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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.

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in solar extreme ultraviolet (EUV) images recorded by the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory. We used the Spatial Possibilistic Clustering Algorithm to prepare datasets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2010–16. By mapping the extracted regions from EUV observations onto Helioseismic and Magnetic Imager (HMI) line-of-sight magnetograms, we also include their magnetic characteristics. We computed average latitude, area, and 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 best rule for differentiating between coronal holes and filament channels, taking into account the imbalance in our dataset, which contains 1 filament channel for 15 coronal holes. We tested classifiers such as support vector machine (SVM), linear SVM, decision tree, k -nearest neighbors, as well as an ensemble classifier based on decision trees. The best performance in terms of true skill statistics is obtained with cost-sensitive learning, SVM classifiers, and when HMI attributes are included in the dataset.

Since the Solar Dynamics Observatory (SDO) began recording ≈ 1 TB of data per day, there has been an increased need to automatically extract features and events for further analysis. Here we compare the overall detection performance, correlations between extracted properties, and usability for feature tracking of four solar feature-detection algorithms: the Solar Monitor Active Region Tracker (SMART) detects active regions in line-of-sight magnetograms; the Automated Solar Activity Prediction code (ASAP) detects sunspots and pores in white-light continuum images; the Sunspot Tracking And Recognition Algorithm (STARA) detects sunspots in white-light continuum images; the Spatial Possibilistic Clustering Algorithm (SPoCA) automatically segments solar EUV images into active regions (AR), coronal holes (CH), and quiet Sun (QS). One month of data from the Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) and SOHO/Extreme Ultraviolet Imaging Telescope (EIT) instruments during 12 May – 23 June 2003 is analysed. The overall detection performance of each algorithm is benchmarked against National Oceanic and Atmospheric Administration (NOAA) and Solar Influences Data Analysis Center (SIDC) catalogues using various feature properties such as total sunspot area, which shows good agreement, and the number of features detected, which shows poor agreement. Principal Component Analysis indicates a clear distinction between photospheric properties, which are highly correlated to the first component and account for 52.86% of variability in the data set, and coronal properties, which are moderately correlated to both the first and second principal components. Finally, case studies of NOAA 10377 and 10365 are conducted to determine algorithm stability for tracking the evolution of individual features. We find that magnetic flux and total sunspot area are the best indicators of active-region emergence. Additionally, for NOAA 10365, it is shown that the onset of flaring occurs during both periods of magnetic-flux emergence and complexity development.

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 visualization and plotting (matplotlib). SunPy is a data-analysis environment specializing 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 SunPyʼs 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.

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.