Ellianna Abrahams

The NASA/CNES Surface Water and Ocean Topography (SWOT) swath‐mapping radar altimetry satellite mission, launched in December 2022, provides repeat‐track coverage to about ±78.2° latitude every 21 days, sampling many of Antarctica's most dynamic ice shelves that play key roles in constraining mass loss from the grounded ice sheet. Understanding how ice‐shelf extent and thickness respond to forcings requires three‐dimensional mapping of ice structure at the short time and space scales of major mass change processes including basal and surface mass balances, and crevasse growth. We demonstrate that repeated SWOT high‐rate acquisitions can accurately monitor changes in ice‐shelf surface heights, revealing key processes including responses to ocean tides and atmospheric forcing, ice flexure along grounding zones, and crevasse deepening, that affect stresses impacting ice loss. Despite limitations in acquisition and processing, some of which are being addressed, the SWOT mission provides exciting new opportunities for studying ice‐shelf dynamics.

Proprietary development solutions are often perceived as being delivered more quickly and easily than open methods, fueling the misconception that they inherently produce better overall outcomes. However, we argue that open development practices can lead to both efficient and maximally impactful outcomes. One successful strategy for accelerating open approaches in the geosciences is co‐creation, a model that emphasizes shared ownership between developers and users. This approach creates a collaborative flywheel dynamic—pioneered in the geosciences by the Pangeo project—that encourages teamwork, standardizes access to shared infrastructure, and facilitates iterations of stakeholder feedback and development to accelerate solution finding.

Subglacial processes exert a major control on ice streaming. Constraining subglacial conditions thus allows for more accurate predictions of ice mass loss. Due to the difficulty in observing large‐scale conditions of the modern subglacial environment, we turn to geological records of ice streaming in deglaciated environments. Morphometric values of streamlined subglacial bedforms provide valuable information about the relative speed, direction, and maturity of past ice streams and the relationship between ice streaming and subglacial erosion and deposition. However, manually identifying streamlined subglacial bedforms across deglaciated landscapes, sometimes in clusters of several thousand, is an arduous task with difficult‐to‐control sources of variability and human‐biased errors. This paper presents a new tool that utilizes a machine learning approach to automatically identify glacially derived streamlined features. Slope variations across a landscape, identified by topographic position index, undergo analysis from a series of supervised machine learning models trained from over 600 000 data points identified across the deglaciated Northern Hemisphere. A filtered data set produced through the combination of scientifically driven preprocessing and statistical downsampling improved the robustness of our approach. After cross‐validation, we found that Random Forest detected the most true positives, up to 94.5% on a withheld test set, and an ensemble average of machine learning models provided the highest stability when applied within the range of applicable data sets, performing at up to 79% identification of true positives on an out of distribution area of interest. We build these models into an open‐source Python package, bedfinder, and apply it to new data in the Green Bay Lobe region, USA, finding the general ice‐flow direction and average streamlined subglacial bedform elongation with minimal effort. This type of open, reproducible machine learning analysis is at the leading edge of glacial geomorphology research and will continue to improve with integration of newly acquired and previously collected data.

Single spacecraft missions do not measure the pristine solar wind continuously because of the spacecrafts' orbital trajectory. The infrequent spatiotemporal cadence of measurement fundamentally limits conclusions about solar wind‐magnetosphere coupling throughout the solar system. At Mars, such single spacecraft missions result in limitations for assessing the solar wind's role in causing lower altitude observations, such as auroral dynamics or atmospheric loss. In this work, we detail the development of a virtual solar wind monitor from the Mars Atmosphere and Volatile Evolution (MAVEN) mission; a single spacecraft. This virtual solar wind monitor provides a continuous estimate of the solar wind upstream from Mars with uncertainties. We specifically employ Gaussian process regression to estimate the upstream solar wind and uncertainty estimations that scale with the data sparsity of our real observations. This proxy enables continuous solar wind estimation at Mars with representative uncertainties for the majority of the time since late 2014. We conclude by discussing suggested uses of this virtual solar wind monitor for statistical studies of the Mars space environment and heliosphere.

We propose a new tiling strategy, Flip-n-Slide, which has been developed for specific use with large Earth observation satellite images when the location of objects-of-interest (OoI) is unknown and spatial context can be necessary for class disambiguation. Flip-n-Slide is a concise and minimalistic approach that allows OoI to be represented at multiple tile positions and orientations. This strategy introduces multiple views of spatio-contextual information, without introducing redundancies into the training set. By maintaining distinct transformation permutations for each tile overlap, we enhance the generalizability of the training set without misrepresenting the true data distribution. Our experiments validate the effectiveness of Flip-n-Slide in the task of semantic segmentation, a necessary data product in geophysical studies. We find that Flip-n-Slide outperforms the previous state-of-the-art augmentation routines for tiled data in all evaluation metrics. For underrepresented classes, Flip-n-Slide increases precision by as much as 15.8%.

