Improvements on coronal hole detection in SDO/AIA images using supervised classification

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared data sets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011 - 2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine, Linear Support Vector Machine, Decision Tree, and Random Forest and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ~0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.Comment: in press for SWS

Three Eruptions Observed by Remote Sensing Instruments Onboard Solar Orbiter

On February 21 and March 21 – 22, 2021, the Extreme Ultraviolet Imager (EUI) onboard Solar Orbiter observed three prominence eruptions. The eruptions were associated with coronal mass ejections (CMEs) observed by Metis, Solar Orbiter’s coronagraph. All three eruptions were also observed by instruments onboard the Solar–TErrestrial RElations Observatory (Ahead; STEREO-A), the Solar Dynamics Observatory (SDO), and the Solar and Heliospheric Observatory (SOHO). Here we present an analysis of these eruptions. We investigate their morphology, direction of propagation, and 3D properties. We demonstrate the success of applying two 3D reconstruction methods to three CMEs and their corresponding prominences observed from three perspectives and different distances from the Sun. This allows us to analyze the evolution of the events, from the erupting prominences low in the corona to the corresponding CMEs high in the corona. We also study the changes in the global magnetic field before and after the eruptions and the magnetic field configuration at the site of the eruptions using magnetic field extrapolation methods. This work highlights the importance of multi-perspective observations in studying the morphology of the erupting prominences, their source regions, and associated CMEs. The upcoming Solar Orbiter observations from higher latitudes will help to constrain this kind of study better

SunPy - Python for Solar Physics

This paper presents SunPy (version 0.5), a community-developed Python package for solar physics. Python, a free, cross-platform, general-purpose, high-level programming language, has seen widespread adoption among the scientific community, resulting in the availability of a large number of software packages, from numerical computation (NumPy, SciPy) and machine learning (scikit-learn) to visualisation and plotting (matplotlib). SunPy is a data-analysis environment specialising in providing the software necessary to analyse solar and heliospheric data in Python. SunPy is open-source software (BSD licence) and has an open and transparent development workflow that anyone can contribute to. SunPy provides access to solar data through integration with the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase (HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It currently supports image data from major solar missions (e.g., SDO, SOHO, STEREO, and IRIS), time-series data from missions such as GOES, SDO/EVE, and PROBA2/LYRA, and radio spectra from e-Callisto and STEREO/SWAVES. We describe SunPy's functionality, provide examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing tools already available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy

Reconstruction of the solar EUV irradiance from 1996 to 2010 based on SOHO/EIT images

The solar Extreme UltraViolet (EUV) spectrum has important effects on the Earth’s upper atmosphere. For a detailed investigation of these effects it is important to have a consistent data series of the EUV spectral irradiance available. We present a reconstruction of the solar EUV irradiance based on SOHO/EIT images, along with synthetic spectra calculated using different coronal features which represent the brightness variation of the solar atmosphere. The EIT images are segmented with the SPoCA2 tool which separates the features based on a fixed brightness classification scheme. With the SOLMOD code we then calculate intensity spectra for the 10–100 nm wavelength range and each of the coronal features. Weighting the intensity spectra with the area covered by each of the features yields the temporal variation of the EUV spectrum. The reconstructed spectrum is then validated against the spectral irradiance as observed with SOHO/SEM. Our approach leads to good agreement between the reconstructed and the observed spectral irradiance. This study is an important step toward understanding variations in the solar EUV spectrum and ultimately its effect on the Earth’s upper atmosphere

Improvements on coronal hole detection in SDO/AIA images using supervised classification

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared datasets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011–2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine (SVM), Linear Support Vector Machine, Decision Tree, and Random Forest, and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ≈ 0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method

The Virtual Space Weather Modelling Centre

Abstract The englacial stratigraphic architecture of internal reflection horizons (IRHs) as imaged by ice‐penetrating radar (IPR) across ice sheets reflects the cumulative effects of surface mass balance, basal melt,andice flow.IRHs,consideredisochrones,havetypicallybeentracedininterior,slow‐flowingregions. Here, we identify three distinctive IRHs spanning the Institute and Möller catchments that cover 50% of West Antarctica's Weddell Sea Sector and are characterized by a complex system of ice stream tributaries. WeplaceageconstraintsonIRHsthroughtheirintersectionswithpreviousgeophysicalsurveystiedtoByrd IceCoreandbyage‐depthmodeling.Wefurthershowwheretheoldesticelikelyexistswithintheregionand that Holocene ice‐dynamic changes were limited to the catchment's lower reaches. The traced IRHs from this study have clear potential to nucleate a wider continental‐scale IRH database for validating ice sheet models. Plain Language Summary Ice‐penetrating radar is widely used to measure the thickness of ice sheets, critical to assessments of global sea level rise potential. This technique also captures reflections fromchemicalcontrastswithintheicesheet,causedbytheatmosphericdepositionofconductiveimpurities, knownas “internalreflectionhorizons” (IRHs)thatcanbetracedoverlargedistances.Asthese depositsare laid down in distinct events, most IRHs are isochronous age tracers and contain valuable information on past ice sheet processes. In this paper we trace and place age constraints on stratigraphic horizons across a large portion of the West Antarctic Ice Sheet, including regions where fast ice flow has disrupted the ice sheet stratigraphy. The resulting data set allows us to identify where the oldest ice is buried in the study region and provides evidence that flow of the ice sheet interior has been stable during the Holocene. Our results can be used to test the performance of ice sheet models, which seek to simulate the response of ice sheets to long‐term environmental change

