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
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...
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Wiley
2024-09-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003765 |
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| author | Kimberly Moreland Maher A. Dayeh Hazel M. Bain Subhamoy Chatterjee Andrés Muñoz‐Jaramillo Samuel T. Hart |
| author_facet | Kimberly Moreland Maher A. Dayeh Hazel M. Bain Subhamoy Chatterjee Andrés Muñoz‐Jaramillo Samuel T. Hart |
| author_sort | Kimberly Moreland |
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| description | 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 (>C‐class 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 spacecraft. We also collect remote sensing data from instruments onboard the Solar Dynamic Observatory, Solar and Heliospheric Observatory, 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 data set created from multiple publicly available observation sources that is validated, cleaned, and carefully curated for our ML pipeline. The data set has been used to drive the newly‐developed Multivariate Ensemble of Models for Probabilistic Forecast of SEPs (MEMPSEP; see MEMPSEP‐I (Chatterjee et al., 2024, https://doi.org/10.1029/2023SW003568) and MEMPSEP‐II (Dayeh et al., 2024, https://doi.org/10.1029/2023SW003697) for accompanying papers). |
| format | Article |
| id | doaj-art-2952079aa2a846b381d63a0a87968614 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
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| series | Space Weather |
| spelling | doaj-art-2952079aa2a846b381d63a0a879686142025-01-14T16:35:31ZengWileySpace Weather1542-73902024-09-01229n/an/a10.1029/2023SW003765MEMPSEP‐III. A Machine Learning‐Oriented Multivariate Data Set for Forecasting the Occurrence and Properties of Solar Energetic Particle Events Using a Multivariate Ensemble ApproachKimberly Moreland0Maher A. Dayeh1Hazel M. Bain2Subhamoy Chatterjee3Andrés Muñoz‐Jaramillo4Samuel T. Hart5The University of Texas at San Antonio San Antonio TX USAThe University of Texas at San Antonio San Antonio TX USACooperative Institute for Research in Environmental Sciences University of Boulder Boulder CO USASouthwest Research Institute Boulder CO USASouthwest Research Institute Boulder CO USAThe University of Texas at San Antonio San Antonio TX USAAbstract 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 (>C‐class 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 spacecraft. We also collect remote sensing data from instruments onboard the Solar Dynamic Observatory, Solar and Heliospheric Observatory, 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 data set created from multiple publicly available observation sources that is validated, cleaned, and carefully curated for our ML pipeline. The data set has been used to drive the newly‐developed Multivariate Ensemble of Models for Probabilistic Forecast of SEPs (MEMPSEP; see MEMPSEP‐I (Chatterjee et al., 2024, https://doi.org/10.1029/2023SW003568) and MEMPSEP‐II (Dayeh et al., 2024, https://doi.org/10.1029/2023SW003697) for accompanying papers).https://doi.org/10.1029/2023SW003765SEPsforecastingdata setmodeldata curationmachine learning |
| spellingShingle | Kimberly Moreland Maher A. Dayeh Hazel M. Bain Subhamoy Chatterjee Andrés Muñoz‐Jaramillo Samuel T. Hart 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 Space Weather SEPs forecasting data set model data curation machine learning |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | SEPs forecasting data set model data curation machine learning |
| url | https://doi.org/10.1029/2023SW003765 |
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