Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux
Abstract In this work, we address the computational challenge of large‐scale physics‐based simulation models for the ring current. Reduced computational cost allows for significantly faster than real‐time forecasting, enhancing our ability to predict and respond to dynamic changes in the ring curren...
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| Format: | Article |
| Language: | English |
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Wiley
2024-06-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2023SW003706 |
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| author | Alfredo A. Cruz Rashmi Siddalingappa Piyush M. Mehta Steven K. Morley Humberto C. Godinez Vania K. Jordanova |
| author_facet | Alfredo A. Cruz Rashmi Siddalingappa Piyush M. Mehta Steven K. Morley Humberto C. Godinez Vania K. Jordanova |
| author_sort | Alfredo A. Cruz |
| collection | DOAJ |
| description | Abstract In this work, we address the computational challenge of large‐scale physics‐based simulation models for the ring current. Reduced computational cost allows for significantly faster than real‐time forecasting, enhancing our ability to predict and respond to dynamic changes in the ring current, valuable for space weather monitoring and mitigation efforts. Additionally, it can also be used for a comprehensive investigation of the system. Thus, we aim to create an emulator for the Ring current‐Atmosphere interactions Model with Self‐Consistent magnetic field (RAM‐SCB) particle flux that not only improves efficiency but also facilitates forecasting with reliable estimates of prediction uncertainties. The probabilistic emulator is built upon the methodology developed by Licata and Mehta (2023), https://doi.org/10.1029/2022sw003345. A novel discrete sampling is used to identify 30 simulation periods over 20 years of solar and geomagnetic activity. Focusing on a subset of particle flux, we use Principal Component Analysis for dimensionality reduction and Long Short‐Term Memory (LSTM) neural networks to perform dynamic modeling. Hyperparameter space was explored extensively resulting in about 5% median symmetric accuracy across all data sets for one‐step dynamic prediction. Using a hierarchical ensemble of LSTMs, we have developed a reduced‐order probabilistic emulator (ROPE) tailored for time‐series forecasting of particle flux in the ring current. This ROPE offers accurate predictions of omnidirectional flux at a single energy with no pitch angle information, providing robust predictions on the test set with an error score below 11% and calibration scores under 8% with bias under 2% providing a significant speed up as compared to the full RAM‐SCB run. |
| format | Article |
| id | doaj-art-699780ef7971486cb163ce0ae898949e |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-699780ef7971486cb163ce0ae898949e2025-01-14T16:30:50ZengWileySpace Weather1542-73902024-06-01226n/an/a10.1029/2023SW003706Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle FluxAlfredo A. Cruz0Rashmi Siddalingappa1Piyush M. Mehta2Steven K. Morley3Humberto C. Godinez4Vania K. Jordanova5Department of Mechanical and Aerospace Engineering West Virginia University Morgantown WV USADepartment of Mechanical and Aerospace Engineering West Virginia University Morgantown WV USADepartment of Mechanical and Aerospace Engineering West Virginia University Morgantown WV USASpace Science and Applications Los Alamos National Laboratory Los Alamos NM USAComputational Physics and Methods, Applied Mathematics and Plasma Physics Los Alamos National Laboratory Los Alamos NM USASpace Science and Applications Los Alamos National Laboratory Los Alamos NM USAAbstract In this work, we address the computational challenge of large‐scale physics‐based simulation models for the ring current. Reduced computational cost allows for significantly faster than real‐time forecasting, enhancing our ability to predict and respond to dynamic changes in the ring current, valuable for space weather monitoring and mitigation efforts. Additionally, it can also be used for a comprehensive investigation of the system. Thus, we aim to create an emulator for the Ring current‐Atmosphere interactions Model with Self‐Consistent magnetic field (RAM‐SCB) particle flux that not only improves efficiency but also facilitates forecasting with reliable estimates of prediction uncertainties. The probabilistic emulator is built upon the methodology developed by Licata and Mehta (2023), https://doi.org/10.1029/2022sw003345. A novel discrete sampling is used to identify 30 simulation periods over 20 years of solar and geomagnetic activity. Focusing on a subset of particle flux, we use Principal Component Analysis for dimensionality reduction and Long Short‐Term Memory (LSTM) neural networks to perform dynamic modeling. Hyperparameter space was explored extensively resulting in about 5% median symmetric accuracy across all data sets for one‐step dynamic prediction. Using a hierarchical ensemble of LSTMs, we have developed a reduced‐order probabilistic emulator (ROPE) tailored for time‐series forecasting of particle flux in the ring current. This ROPE offers accurate predictions of omnidirectional flux at a single energy with no pitch angle information, providing robust predictions on the test set with an error score below 11% and calibration scores under 8% with bias under 2% providing a significant speed up as compared to the full RAM‐SCB run.https://doi.org/10.1029/2023SW003706 |
| spellingShingle | Alfredo A. Cruz Rashmi Siddalingappa Piyush M. Mehta Steven K. Morley Humberto C. Godinez Vania K. Jordanova Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux Space Weather |
| title | Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux |
| title_full | Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux |
| title_fullStr | Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux |
| title_full_unstemmed | Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux |
| title_short | Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux |
| title_sort | reduced order probabilistic emulation of physics based ring current models application to ram scb particle flux |
| url | https://doi.org/10.1029/2023SW003706 |
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