Three eruptions observed by remote sensing instruments onboard solar orbiter

On February 21 and March 21 – 22, 2021, the Extreme Ultraviolet Imager (EUI) onboard Solar Orbiter observed three prominence eruptions. The eruptions were associated with coronal mass ejections (CMEs) observed by Metis, Solar Orbiter’s coronagraph. All three eruptions were also observed by instruments onboard the Solar–TErrestrial RElations Observatory (Ahead; STEREO-A), the Solar Dynamics Observatory (SDO), and the Solar and Heliospheric Observatory (SOHO). Here we present an analysis of these eruptions. We investigate their morphology, direction of propagation, and 3D properties. We demonstrate the success of applying two 3D reconstruction methods to three CMEs and their corresponding prominences observed from three perspectives and different distances from the Sun. This allows us to analyze the evolution of the events, from the erupting prominences low in the corona to the corresponding CMEs high in the corona. We also study the changes in the global magnetic field before and after the eruptions and the magnetic field configuration at the site of the eruptions using magnetic field extrapolation methods. This work highlights the importance of multi-perspective observations in studying the morphology of the erupting prominences, their source regions, and associated CMEs. The upcoming Solar Orbiter observations from higher latitudes will help to constrain this kind of study better.Fil: Mierla, Marilena. Royal Observatory Of Belgium (rob);Fil: Cremades Fernandez, Maria Hebe. Universidad de Mendoza. Facultad de Ingenieria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Andretta, Vincenzo. Istituto Nazionale di Astrofisica; ItaliaFil: Chifu, Iulia. Universität Göttingen; AlemaniaFil: Zhukov, Andrei N.. Royal Observatory Of Belgium (rob);Fil: Susino, Roberto. Istituto Nazionale di Astrofisica; ItaliaFil: Auchère, Frédéric. Universite Paris-saclay (universite Paris-saclay);Fil: Vourlidas, Angelos. University Johns Hopkins; Estados UnidosFil: Talpeanu, Dana Camelia. Royal Observatory Of Belgium (rob);Fil: Rodriguez, Luciano. Royal Observatory Of Belgium (rob);Fil: Janssens, Jan. Royal Observatory Of Belgium (rob);Fil: Nicula, Bogdan. Royal Observatory Of Belgium (rob);Fil: Aznar Cuadrado, Regina. Max-Planck-Institut für Sonnensystemforschung; AlemaniaFil: Berghmans, David. Royal Observatory Of Belgium (rob);Fil: Bemporad, Alessandro. Istituto Nazionale di Astrofisica; ItaliaFil: D’Huys, Elke. Royal Observatory Of Belgium (rob);Fil: Dolla, Laurent. Royal Observatory Of Belgium (rob);Fil: Gissot, Samuel. Royal Observatory Of Belgium (rob);Fil: Jerse, Giovanna. Royal Observatory Of Belgium (rob);Fil: Kraaikamp, Emil. Royal Observatory Of Belgium (rob);Fil: Long, David M.. The Queens University of Belfast; IrlandaFil: Mampaey, Benjamin. Royal Observatory Of Belgium (rob);Fil: Möstl, Christian. Austrian Space Weather Office; AustriaFil: Pagano, Paolo. Università degli Studi di Palermo; Italia. Istituto Nazionale di Astrofisica; ItaliaFil: Parenti, Susanna. Universite Paris-saclay (universite Paris-saclay);Fil: West, Matthew J.. Southwest Research Institute.; Estados UnidosFil: Podladchikova, Olena. National Polytechnic University of Ukraine; Ucrania. Leibniz Institute for Astrophysics Potsdam; AlemaniaFil: Romoli, Marco. Università di Firenze; Italia. Istituto Nazionale di Astrofisica; ItaliaFil: Sasso, Clementina. Istituto Nazionale di Astrofisica; ItaliaFil: Stegen, Koen. Royal Observatory Of Belgium (rob);Fil: Teriaca, Luca. Max-Planck-Institut für Sonnensystemforschung; AlemaniaFil: Thompson, William. National Aeronautics and Space Administration; Estados UnidosFil: Verbeeck, Cis. Royal Observatory Of Belgium (rob);Fil: Davies, Emma. University of New Hampshire; Estados Unido