A modified version of the commercially available RheOptiCAD® was developed to examine the conformational changes and break-up of flocs in clay suspensions in microscopic detail, under shearing action. In contrast to the RheOptiCAD®, our device aims to enable the structural observation of suspensions even when coagulation or settling of particles onto the bottom plate of the shear cell occurs. This challenge was met by using an upright modular microscope instead of an inverted one, equipped with a CMOS camera to record the structural evolution of samples as a function of shear. The verification of the design was done by analysing a model system of un-flocculated and flocculated kaolin suspensions. Additional experiments with natural clay (mud) suspensions were performed.

We present a neural network (NN) based algorithm for the retrieval of cloud and aerosol properties from above cloud aerosol (ACA) scenes. The large state space explored in ACA scenes causes traditional retrieval approaches slow and complicated. This is especially true for optimal inversion retrieval approaches, where a growth in the number of dependent variables can drastically complicate and slow the retrieval search. Our NN retrieval is applied to data from the airborne Research Scanning Polarimeter (RSP), which measures both polarized and total reflectance in the spectral range of 410 to 2260 nm, scanning along the flight track at ~150 viewing zenith angles spanning the angular range between -60˚ to 60˚. We apply this algorithm to field campaign data from the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) 2016 and 2017 campaigns and compare to results obtained from other algorithms.

We propose an objective threshold determination method for detecting outliers from the empirical distribution of cross-correlation coefficients among seismic waveforms. This method is aimed at detecting seismic signals from continuous waveform records. In our framework, detectability is automatically determined from Akaike’s Information Criterion (AIC). We applied the method of seismic signal detection to continuous records collected over 2 years. The results show that the maximum value of network cross-correlation coefficients sampled from each constant interval can be approximated by the theory of extreme value statistics, which provides a parametric probability density function of maxima. Using the function, outliers can be considered with a reasonable criterion.

The ultimate goal of a scientific investigation is usually to find answers to specific, often low-dimensional questions: what is the size of a subsurface body? Does a hypothesised subsurface feature exist? Existing information is reviewed, an experiment is designed and performed to acquire new data, and the most likely answer is estimated. Typically the answer is interpreted from geological and geophysical data or models, but is biased because only one particular forward function is considered, one inversion method is applied, and because human interpretation is a biased process. Interrogation theory provides a systematic way to answer specific questions by combining forward, design, inverse and decision theories. The optimal answer is made more robust since it balances multiple possible forward models, inverse algorithms and model parametrizations, probabilistically. In a synthetic test, we evaluate the area of a low-velocity anomaly by interrogating Bayesian tomographic results. By combining the effect of four inversion algorithms, the optimal answer is very close to the true answer, even on a coarsely gridded parametrisation. In a field data test, we evaluate the volume of the East Irish Sea basins using 3D shear wave speed depth inversion results. This example shows that interrogation theory provides a useful way to answer realistic questions about the Earth. A key revelation is that while the majority of computation may be spent solving inverse problem, much of the skill and effort involved in answering questions may be spent defining and calculating those target function values in a clear and unbiased manner.

The Super Dual Auroral Radar Network (SuperDARN) currently consists of more than thirty high-frequency (HF, 3–30 MHz) radars covering mid-latitude to polar regions in both hemispheres. Their major task is to map ionospheric plasma circulation which provides information about the interactions between the solar wind and the near-Earth’s space plasma environment. One of the major factors defining radar data quality is the signal-to-noise ratio (SNR), which requires an accurate characterisation of the HF noise. The standard SuperDARN data analysis software uses the SNR as part of a set of empirical procedures designed to remove low-quality data from further analysis. In this study we found that the currently used empirical algorithm systematically underestimates the noise level by up to 40%. Based on comparison of theoretical and observational noise statistics, we resolve this issue by designing and validating a procedure for accurate background noise level estimation. We then propose a simple SNR threshold to replace the existing criteria for excluding low-quality data. In addition, we show that several aspects of the radar operational regime design, as well as short-lived anthropogenic radio interference, can adversely affect the quality of the noise estimates at selected radar sites, and we propose ways to mitigate these problems.

