Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-sensor Analysis, Integration, and Algorithm Development

Estimating snow mechanical properties – such as elastic modulus, stiffness, and strength – is important for understanding how effectively a vehicle can travel over snow-covered terrain. Vehicle instrumentation data and observations of the snowpack are valuable for improving the estimates of winter vehicle performance. Combining in-situ and remotely-sensed snow observations, driver input, and vehicle performance sensors requires several techniques of data integration. I explored correlations between measurements spanning from millimeter to meter scales, beginning with the SnowMicroPenetrometer (SMP) and instruments applied to snow that were designed for measuring the load bearing capacity and the compressive and shear strengths of roads and soils. The spatial distribution of snow’s mechanical properties is still largely unknown. From this initial work, I determined that snow density remains a useful proxy for snowpack strength. To measure snow density, I applied multi-sensor electromagnetic methods. Using spatially distributed snowpack, terrain, and vegetation information developed in the subsequent chapters, I developed an over-snow vehicle performance model. To measure the vehicle performance, I joined driver and vehicle data in the coined Normalized Difference Mobility Index (NDMI). Then, I applied regression methods to distribute NDMI from spatial snow, terrain, and vegetation properties. Mobility prediction is useful for the strategic advancement of warfighting in cold regions. The security of water resources is climatologically inequitable and water stress causes international conflict. Water resources derived from snow are essential for modern societies in climates where snow is the predominant source of precipitation, such as the western United States. Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. In this work, I combined high-spatial resolution light detection and ranging (LiDAR) measured snow depths with ground-penetrating radar (GPR) measurements of two-way travel-time (TWT) to solve for snow density. Then using LiDAR derived terrain and vegetation features as predictors in a multiple linear regression, the density observations are distributed across the SnowEx 2020 study area at Grand Mesa, Colorado. The modeled density resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation. The integration of radar and LiDAR sensors shows promise as a technique for estimating SWE across entire river basins and evaluating observational- or physics-based snow-density models. Accurate estimation of SWE is a means of water security. In our changing climate, snow and ice mass are being permanently lost from the cryosphere. Mass balance is an indicator of the (in)stability of glaciers and ice sheets. Surface mass balance (SMB) may be estimated by multiplying the thickness of any annual snowpack layer by its density. Though, unlike applications in seasonal snowpack, the ages of annual firn layers are unknown. To estimate SMB, I modeled the firn depth, density, and age using empirical and numerical approaches. The annual SMB history shows cyclical patterns representing the combination of atmospheric, oceanic, and anthropogenic climate forcing, which may serve as evaluation or assimilation data in climate model retrievals of SMB. The advancements made using the SMP, multi-channel GPR arrays, and airborne LiDAR and radar within this dissertation have made it possible to spatially estimate the snow depth, density, and water equivalent in seasonal snow, glaciers, and ice sheets. Open access, process automation, repeatability, and accuracy were key design parameters of the analyses and algorithms developed within this work. The many different campaigns, objectives, and outcomes composing this research documented the successes and limitations of multi-sensor estimation techniques for a broad range of cryosphere applications

Assessing Controls on Ice Dynamics at Crane Glacier, Antarctic Peninsula, Using a Numerical Ice Flow Model

The Antarctic Peninsula\u27s widespread glacier retreat and ice shelf collapse have been attributed to atmospheric and oceanic warming. Following the initial post-collapse period of retreat, several former tributary glaciers of the Larsen A and B ice shelves have been slowly re-advancing for more than a decade. Here, we use a flowline model of Crane Glacier to gauge the sensitivity of former tributary glaciers to future climate change following this period of long-term dynamic adjustment. The glacier\u27s long-term geometry and speed changes are similar to those of other former Larsen A and B tributaries, suggesting that Crane Glacier is a reasonable representation of regional dynamics. For the unperturbed climate simulations, discharge remains nearly unchanged in 2018–2100, indicating that dynamic readjustment to shelf collapse took ~15 years. Despite large uncertainties in Crane Glacier\u27s past and future climate forcing, a wide range of future climate scenarios leads to a relatively modest range in grounding line discharge (0.8–1.5 Gt a−1) by 2100. Based on the model results for Crane, we infer that although former ice shelf tributaries may readvance following collapse, similar to the tidewater glacier cycle, their dynamic response to future climate perturbations should be less than their response to ice shelf collapse

