Summary
High-speed solar wind streams from the Sun can affect Earth’s magnetic environment. These streams come from dark areas called coronal holes that show up in special solar images. The authors used a method called Topological Data Analysis (TDA) to study the shape and connectivity of these dark areas to see if they could predict when the solar wind streams would arrive at Earth four days later. Their results show that just the topological features of the images carry useful information for forecasting these solar wind events. They also created a way to explain how the topological data links to the predictions, making their approach more understandable.
What this means in practice
- •For space weather forecasters: Improve early warnings of solar wind-driven geomagnetic disturbances using topology-based analysis of solar images four days in advance.
- •For satellite operators: Enhance planning for satellite risk mitigation by using topological features of solar observations to predict high-speed solar wind impacts before they occur.
Abstract
High-speed solar wind streams (HSSs) originating from coronal holes (CHs) are an important driver of recurrent geomagnetic activity. Since HSSs are associated to coronal structures visible in extreme-ultraviolet observations, this provides a natural basis for forecasting several days in advance. In this work, we investigate whether the topology of coronal structures observed in extreme ultraviolet (EUV) images contains predictive information on the subsequent occurrence of CH-driven HSSs at Earth. To this end, we consider Topological Data Analysis (TDA), and in particular persistent homology, to classify EUV solar images according to the occurrence of HSSs at Earth four days later. The target is a binary classification of HSS occurrence at a four-day lead time, defined using an HSS interval catalog and solar wind speed thresholds. Several combinations of topological representations and classifiers are evaluated under a common cross-validation framework. The results indicate that the topology of EUV coronal structures alone contains predictive information on subsequent CH-driven HSS occurrence, despite the absence of explicit CH geometric descriptors in the model input. To investigate the origin of this predictive information, we develop a model-level explainability framework based on cross-validation-aggregated feature weights and spatial evidence maps. A fully traced example connects persistence diagrams and persistence-image features to the classifier output and to their spatial localization in the original EUV observation. The proposed framework provides a transparent and computationally lightweight approach for investigating the relationship between coronal topology and subsequent high-speed solar wind streams.