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

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), . 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. Our study tackles the challenges of simulating the ring current in space and time, using a model called RAM-SCB. By making our simulations faster, we can predict changes in the ring current faster than in real time. This is crucial for monitoring space weather and taking actions to protect satellites and other space assets. Our goal is to create a computer program (emulator) that is efficient, can forecast changes accurately, and gives reliable estimates of its predictions. We follow a method developed by Licata and Mehta (2023), and extend it to the ring current. Discrete sampling is used to pick specific periods for our simulations. We focus on a part of the simulation data and use an advanced method, a hierarchical ensemble of Long Short-Term Memory (LSTM) to build our emulator. This is called a ROPE, that can predict the ring current's behavior by providing forecasts with errors below 11% and calibration scores under 8% (how aligned the predictions are with the actual). A probabilistic emulator for the ring current-atmosphere interactions model with self-consistent magnetic field (RAM-SCB) particle flux is designed with a focus on reducing computational resources of time and space An ensemble of long-short term memory captures temporal variations and provides a probabilistic emulator The emulator makes a week-long prediction for single energy flux in similar to 20 s providing a significant speed-up over the full RAM-SCB run