In this study, we use data from the Juno and Galileo spacecraft to analyze the internal magnetic dynamo of Ganymede. As the only known moon with a strong internal magnetic field, Ganymede is a uniquely interesting object in the context of understanding the formation and structure of planetary magnetospheres. Using a spherical harmonic model centered on the moon, we report a dipole approximation for Ganymede of g01 = -716.4 nT, g11 = 56.0 nT, and h11 = 27.0 nT. We find that using a quadrupole fit rather than a dipole fit provides only a marginal increase in accuracy, and instead favor the use of a dipole approximation until more data can be obtained. The magnetic moment estimates provided here can be used as a baseline for interpreting data from future spacecraft flybys of the moon, and can serve as inputs into numerical models studying Ganymede’s magnetosphere.

Ambient air pollution is an increasing threat to society, with rising numbers of adverse outcomes and exposure inequalities across the globe. Reducing uncertainty in health outcomes models and exposure disparity studies is therefore essential to develop policies effective in protecting the most affected places and populations. This study uses the concept of information entropy to study tradeoffs in mortality uncertainty reduction from increasing input data of air pollution versus health outcomes. We study a case scenario for short-term mortality from fine particulate matter (PM2.5) in North Carolina for 2001-2016, employing a case-crossover design with inputs from an individual-level mortality dataset and high-resolution gridded datasets of PM2.5 and weather covariates. We find a significant association between mortality and PM2.5, and the information tradeoffs indicate that in this case increasing information from mortality may reduce model uncertainty at a faster rate than increasing information from air pollution. We also find that Non-Hispanic Black (NHB) residents tend to live in relatively more polluted census tracts, and that the mean PM2.5 for NHB cases in the mortality model is significantly higher than that of Non-Hispanic White (NHW) cases. The distinct distribution of PM2.5 for NHB cases results in a relatively higher information value, and therefore faster uncertainty reduction, for new NHB cases introduced into the mortality model. This newfound influence of exposure disparities in the rate of uncertainty reduction highlights the importance of minority representation in environmental research as a quantitative advantage to produce more confident estimates of the true effects of environmental pollution.

We elucidated the effects of snow and remineralization processes on nutrient distributions in multi-year landfast sea ice (fast ice) in Lützow-Holm Bay, East Antarctica. Based on sea-ice salinity, oxygen isotopic ratios, and thin section analyses, we found that the multi-year fast ice grew upward due to the year-by-year accumulation of snow. Compared to ice of seawater origin, nutrient concentrations in shallow fast ice were low due to replacement by clean and fresh snow. In deeper ice of seawater origin (the lower half of the multi-year fast ice column), remineralization was dominated by the degradation of organic matter. In addition, denitrification was detected in the low brine volume fraction and impermeable multi-year ice due to their disconnection from gas and water exchanges with the atmosphere and under-ice water. By comparison with first-year ice, we develop a conceptual image of the evolution of nutrient concentrations, with biological uptake dominating in relatively young ice and remineralization dominating in older, multi-year ice under the physical process of brine drainage.

Seafloor massive sulfide deposits form in remote environments, and the assessment of deposit size and composition through drilling is technically challenging and expensive. To aid the evaluation of the resource potential of seafloor massive sulfide deposits, three-dimensional inverse modelling of geophysical potential field data (magnetic and gravity) collected near the seafloor can be carried out to further enhance geologic models interpolated from sparse drilling. Here, we present inverse modelling results of magnetic and gravity data collected from the active mound at the Trans-Atlantic Geotraverse hydrothermal vent field, located at 26o08’N on the Mid-Atlantic Ridge, using autonomous underwater vehicle (AUV) and submersible surveying. Both minimum-structure and surface geometry inverse modelling methods were utilized. Through deposit-scale magnetic modelling, the outer extent of a chloritized alteration zone within the basalt host rock below the mound was resolved, providing an indication of the angle of the rising hydrothermal fluid and the depth and volume of seawater/hydrothermal mixing zone. The thickness of the massive sulfide mound was determined by modelling the gravity data, enabling the tonnage of the mound to be estimated at 2.17 +/- 0.44 Mt through this geophysics-based, non-invasive approach.

Heat waves are among the deadliest natural hazards affecting the United States (US). Therefore, understanding the physical mechanisms modulating their occurrence is essential for improving their predictions and future projections. Using observational data and model simulations, this study finds that the interannual variability of the tropical Atlantic warm pool (AWP, measured as the area enclosed by the 28.5°C sea surface temperature isotherm) modulates heat wave occurrence over the US Great Plains during boreal summer. For example, a larger than normal AWP enhances atmospheric convection over the Caribbean Sea, driving an upper tropospheric anticyclonic anomaly over the Gulf of Mexico and Great Plains, which strengthens subsidence, reduces cloud cover, and increases surface warming. This circulation anomaly thus weakens the Great Plains low-level jet (GPLLJ) and associated moisture transport into the Great Plains, leading to drought conditions and increased heat wave occurrence for most of the US east of the Rockies.

