Machine learning (ML) has often been applied to space weather (SW) problems in recent years. SW originates from solar perturbations and is comprised of the resulting complex variations they cause within the systems between the Sun and Earth. These systems are tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system is the thermosphere, the neutral region of Earth's upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and collision avoidance operations for objects in low Earth orbit. Even with (assumed) perfect driver forecasts, our incomplete knowledge of the system results in often inaccurate neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of uncertainty. In this work, we propose two techniques to develop nonlinear ML models to predict thermospheric density while providing calibrated uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance for models trained on local and global datasets. This shows that NLPD provides similar results for both techniques but the direct probability method has a much lower computational cost. For the global model regressed on the SET HASDM density database, we achieve errors of 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAMP density dataset, both techniques provide test error on the order of 13%. The CHAMP models (on independent data) are within 2% of perfect calibration for all prediction intervals tested. This model can also be used to obtain global predictions with uncertainties at a given epoch.
West Virginia University（西弗吉尼亚大学）；Space Environment Technologies（空间环境技术公司）
The first thermospheric neutral mass density model with robust and reliable uncertainty estimates is developed based on the SET HASDM density database. This database, created by Space Environment Technologies (SET), contains 20 years of outputs from the U.S. Space Force's High Accuracy Satellite Drag Model (HASDM), which represents the state-of-the-art for density and drag modeling. We utilize principal component analysis (PCA) for dimensionality reduction, creating the coefficients upon which nonlinear machine-learned (ML) regression models are trained. These models use three unique loss func…
West Virginia University（西弗吉尼亚大学）；Virginia Tech（弗吉尼亚理工大学）；U.S. Naval Research Laboratory（美国海军研究实验室）；Space Environments Technologies（空间环境技术公司）
Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information related to the ground-truth dataset used for fitting. A benefit to ML over parametric models is that there are no predefined basis functions limiting the phenomena that can be modeled. In this work, we develop ML models on three datasets: the Space Environment Technologies (SET) High Accuracy Satellite Drag Model (HASDM) density database, a spatiotemporally…
University of Technology Sydney（悉尼科技大学）；Southwest Jiaotong University（西南交通大学）；JD Digits（京东数科）
Weather forecasting is usually solved through numerical weather prediction (NWP), which can sometimes lead to unsatisfactory performance due to inappropriate setting of the initial states. In this paper, we design a data-driven method augmented by an effective information fusion mechanism to learn from historical data that incorporates prior knowledge from NWP. We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function. A notable advantage of our proposed method is that it simultaneously imp…
Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations. To provide accurate estimation, dozens of such computationally intensive simulations must be run. We show that deep neural networks can be used on a small set of numerical weather simulations to estimate the spread of a weather forecast, significantly reducing computational cost. To train the system, we both modify the 3D U-Net architecture and explore models that incorporate temporal data. Our models serve as a sta…
On the Predictive Skill of Artificial Intelligence-based Weather Models for Extreme Events using Uncertainty Quantification
Applied Machine Learning Group（应用机器学习组）；Fraunhofer Heinrich-Hertz Institute（弗劳恩霍夫海因里希-赫兹研究所）；Signal Theory and Communications Dept.（信号理论与通信系）；Universidad Carlos III de Madrid (UC3M)（马德里卡洛斯三世大学）
West Virginia University（西弗吉尼亚大学）；Virginia Tech（弗吉尼亚理工学院暨州立大学）；Space Environments Technologies（空间环境技术公司）
The Mass Spectrometer and Incoherent Scatter radar (MSIS) model family has been developed and improved since the early 1970's. The most recent version of MSIS is the Naval Research Laboratory (NRL) MSIS 2.0 empirical atmospheric model. NRLMSIS 2.0 provides species density, mass density, and temperature estimates as function of location and space weather conditions. MSIS models have long been a popular choice of atmosphere model in the research and operations community alike, but - like many models - does not provide uncertainty estimates. In this work, we develop an exospheric temperature mode…
