MEMPSEP‐II. Forecasting the Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach
Abstract Solar Energetic Particles (SEPs) form a critical component of Space Weather. The complex, intertwined dynamics of SEP sources, acceleration, and transport make their forecasting very challenging. Yet, information about SEP arrival and their properties (e.g., peak flux) is crucial for space...
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| Format: | Article |
| Language: | English |
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Wiley
2024-09-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003697 |
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| author | Maher A. Dayeh Subhamoy Chatterjee Andrés Muñoz‐Jaramillo Kimberly Moreland Hazel M. Bain Samuel T. Hart |
| author_facet | Maher A. Dayeh Subhamoy Chatterjee Andrés Muñoz‐Jaramillo Kimberly Moreland Hazel M. Bain Samuel T. Hart |
| author_sort | Maher A. Dayeh |
| collection | DOAJ |
| description | Abstract Solar Energetic Particles (SEPs) form a critical component of Space Weather. The complex, intertwined dynamics of SEP sources, acceleration, and transport make their forecasting very challenging. Yet, information about SEP arrival and their properties (e.g., peak flux) is crucial for space exploration on many fronts. We have recently introduced a novel probabilistic ensemble model called the Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). Its primary aim is to forecast the occurrence and physical properties of SEPs. The occurrence forecasting, thoroughly discussed in a preceding paper (MEMPSEP‐I by Chatterjee et al., 2024a, https://doi.org/10.1029/2023sw003568), is complemented by the work presented here, which focuses on forecasting the physical properties of SEPs. The MEMPSEP model relies on an ensemble of Convolutional Neural Networks, which leverage a multi‐variate data set comprising full‐disc magnetogram sequences and numerous derived and in‐situ data from various sources (MEMPSEP‐III by Moreland et al., 2024, https://doi.org/10.1029/2023SW003765). Skill scores demonstrate that MEMPSEP exhibits improved predictions on SEP properties for the test set data with SEP occurrence probability above 50%, compared to those with a probability below 50%. Results present a promising approach to address the challenging task of forecasting SEP physical properties, thus improving our forecasting capabilities and advancing our understanding of the dominant parameters and processes that govern SEP production. |
| format | Article |
| id | doaj-art-3c683507a9894bee8ecdea1efcd62e6c |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-3c683507a9894bee8ecdea1efcd62e6c2025-01-14T16:35:30ZengWileySpace Weather1542-73902024-09-01229n/an/a10.1029/2023SW003697MEMPSEP‐II. Forecasting the Properties of Solar Energetic Particle Events Using a Multivariate Ensemble ApproachMaher A. Dayeh0Subhamoy Chatterjee1Andrés Muñoz‐Jaramillo2Kimberly Moreland3Hazel M. Bain4Samuel T. Hart5Southwest Research Institute San Antonio TX USASouthwest Research Institute Boulder CO USASouthwest Research Institute Boulder CO USASouthwest Research Institute San Antonio TX USACooperative Institute for Research in Environmental Sciences University of Boulder Boulder CO USASouthwest Research Institute San Antonio TX USAAbstract Solar Energetic Particles (SEPs) form a critical component of Space Weather. The complex, intertwined dynamics of SEP sources, acceleration, and transport make their forecasting very challenging. Yet, information about SEP arrival and their properties (e.g., peak flux) is crucial for space exploration on many fronts. We have recently introduced a novel probabilistic ensemble model called the Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). Its primary aim is to forecast the occurrence and physical properties of SEPs. The occurrence forecasting, thoroughly discussed in a preceding paper (MEMPSEP‐I by Chatterjee et al., 2024a, https://doi.org/10.1029/2023sw003568), is complemented by the work presented here, which focuses on forecasting the physical properties of SEPs. The MEMPSEP model relies on an ensemble of Convolutional Neural Networks, which leverage a multi‐variate data set comprising full‐disc magnetogram sequences and numerous derived and in‐situ data from various sources (MEMPSEP‐III by Moreland et al., 2024, https://doi.org/10.1029/2023SW003765). Skill scores demonstrate that MEMPSEP exhibits improved predictions on SEP properties for the test set data with SEP occurrence probability above 50%, compared to those with a probability below 50%. Results present a promising approach to address the challenging task of forecasting SEP physical properties, thus improving our forecasting capabilities and advancing our understanding of the dominant parameters and processes that govern SEP production.https://doi.org/10.1029/2023SW003697space weatherforecasting SEP occurrenceforecasting SEP properties |
| spellingShingle | Maher A. Dayeh Subhamoy Chatterjee Andrés Muñoz‐Jaramillo Kimberly Moreland Hazel M. Bain Samuel T. Hart MEMPSEP‐II. Forecasting the Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach Space Weather space weather forecasting SEP occurrence forecasting SEP properties |
| title | MEMPSEP‐II. Forecasting the Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach |
| title_full | MEMPSEP‐II. Forecasting the Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach |
| title_fullStr | MEMPSEP‐II. Forecasting the Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach |
| title_full_unstemmed | MEMPSEP‐II. Forecasting the Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach |
| title_short | MEMPSEP‐II. Forecasting the Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach |
| title_sort | mempsep ii forecasting the properties of solar energetic particle events using a multivariate ensemble approach |
| topic | space weather forecasting SEP occurrence forecasting SEP properties |
| url | https://doi.org/10.1029/2023SW003697 |
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