The Southern Ocean is chronically under-sampled due to its remoteness, harsh environment and sea-ice cover. Ocean circulation models yield significant insight into key processes and to some extent obviate the dearth of data, however they often underestimate surface mixed layer depth (MLD), with consequences for water-column properties. In this study, a coupled circulation and sea-ice model was implemented for the region adjacent to the West Antarctic Peninsula (WAP), a climatically sensitive region which has exhibited decadal trends toward higher temperatures, a shorter sea-ice season and increasing glacial freshwater input, overlain by strong interannual variability. Hindcast simulations were conducted with different air-ice drag coefficients and Langmuir-circulation parameterizations to determine the impact of these factors on MLD. Including Langmuir circulation deepened the surface mixed layer, with the deepening being more pronounced in the shelf and slope regions. Optimal selection of an air-ice drag coefficient also increased the modeled MLD by similar amounts, and had a larger impact in improving the reliability of the simulated MLD interannual variability. This study highlights the importance of sea ice volume and redistribution to correctly reproduce the physics of the underlying ocean, and the potential of appropriately parameterizing Langmuir circulation to help correct for a bias towards shallow MLD in the Southern Ocean. The model also reproduces observed freshwater patterns in the WAP during late summer and suggests that areas of intense summertime sea-ice melt can still show net annual freezing due to high sea-ice formation during the winter.

Fluctuations in groundwater content may produce surface deformation and affect the elastic properties of the Earth’s crust. In this study we evaluate the temporal variations of the Earth’s crust elastic properties (in the form of relative seismic-velocity variations) in a tectonically active region in Northern Italy characterized by the presence of karst systems. In this area, GPS measurements already revealed hydrologically-induced deformation, modulated by changes in groundwater storage. We study the relation of our seismological observations with the geodetic and hydrological results and identify the effects of groundwater-content variations in the seismic-velocity perturbations. Our results show that hydrologically-induced changes in karstic media produce significant seismic-velocity perturbations, therefore its role in tectonic-stress adjustment studies must not be ignored. Depth sensitivity analysis of our results constrain the crustal perturbations to range between 1 and 4 km depth. Results from scattering imaging locate the crustal perturbations along the main karst systems.

We describe a novel particle telescope, the Advanced enerGetic Ion eLectron tElescope (AGILE), which utilizes full pulse shape discrimination to identify solar energetic particles (SEP), and characterizes their spectra from about 2 to 200 MeV/nuc for most species from H to Fe. AGILE is a compact, low mass, and low-power particle telescope suitable for CubeSat platforms that enable multi-point measurements in interplanetary space. AGILE will employ high heritage solid state detectors, a state-of-art high-speed sampling ASIC, as well as novel algorithms to characterize SEP to advance the understanding of charged particle energization, loss, and transport throughout the heliosphere. AGILE will resolve ion isotopes (e.g. $^3$He vs. $^4$He) with high robustness and reliability. Currently, a prototype of the instrument is being built and will be part of science payload on the GenSat-1 CubeSat. GenSat-1 is expected be launched early 2022 into a high inclination low earth orbit, with AGILE collecting data over the polar regions, i.e., the open field line regions accessed by SEP.

The resolution of climate models is limited by computational cost. Therefore, we must rely on parameterizations to represent processes occurring below the scale resolved by the models. Here, we focus on parameterizations of ocean mesoscale eddies and employ machine learning (ML), namely relevance vector machines (RVM) and convolutional neural networks (CNN), to derive computationally efficient parameterizations from data, which are interpretable and/or encapsulate physics. In particular, we demonstrate the usefulness of the RVM algorithm to reveal closed-form equations for eddy parameterizations with embedded conservation laws. When implemented in an idealized ocean model, all parameterizations improve the statistics of the coarse-resolution simulation. The CNN is more stable than the RVM such that its skill in reproducing the high-resolution simulation is higher than the other schemes; however, the RVM scheme is interpretable. This work shows the potential for new physics-constrained interpretable ML turbulence parameterizations for use in ocean climate models.

