Space Weather Forecasting

Latest papers

11 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

Machine learning applications in the heliosphere
Oct 27, 2026Vishal Upendran, Banafsheh FerdousiSpace Weather Forecasting Scientific ML
SETI Institute, Mountain View, CA, USA 94043 · Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, USA · Air Force Research Lab, Albuquerque, New Mexico, USA
ARENA: Active Region attENtion Aggregation for interpretable solar flare prediction
Oct 23, 2026Luis Bauer, Annalena Kofler, Andrés Asensio Ramos +3Space Weather Forecasting Attention Mechanisms
MPI for Intelligent Systems, Tübingen · University of Tübingen · MPI for Gravitational Physics, Potsdam +4
Space weather dependence of decameter-scale ionospheric irregularities spatial distribution
Oct 21, 2026Oleg I. Berngardt, Ivan A. LavyginSpatiotemporal Forecasting Space Weather Forecasting
ISTP SB RAS, 664033, Lermontova Str, 126A, Irkutsk, Russia
Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques
Sep 25, 2026Francesco Pio Ramunno, Simone Mestici, Igino Coco +5Space Weather Forecasting Spatiotemporal Forecasting
University of Applied Sciences and Arts Nortwestern Switzerland · Department of Computer Science, University of Geneva, 1211 Geneva, Switzerland · Universit´a Roma ”La Sapienza” +3
PISCES: Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather Anomaly Detection and Early Warning
Sep 23, 2026Kevin Lee, Alison J. MarchUnsupervised Anomaly Detection Interpretable Anomaly Detection
NASA Jet Propulsion Laboratory, La Cañada Flintridge, CA, USA · Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA · College of Engineering & Applied Science, University of Colorado, Boulder, CO, USA
PROSWIN: Probabilistic Solar Wind Speed Forecasting Using Deep Distributional Regression From Solar Images
Sep 22, 2026Daniel Collin, Yuri Shprits, Luca Chiarabini +3Time Series Forecasting Distributional Regression
Space Physics and Space Weather, GFZ Helmholtz Centre for Geosciences, Potsdam, Germany · Department of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany · Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany +5
A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
Jul 21, 2026Sathvik Soman, Jason T. L. Wang, Haimin Wang +1Solar Irradiance Forecasting Time Series Forecasting
New Jersey Institute of Technology, Newark, NJ, USA · Sam Houston State University, Huntsville, TX, USA
Review of Machine Learning Models for Solar Energetic Particle Prediction
Jun 17, 2026Spiridon Kasapis, Pouya Hosseinzadeh, Kathryn Whitman +73Space Weather Forecasting
Deep Learning-Enabled Prediction of Geoeffective CMEs Using SOHO and SDO Observations
May 23, 2026Zhaoxin Yan, Jason T. L. Wang, Haimin Wang +5Probabilistic Forecasting Space Weather Forecasting
Institute for Space Weather Sciences, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA · Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA · Center for Solar-Terrestrial Research, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA +1
Aurora Hunter: A Two-Stage Framework for Probabilistic Visibility Forecasting
May 21, 2026Zongyuan Ge, Chenwaner Zhang, Haoyang Li +4Probabilistic Forecasting Spatiotemporal Forecasting
College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao 266100, China · School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, People’s Republic of China · Engineering Research Center of Advanced Marine Physical Instruments and Equipment, Ministry of Education, Qingdao 266100, China +1
Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning
Apr 11, 2026Zhenduo Wang, Yasser Abduallah, Jason T. L. Wang +6Space Weather Forecasting Time Series Forecasting
Institute for Space Weather Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA · Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA · Center for Solar-Terrestrial Research, New Jersey Institute of Technology, Newark, NJ 07102, USA +3

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Machine learning applications in the heliosphere

Vishal Upendran, Banafsheh Ferdousi

SETI Institute, Mountain View, CA, USA 94043 · Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, USA · Air Force Research Lab, Albuquerque, New Mexico, USA

Space Weather Forecasting Scientific ML

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Space weather is the study of the near-Earth impact of the processes on the Sun. In a this era of technology, space weather emerges as a crucial area of study wherein the state of the near-Earth conditions need to be modeled and forecasted. Crucially, the advent of machine learning provides us with opportunities to study this subject in a rather comprehensive manner. In this chapter, we treat the the processes arising from Sun to the impact on the ground as a set of coupled systems, which can be studied in isolation or in a coupled manner. We define these individual systems in terms of their state vector

\vec{S}

, which provides us with an opportunity to define the physical variables determining the system. This also allows us to consider the systems in terms of their observables, obtained through the action of operators as

\vec{O}[\vec{S}]

. In such a formalism, the machine learning algorithms serve as {\it learned operators}, defined generally as inference (

\mathcal{I}

), propagation (

\mathcal{P}

), and emulation (

\mathcal{E}

). Through such a formalism, we present how machine learning helps advance our understanding of the physical systems under study, while also accelerating the development of operational models for space weather forecasting.