MEMPSEP III - A machine learning-oriented multivariate data set for forecasting the occurrence and properties of solar energetic particle events using a multivariate ensemble approach

Aug 6, 2026

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A new multivariate dataset supports machine-learning research in heliophysics by linking multiple spacecraft measurements to physical processes responsible for solar energetic particle (SEP) events. Using the GOES flare event list for Solar Cycle 23 and part of Cycle 24 (1998–2013), 252 SEP-producing flare events and 17,542 non-producing events are identified. For each event, local 1 au plasma properties, energetic protons/electrons, upstream solar-wind and IMF vector quantities from GOES and ACE are compiled, alongside remote-sensing data from SDO, SoHO, and Wind/WAVES. Public observations are validated, cleaned, and curated for the MEMPSEP pipeline.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Key Points

Abstract

Plain Language Summary

Introduction

Solar energetic particles and hazards

Solar flares and coronal mass ejections

MEMPSEP III. A machine learning-oriented multivariate data set for forecasting the Occurrence and Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach Kimberly Moreland1,2 , Maher Dayeh2,1 , Hazel M. Bain4,5, Subhamoy Chatterjee3, Andre´s Mu n˜oz-Jaramillo3, Samuel Hart1,2 1The University of Texas at San Antonio, San Antonio, TX, USA 2Southwest Research Institute, San Antonio, TX, USA 3Southwest Research Institute, Boulder, CO, USA 4Cooperative Institute for Research in Environmental Sciences, University of Boulder, CO, USA 5Space Weather Prediction Center, NOAA, Boulder, CO, USA Key Points: • Machine Learning oriented dataset for SEP event prediction and subsequent properties • Multivariate remote sensing and in-situ observations • Continuous dataset spanning several decades Corresponding author: Kimberly Moreland, [kim.moreland@contractor.swri.org](kim.moreland@contractor.swri.org) Abstract We introduce a new multivariate data set that utilizes multiple spacecraft collecting in-situ and remote sensing heliospheric measurements shown to be linked to physical processes responsible for generating solar energetic particles (SEPs) . Using the Geostationary Operational Environmental Satellites (GOES) flare event list from Solar Cycle (SC) 23 and part of SC 24 (1998-2013), we identify 252 solar events (flares) that produce SEPs and 17,542 events that do not. For each identified event, we acquire the local plasma properties at 1 au, such as energetic proton and electron data, upstream solar wind conditions, and the interplanetary magnetic field vector quantities using various instruments onboard GOES and the Advanced Composition Explorer (ACE) spacecraft. We also collect remote sensing data from instruments onboard the Solar Dynamic Observatory (SDO), Solar and Heliospheric Observatory (SoHO), and the Wind solar radio instrument WAVES. The data set is designed to allow for variations of the inputs and feature sets for machine learning (ML) in heliophysics and has a specific purpose for forecasting the occurrence of SEP events and their subsequent properties. This paper describes a dataset created from multiple publicly available observation sources that is validated, cleaned, and carefully curated for our machine-learning pipeline. The dataset has been used to drive the newly-developed Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP; see MEMPSEP I (Chatterjee et al., 2023) and MEMPSEP II (Dayeh et al., 2023) for associated papers) . Plain Language Summary We present a new dataset that uses observations from multiple spacecraft observing the Sun and the interplanetary space around it. This data is connected to the processes that create solar energetic particles (SEPs). SEP events pose threats to both astronauts and equipment in space. The dataset contains 252 solar events that caused SEPs and 17,542 that do not. For each event, we gather information about the local space environment around the sun, such as energetic protons and electrons, the conditions of the solar wind, the magnetic field, and remote solar imaging data. We use instruments from NOAA’s Geo-stationary Operational Environmental Satellites (GOES) and the Advanced Composition Explorer (ACE) spacecraft, as well as data from the Solar Dynamic Observatory (SDO), the Solar and Heliospheric Observatory (SoHO), and the Wind solar radio instrument WAVES. This data set is designed to be used in machine learning, with a focus on predicting the occurrence and properties of SEP events. We detail each observation obtained from publicly available sources, and the data treatment processes used to validate the reliability and usefulness for machine-learning applications. 1 Introduction Solar energetic particles are high-energy particles associated with two main types of solar activity: solar flares (SFs) and coronal mass ejections (CMEs) (M. Desai & Giacalone, 2016; Reames

FAQs

What does the dataset enable in SEP forecasting research?

It provides a machine-learning-ready multivariate dataset designed to forecast the occurrence of SEP events and their subsequent properties using variations of inputs and feature sets.

How are SEP-producing and non-producing events selected?

Events are identified using the GOES flare event list from Solar Cycle 23 and part of Solar Cycle 24 (1998–2013), resulting in 252 SEP-producing flare events and 17,542 non-producing events.

Which observations are included for each event?

The dataset combines 1 au local plasma properties (energetic protons and electrons), upstream solar-wind conditions and interplanetary magnetic-field vectors from GOES and ACE, plus remote-sensing measurements from SDO, SoHO, and Wind/WAVES.

Aug 6, 2026

Upload by Patrick