Snowpack Relative Permittivity and Density Derived from Near-Coincident Lidar and Ground-Penetrating Radar

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive \u3e20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to \u3e 30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged \u3e 400 mm, however, model uncertainty increased to \u3e 20% when SWE averaged \u3c 300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods

Epigenetic control of nuclear architecture

The cell nucleus is a highly structured compartment where nuclear components are thought to localize in non-random positions. Correct positioning of large chromatin domains may have a direct impact on the localization of other nuclear components, and can therefore influence the global functionality of the nuclear compartment. DNA methylation of cytosine residues in CpG dinucleotides is a prominent epigenetic modification of the chromatin fiber. DNA methylation, in conjunction with the biochemical modification pattern of histone tails, is known to lock chromatin in a close and transcriptionally inactive conformation. The relationship between DNA methylation and large-scale organization of nuclear architecture, however, is poorly understood. Here we briefly summarize present concepts of nuclear architecture and current data supporting a link between DNA methylation and the maintenance of large-scale nuclear organization

Search for pair-produced scalar and vector leptoquarks decaying into third-generation quarks and first- or second-generation leptons in pp collisions with the ATLAS detector

Abstract A search for pair-produced scalar and vector leptoquarks decaying into quarks and leptons of different generations is presented. It uses the full LHC Run 2 (2015–2018) data set of 139 fb −1 collected with the ATLAS detector in proton–proton collisions at a centre-of-mass energy of s $$\sqrt{s}$$ = 13 TeV. Scalar leptoquarks with charge −(1/3)e as well as scalar and vector leptoquarks with charge +(2/3)e are considered. All possible decays of the pair-produced leptoquarks into quarks of the third generation (t, b) and charged or neutral leptons of the first or second generation (e, μ, ν) with exactly one electron or muon in the final state are investigated. No significant deviations from the Standard Model expectation are observed. Upper limits on the production cross-section are provided for eight models as a function of the leptoquark mass and the branching ratio of the leptoquark into the charged or neutral lepton. In addition, lower limits on the leptoquark masses are derived for all models across a range of branching ratios. Two of these models have the goal of providing an explanation for the recent B-anomalies. In both models, a vector leptoquark decays into charged and neutral leptons of the second generation with a similar branching fraction. Lower limits of 1980 GeV and 1710 GeV are set on the leptoquark mass for these two models

Search for flavour-changing neutral-current couplings between the top quark and the photon with the ATLAS detector at root s = 13 TeV

This letter documents a search for flavour-changing neutral currents (FCNCs), which are strongly sup-pressed in the Standard Model, in events with a photon and a top quark with the ATLAS detector. The analysis uses data collected in pp collisions at & RADIC;s =13 TeV during Run 2 of the LHC, corresponding to an integrated luminosity of 139 fb-1. Both FCNC top-quark production and decay are considered. The final state consists of a charged lepton, missing transverse momentum, a b-tagged jet, one high-momentum photon and possibly additional jets. A multiclass deep neural network is used to classify events either as signal in one of the two categories, FCNC production or decay, or as background. No significant ex-cess of events over the background prediction is observed and 95% CL upper limits are placed on the strength of left-and right-handed FCNC interactions. The 95% CL bounds on the branching fractions for the FCNC top-quark decays, estimated (expected) from both top-quark production and decay, are B(t & RARR; u & gamma; ) < 0.85 (0.88+0.37 -0.25) x 10-5 and B(t & RARR; c & gamma; ) < 4.2 (3.40+1.35-0.95) x 10-5 for a left-handed tq & gamma; cou-pling, and B(t & RARR; u & gamma; ) < 1.2 (1.20+0.50 -0.33) x10-5 and B(t & RARR; c & gamma; ) < 4.5 (3.70+1.47 -1.03) x10-5 for a right-handed coupling. & COPY; 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/). Funded by SCOAP3