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arXiv · 2109.06960

OSPREI: A Coupled Approach to Modeling CME-Driven Space Weather with Automatically-Generated, User-Friendly Outputs

C. Kay·M. L. Mays·Y. M. Collado-Vega
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Abstract

Coronal Mass Ejections (CMEs) drive space weather activity at Earth and throughout the solar system. Current CME-related space weather predictions rely on information reconstructed from coronagraphs, sometimes from only a single viewpoint, to drive a simple interplanetary propagation model, which only gives the arrival time or limited additional information. We present the coupling of three established models into OSPREI (Open Solar Physics Rapid Ensemble Information), a new tool that describes Sun-to-Earth CME behavior, including the location, orientation, size, shape, speed, arrival time, and internal thermal and magnetic properties, on the timescale needed for forecasts. First, ForeCAT describes the trajectory that a CME takes through the solar corona. Second, ANTEATR simulates the propagation, including expansion and deformation, of a CME in interplanetary space and determines the evolution of internal properties via conservation laws. Finally, FIDO produces in situ profiles for a CME's interaction with a synthetic spacecraft. OSPREI includes ensemble modeling by varying each input parameter to probe any uncertainty in their values, yielding probabilities for all outputs. Standardized visualizations are automatically generated, providing easily-accessible, essential information for space weather forecasting. We show OSPREI results for CMEs observed in the corona on 2021 April 22 and 2021 May 09. We approach these CME as a forecasting proof-of-concept, using information analogous to what would be available in real time rather than fine-tuning input parameters to achieve a best fit for a detailed scientific study. The OSPREI prediction shows good agreement with the arrival time and in situ properties.

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C. Kay, M. L. Mays, Y. M. Collado-Vega. 2021-09-14. OSPREI: A Coupled Approach to Modeling CME-Driven Space Weather with Automatically-Generated, User-Friendly Outputs. https://doi.org/10.1029/2021sw002914

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