Sensible-heat polynyas, i.e. persistent polynyas produced thermodynamically, can form across multiple years where relatively warm water flows out from the sub-ice-shelf cavity toward ice shelf fronts, typically directed by channels carved into the ice-shelf base. Polynyas represent an important component of the interactions between ice shelves, channel water outflows, and ambient ocean properties and circulation. However, time-resolved investigations of persistent polynyas are sparse across Antarctica, due to a dearth of field observations and the complexity of identifying their extent and evolution in remotely sensed images. The challenge, particularly along the coastal margin of the West Antarctic Ice Sheet is not one of a paucity of data, but one of a plethora of pixels lacking classification; typically, an expert is required to label pixels belonging to ice shelf, iceberg, sea ice, open ocean, polynya, or a mixture. Here, we engineer a physics-featurized convolutional neural network with an approach that allows us to predict pixel classifications even in the case where expertly labeled data are sparse. We demonstrate the potential of physics-featurized neural networks to robustly automate complex classification tasks, such as pixel-based labeling of remote sensing imagery, that are often a necessary stepping stone to answering more specific scientific questions across the cryosphere. 

The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time domain survey cadences and the training of classification models on data sets with few to no labels. Traditional data augmentation techniques expand training sets by reenvisioning observed exemplars, seeking to simulate observations of specific training sources under different (exogenous) conditions. Unlike fully theory-driven models, these approaches do not typically allow principled interpolation nor extrapolation. Moreover, the principal drawback of theory-driven models lies in the prohibitive computational cost of simulating source observables from ab initio parameters. In this work, we propose a computationally tractable machine learning approach to generate realistic light curves of periodic variables capable of integrating physical parameters and variability classes as inputs. Our deep generative model, inspired by the transparent latent space generative adversarial networks, uses a variational autoencoder (VAE) architecture with temporal convolutional network layers, trained using the OGLE-III optical light curves and physical characteristics (e.g., effective temperature and absolute magnitude) from Gaia DR2. A test using the temperature–shape relationship of RR Lyrae demonstrates the efficacy of our generative “physics-enhanced latent space VAE” (PELS-VAE) model. Such deep generative models, serving as nonlinear nonparametric emulators, present a novel tool for astronomers to create synthetic time series over arbitrary cadences.

The orbital-period (P _orb) gap in the population of cataclysmic variables (CVs) informs the theoretical narrative of CV evolution, yet a complete understanding of the driving angular momentum loss mechanisms above and below this gap remains elusive. Here we identify, for standard CVs, a new, apparently monotonic relationship between quiescent color (G _BP–G _RP), absolute magnitude (M _G ), and P _orb (between 70 minutes and 8 hr) revealed in Gaia DR2 and EDR3. We show that P _orb increases in the color–absolute-magnitude diagram roughly orthogonally to the white dwarf and main sequences. We find the orbital-period–color–absolute-magnitude relationship to be stable across different CV subtypes: dwarf novae, intermediate polars, polars, and novalike systems. We place our findings in context with the known semiempirical donor sequence for CVs and find a dependence between color and M _G for a given P _orb specifically for dwarf novae and intermediate polars above the period gap. These relations have the potential to inform a more complete picture of CV evolution.

We present final Spitzer trigonometric parallaxes for 361 L, T, and Y dwarfs. We combine these with prior studies to build a list of 525 known L, T, and Y dwarfs within 20 pc of the Sun, 38 of which are presented here for the first time. Using published photometry and spectroscopy as well as our own follow-up, we present an array of color–magnitude and color–color diagrams to further characterize census members, and we provide polynomial fits to the bulk trends. Using these characterizations, we assign each object a T _eff value and judge sample completeness over bins of T _eff and spectral type. Except for types ≥T8 and T _eff < 600 K, our census is statistically complete to the 20 pc limit. We compare our measured space densities to simulated density distributions and find that the best fit is a power law ( ) with α = 0.6 ± 0.1. We find that the evolutionary models of Saumon & Marley correctly predict the observed magnitude of the space density spike seen at 1200 K < T _eff < 1350 K, believed to be caused by an increase in the cooling timescale across the L/T transition. Defining the low-mass terminus using this sample requires a more statistically robust and complete sample of dwarfs ≥Y0.5 and with T _eff < 400 K. We conclude that such frigid objects must exist in substantial numbers, despite the fact that few have so far been identified, and we discuss possible reasons why they have largely eluded detection.