A multimodal tiered magnetic polarity inversion features dataset for space weather forecasting

Area of Science:

• * Heliophysics and Space Weather Research
• * Solar Physics
• * Magnetohydrodynamics

Background:

• * Understanding solar magnetic field evolution is crucial for space weather prediction.
• * Magnetic Polarity Inversion Lines (MPILs) are key indicators of solar activity.
• * Previous datasets lacked the multi-tiered detail necessary for nuanced analysis.

Purpose of the Study:

• * To introduce a comprehensive, publicly available, multimodal, tiered MPILs dataset.
• * To provide researchers with detailed magnetic polarity information for diverse applications.
• * To support advancements in heliophysics and space weather forecasting.

Main Methods:

• * Extraction of MPILs from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) Active Region Patches (HARP) data.
• * Application of a detection methodology with four distinct magnetic field strength thresholds.
• * Generation of four tiers of MPIL data, including binary masks and multivariate time series metadata.
• * Mapping of 6,695 HARP series using Lambert Cylindrical Equal Area (CEA) projection at a 12-minute cadence.

Main Results:

• * A novel, tiered MPILs dataset spanning May 2010 to April 2025.
• * Four tiers of data capturing MPIL features at multiple levels of detail.
• * Six types of binary masks per tier (MPIL, RoPI, polarity regions, convex hulls).
• * Inclusion of structured multivariate time series metadata for temporal analysis.

Conclusions:

• * The tiered dataset offers unprecedented detail for studying magnetic field changes on the Sun.
• * This resource facilitates tailored analyses for various scientific and operational objectives.
• * The dataset is a valuable tool for improving heliophysics research and space weather forecasting capabilities.