Search for: All records

Abstract The SOL2013-10-28T02:02:58L133C110 flare occurred on the western limb, acquiring the GOES class of X1.0, and we focus on an oscillatory phenomenon detected at 34 GHz by the Nobeyama Radioheliograph (NoRH) during this flare. The oscillation is less obvious at 17 GHz and is unseen for a hard X-ray source detected by the RHESSI. In the 94 Å images from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory, we traced the evolution of the extreme ultraviolet (EUV) images capturing an eruption around 01:58 UT over the location of the RHESSI 50–100 keV source. We located the microwave emitting loop inferred from the 17/34 GHz maps within the complex EUV loop systems, and performed a model calculation of the dynamic evolution of the microwave brightness, including the radiative transfer in a magnetically asymmetric loop and evolving nonthermal electrons. The results demonstrate that a quasiperiodic injection of energetic electrons at a fixed spatial point is sufficient to reproduce such an oscillatory motion, without an actual shift of the nonthermal electron injection point, and that the magnetic environment required for the microwave loop model is consistent with the observed EUV activities related to the overall reconnection geometry. 

Abstract Plumes are bright structures in coronal holes extending from the solar surface into the corona and are considered as a possible source of the solar wind. Plumes are thought to be rooted in strong unipolar photospheric flux patches (network/plage region). The magnetic activities at the base of plumes may play a crucial role in producing outflows and propagating disturbances (PDs). However, the role of photospheric/chromospheric activities (e.g., jets/spicules) at the base of plumes and their connection to PDs is poorly understood. Using high-resolution observations of a plume taken on 2020 July 23 with the 1.6 m Goode Solar Telescope (GST), Interface Region Imaging Spectrograph (IRIS), and the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory, we analyzed chromospheric/transition region activities at the base of the plume and their connection to outflows/PDs in the plume. The GST Visible Imaging Spectrometer images reveal repetitive spicules with blueshifted emission (pseudo-Doppler maps) at the plume’s footpoint. In addition, the photospheric magnetograms provide evidence of mixed polarities at the base of the plume. The IRIS Mg ii k Dopplergrams show strong blueshifted emission (∼50 km s −1 ) and a high brightness temperature (Mg ii k2 line) at the footpoint of the plume. The long-period PDs ( P ≈ 20–25 minutes) along the plume (AIA 171 Å) match the periodicity of spicules in the chromospheric images, suggesting a close connection between the spicules and the PDs. We suggest that the interchange reconnection between the closed and open flux of the coronal bright point at the plume’s footpoint is the most likely candidate to produce upflows and associated PDs along the plume. 

Aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015) , we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a Big Data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (AI) methodologies, in variants of machine and deep learning, developed particularly for tackling Big Data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and AI methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the plethora of methods is typically far from perfect, with physical reasons to blame, besides practical shortcomings: imperfect data, data gaps and a lack of multiple, and meaningful, vantage points of solar observations. We briefly discuss these issues, too, that shape our desired short- and long-term objectives for an efficient future predictive capability. A central aim of this article is to trigger meaningful, targeted discussions that will compel the community to adopt standards for performance verification and validation, which could be maintained and enriched by institutions such as NASA’s Community Coordinated Modeling Center (CCMC) and the community-driven COSPAR/ISWAT initiative.