Numerous studies have indicated that mesoscale eddies play an important role in diapycnal mixing and drive global water circulation. Although the relationships between eddy surface features and turbulent mixing have been discussed for individual cases of typical eddies, global studies cannot rely on ship-based microstructural measurements. Fine-scale methods have been developed, wherein either the vertical shear of velocity or density strain is used to estimate turbulent mixing. In this study, the turbulent dissipation rate was estimated using strain information from Argo floats trapped in eddies. Spatially averaged estimates revealed the global distribution patterns of the dissipation rates inside the eddies. In addition, the relationships between eddy features (polarity, radius, vertical extent, and aspect ratio) and turbulent dissipation rates were analyzed. Three main conclusions were made from this study. First, turbulent dissipation rates inside anticyclones were generally larger than those inside cyclones. Second, turbulent dissipation rates inside eddies are related to their vertical extent but not to their horizontal scale. For shallow eddies (with a vertical extent less than 250 m for cyclones and 300 m for anticyclones), the deeper the vertical extent, the larger the turbulent dissipation rate. Finally, the relationship between the eddy aspect ratio and turbulent dissipation rate was characterized by skewness. The average turbulent dissipation rate reached a maximum value when the cyclone aspect ratio was approximately 0.007; conversely, no maximum value was reached for anticyclones. This work establishes a correlation between eddy features and turbulent dissipation rates, which will help guide numerical simulations of mesoscale eddies.

Geoscientists use observed data to estimate properties of the Earth’s interior. This often requires non-linear inverse problems to be solved and uncertainties to be estimated. Bayesian inference solves inverse problems under a probabilistic framework, in which uncertainty is represented by a so-called posterior probability distribution. Recently, variational inference has emerged as an efficient method to estimate Bayesian solutions. By seeking the closest approximation to the posterior distribution within any chosen family of distributions, variational inference yields a fully probabilistic solution. It is important to define expressive variational families so that the posterior distribution can be represented accurately. We introduce boosting variational inference (BVI) as a computationally efficient means to construct a flexible approximating family comprising all possible finite mixtures of simpler component distributions. We use Gaussian mixture components due to their fully parametric nature and the ease to optimise. We apply BVI to seismic travel time tomography and full waveform inversion, comparing its performance with other methods. The results demonstrate that BVI achieves reasonable efficiency and accuracy while enabling the construction of a fully analytic expression for the posterior distribution. Samples that represent major components of uncertainty in the solution can be obtained analytically from each mixture component. We demonstrate that these samples can be used to solve an interrogation problem: to assess the size of a subsurface target structure. To the best of our knowledge, this is the first method in geophysics that provides both analytic and reasonably accurate solutions to fully non-linear, high-dimensional Bayesian full waveform inversion problems.

There is much discussion in the literature about the need to balance abilities to respond to short-term and long-term risks among water systems. Yet our understanding of what contributes to our ability to respond to acute as well as long-term change is challenged by insufficient empirical work on water systems to date. We begin to fill this gap through an examination of the capacities of community water systems to absorb short-term shocks as well as to learn from them and act to reduce future risks. We investigated these capacities and how they interact using interviews (n=24) and a survey (n=85) of Connecticut community water systems. We found most systems have increased their capacity to respond to short-term risks and most have some (but differing levels) of capacity for learning from those experiences. However, few are preparing adequately for future risks. We present opportunities for reducing this gap.

The region of northern Borneo in South East Asia sits within a post-subduction setting formed by the recent termination of two sequential but opposed subduction systems. In this study we use seismic data from a recent temporary array deployment to image the crustal velocity structure beneath northern Borneo using a two-stage Bayesian trans-dimensional tomography scheme, in which period dependent phase velocity maps are first generated, and then used to build a 3-D shear wave model through a series of 1-D inversions. In the second stage, we also apply an Artificial Neural Network to solve the 1D inverse problem, which results in a smoother 3-D model compared to the TransD approach without sacrificing data fit. Our shear wave velocity model reveals a complex crustal structure. Under the Crocker Range, a heterogeneous velocity structure likely represents remnants of early Miocene subduction, including underthrust continental crust from subsequent continent-continent collision. In the east we observe high velocities that are interpreted to be igneous rocks in the crust generated by melting due to mid Miocene Celebes Sea subduction and later decompression melting as well as a low velocity zone that could represent underthrust sediment or duplexes from Celebes Sea subduction. A low velocity zone in the lower crust is present in a region of apparent crustal thinning. Our preferred explanation for this anomaly is remnant thermal upwelling within a failed rift that represents the on-shore continuation of the extension of the Sulu Sea, most likely caused by rollback of the Celebes Sea slab.