Speaker
Description
Accurate forecasting of Earth-directed Coronal Mass Ejections (CMEs) and their geo-effectiveness requires advanced early warning capabilities that exceed the limitations of current single-point L1 monitors. Sub-L1 multi-spacecraft architectures offer a highly promising solution; however, architecting these missions requires robust, end-to-end simulation environments to evaluate payload configurations and ground-segment algorithms prior to deployment.
In this contribution, we present a novel software pipeline designed to comprehensively simulate and analyze data from sub-L1 multi-spacecraft space weather missions. The software generates a realistic 2D spatial domain featuring an intermittent and turbulent background solar wind. This background is dynamically populated with key large-scale structures, including Corotating Interaction Regions (CIRs), Heliospheric Current Sheet (HCS) crossings, and transient CMEs. By modeling these interplanetary structures in 2D, the tool accurately reproduces the distinct, location-dependent solar wind conditions encountered by a distributed constellation of spacecraft.
To bridge the gap between physical models and mission engineering, the pipeline incorporates instrument transfer functions, effectively translating the simulated space environment into realistic, telemetry-like in-situ sensor responses. Furthermore, the software features an integrated ground-segment analysis module tailored for multi-point data. This module demonstrates advanced operational techniques, including the use of magnetic helicity for robust automated CME detection. Crucially, the software implements geometric interferometry across the simulated spacecraft network to accurately estimate the propagation direction of CMEs—a vital measurement for predicting Earth-impact that is fundamentally impossible with traditional single-spacecraft observations.
Ultimately, this software suite serves as a powerful mission planning tool. We will demonstrate how it can be utilized to define strict scientific requirements for CME early warning, optimize scientific payload configurations, and validate the most effective data analysis techniques for estimating CME propagation and geo-effectiveness at Earth.