Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux

Journal Article · Sat Jun 15 00:00:00 UTC 2024 · Space Weather (Online)

DOI: https://doi.org/10.1029/2023SW003706 · OSTI ID:2373349

Cruz, Alfredo A. ^[1]; Siddalingappa, Rashmi ^[1]; Mehta, Piyush M.  ^[1]; Morley, Steven K.  ^[2]; Godinez, Humberto C.  ^[3]; Jordanova, Vania K.  ^[2]

---

1. Department of Mechanical and Aerospace Engineering West Virginia University Morgantown WV USA
2. Space Science and Applications Los Alamos National Laboratory Los Alamos NM USA
3. Computational Physics and Methods, Applied Mathematics and Plasma Physics Los Alamos National Laboratory Los Alamos NM USA

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.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: National Aeronautics and Space Administration (NASA); National Science Foundation (NSF); USDOE; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001; SC0020294
• OSTI ID: 2373349
• Report Number(s): LA-UR--24-23895; e2023SW003706
• Journal Information: Space Weather (Online), Journal Name: Space Weather (Online) Journal Issue: 6 Vol. 22; ISSN 1542-7390
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English