与行星科学系）；Berkeley Seismological Laboratory, University of California, Berkeley（加州大学伯克利分校伯克利地震实验室）
Shifts in magnetotelluric (MT) responses owing to localized source currents should be considered when visualizing deep subsurface resistivity structures such as the earth mantle. The earth is not flat but spherical; nevertheless, non-uniform geomagnetic temporal variations arising from localized source currents are evaluated on the basis of components in a Cartesian coordinate system. To address this issue, this study assesses the difference in source bias within MT responses of a flat earth and spherical earth. Apparent resistivity and phases are calculated by setting the time period and cond…
This report starts by describing the continuum model used by Lingle & Clark (1985) to approximate the deformation of the earth under changing ice sheet and ocean loads. That source considers a single ice stream, but we apply their underlying model to continent-scale ice sheet simulation. Their model combines Farrell's (1972) elastic spherical earth with a viscous half-space overlain by an elastic plate lithosphere. The latter half-space model is derivable from calculations by Cathles (1975). For the elastic spherical earth we use Farrell's tabulated Green's function, as do Lingle & Clark. For …
For realistic geodynamo simulations, one must solve the magnetohydrodynamic equations to follow time development of thermal convection motion of electrically conducting fluid in a rotating spherical shell. We have developed a new geodynamo simulation code by combining the finite difference method with the recently proposed spherical overset grid called Yin-Yang grid. We achieved performance of 15.2 Tflops (46% of theoretical peak performance) on 4096 processors of the Earth Simulator.
Real-time probabilistic tsunami forecasting in Cascadia from sparse offshore pressure observations
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校奥登计算工程与科学学院）；Scripps Institution of Oceanography, University of California San Diego（加州大学圣地亚哥分校斯克里普斯海洋研究所）；Walker Department of Mechanical Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校沃克机械工程系）；Department of Earth and Environmental Sciences, Ludwig‐Maximilians‐Universität München（慕尼黑路德维希-马克西米利安大学地球与环境科学系）
Current source models for the Earth's main geomagnetic field are calculated employing conventional discrete inverse theory methods. Source structures are spherical surfaces placed at the surface of the Earth's core, and at the surface of the Earth. The data set consists of measurements taken by the MAGSAT satellite in 1979. The resulting current distributions are discussed in relation to dipole and current loop models.
While many geological and geophysical processes such as the melting of icecaps, the magnetic expression of bodies emplaced in the Earth's crust, or the surface displacement remaining after large earthquakes are spatially localized, many of these naturally admit spectral representations, or they may need to be extracted from data collected globally, e.g. by satellites that circumnavigate the Earth. Wavelets are often used to study such nonstationary processes. On the sphere, however, many of the known constructions are somewhat limited. And in particular, the notion of `dilation' is hard to rec…
Many approaches are developed for the forecasting of the Earth rotation pa-rameters. In this work, we consider long-term vector prediction scheme realized on the artificial neural network. Learning set is formed on basis of the Taken' algorithm. Our approach allows us to obtain the vector of the parameter values and escape the expo-nential growth of the prediction errors. The versions of the prediction enhancement based on the using for nonlinear corrector are discussed.
Predicting tsunami impacts at remote coasts largely relies on tsunami en-route measurements in an open ocean. In this work, these measurements are used to generate instant tsunami predictions in deep water and near the coast. The predictions are generated as a response or a combination of responses to one or more tsunameters, with each response obtained as a convolution of real-time tsunameter measurements and a pre-computed Pulse Response Function (PRF). Practical implementation of this method requires tables of PRFs in a 3D parameter space: earthquake location - tsunameter - forecasted site.…