Uncertainty Quantification of Wind Gust Predictions in the Northeast United States: An Evidential Neural Network and Explainable Artificial Intelligence Approach
University of Connecticut（康涅狄格大学）；NSF National Center for Atmospheric Research（NSF国家大气研究中心）
Accurate and Efficient Hybrid-Ensemble Atmospheric Data Assimilation in Latent Space with Uncertainty Quantification
Department of Earth and Environmental Engineering, School of Engineering and Applied Sciences, Climate School, Columbia University（地球与环境工程系，工程与应用科学学院，气候学院，哥伦比亚大学）；Learning the Earth with Artificial Intelligence and Physics (LEAP) Center, Columbia University（人工智能与物理学习地球中心，哥伦比亚大学）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室，上海，中国）；The Chinese University of Hong Kong, Hong Kong, China（香港中文大学，香港，中国）；Department of Computer Science and Technology, Tsinghua University, Beijing, China（计算机科学与技术系，清华大学，北京，中国）
CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty Quantification
Florida International University（佛罗里达国际大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Hurricane Research Division, National Oceanic and Atmospheric Administration（国家海洋和大气管理局飓风研究 division）
Data-Driven Uncertainty-Aware Forecasting of Sea Ice Conditions in the Gulf of Ob Based on Satellite Radar Imagery
Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russia（斯克尔科沃科学与技术研究所应用人工智能中心）；Moscow Institute of Physics and Technology, Dolgoprudny, Russia（莫斯科物理技术学院）；Autonomous Non-Profit Organization Artificial Intelligence Research Institute (AIRI), Moscow, Russia（自主非营利组织人工智能研究 institute（AIRI））
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
National Center for Atmospheric Research（国家大气研究中心）；Cooperative Institute for Severe and High-Impact Weather Research and Operations（恶劣与高影响天气研究与合作研究所）；National Severe Storms Laboratory（国家强风暴实验室）；University Corporation for Atmospheric Research（大学大气研究公司）；University of Maryland（马里兰大学）；University at Albany, State University of New York（纽约州立大学奥尔巴尼分校）；Colorado State University（科罗拉多州立大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps
Department of Statistics and Data Science, Carnegie Mellon University（统计与数据科学系，卡内基梅隆大学）；Department of Statistics, Federal University of Sao Carlos（统计系，圣卡洛斯联邦大学）
Online machine-learning forecast uncertainty estimation for sequential data assimilation
Servicio Meteorológico Nacional（国家气象局）；Universidad de Buenos Aires（布宜诺斯艾利斯大学）；Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (IRL 3351 IFAECI)（法阿气候及其影响研究所（IRL 3351 IFAECI））；Universidad Nacional del Nordeste（东北国立大学）；Centro de Investigaciones del Mar y la Atmósfera（海洋与大气研究中心）；IMT Atlantique（大西洋国立高等工业技术学院）；Inria（法国国家信息与自动化研究所）
A Diffusion-Based Framework for High-Resolution Precipitation Forecasting over CONUS
University of Oklahoma, School of Meteorology, Norman, Oklahoma（俄克拉荷马大学气象学院）；University of Oklahoma, School of Computer Science, Norman, Oklahoma（俄克拉荷马大学计算机科学学院）；NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, Norman, Oklahoma（国家科学基金会可信人工智能研究机构）；University of California Irvine, Irvine, California（加州大学伊维德分校）；San Diego State University, San Diego, California（圣地亚哥州立大学）
Probabilistic neural operators for functional uncertainty quantification
Ludwig-Maximilians-Universität München（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；University of Tromsø（特罗姆瑟大学）；DLR-German Aerospace Center（德国航空航天中心）
Computer Science Georgia State University Atlanta, USA（计算机科学 乔治亚州立大学 奥斯汀, 美国）；Computer Science Texas Christian University Texas, USA（计算机科学 德克萨斯基督教大学 德克萨斯, 美国）
The disturbance storm time (Dst) index is an important and useful measurement in space weather research. It has been used to characterize the size and intensity of a geomagnetic storm. A negative Dst value means that the Earth's magnetic field is weakened, which happens during storms. In this paper, we present a novel deep learning method, called the Dst Transformer, to perform short-term, 1-6 hour ahead, forecasting of the Dst index based on the solar wind parameters provided by the NASA Space Science Data Coordinated Archive. The Dst Transformer combines a multi-head attention layer with Bay…
There has been significant research done on developing methods for improving robustness to distributional shift and uncertainty estimation. In contrast, only limited work has examined developing standard datasets and benchmarks for assessing these approaches. Additionally, most work on uncertainty estimation and robustness has developed new techniques based on small-scale regression or image classification tasks. However, many tasks of practical interest have different modalities, such as tabular data, audio, text, or sensor data, which offer significant challenges involving regression and dis…
TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction
Florida State University（佛罗里达州立大学）；Massachusetts Institute of Technology（麻省理工学院）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；University of Florida（佛罗里达大学）
