Reusing produced water for hydraulic fracturing simultaneously satisfies challenges of fresh water sourcing and the design/operation of an extensive disposal well infrastructure. This presentation provides an overview of a reuse program from concept through implementation including qualification of advanced water recycling technologies. We target the most prolific unconventional reservoir play in the United States – the Permian Basin. Sourcing water for full-field development therein represents a significant problem since it is in short supply in (semi)arid regions of West Texas and Southeastern New Mexico. We report results from a synergistic industry-academia collaboration wherein desalination pretreatment was first evaluated at lab scale to (i) systematically evaluate partial softening (i.e. “floc-and-drop”) versus neutral pH oxidation for iron removal (ii) investigate synergistic effects of FeCl3 and polymer addition to destabilize colloids (including particulate iron) and induce high-rate sedimentation and (iii) develop and implement robust techniques using video and image analysis to characterize process performance and floc properties (e.g. morphology, size, and settling velocity). Jar tests and associated measurements were completed in the range 4 - 44 ºC covering the range of temperatures measured in the Permian. FeCl3 in conjunction with an anionic polymer dramatically improved colloid destabilization and floc growth via enmeshment of primary colloids by amorphous iron precipitates and inter-particle bridging by the adsorbed polymer. Larger, stronger, and denser flocs thus formed settled extremely rapidly without breakage (i.e. high rate sedimentation). Bench-scale results were integrated in the design and testing a 5,000 BPD pilot scale high-rate clarifier. Pilot scale results show that the neutral pH method of clean-brine generation produced 5-10 times less sludge while achieving 15-20% higher throughput over the alternative floc-and-drop method. Both bench- and pilot-scale findings were incorporated in design and operation of a 50,000 BPD full-scale reuse facility in the Permian Basin. This presentation will share lessons learned from operating a large-scale reuse facility and how academic research can inform and be motivated by industrial practices (and vice versa).

The precise spectroscopic identification of mineral polytypes and specific organic molecules is key to understanding planetary processes and the potential for life beyond Earth in the solar system. For in situ exploration, Raman spectroscopy has been chosen for the NASA Mars Perseverance Rover and upcoming ESA ExoMars missions because it is an information-rich, non-contact, non-destructive method for identifying and characterizing compounds. Misinterpretation of Raman spectra can result in the misidentification of key information used to reconstruct environmental regimes or the detection of potential biosignatures. Machine learning can provide a means to disentangle the mixed signatures that occur in spectra from heterogenous targets by building algorithms capable of discerning subtle differences. Here we discuss an approach that incorporates a Matlab-based machine learning algorithm to study individual mineral samples as a starting point for more complex algorithms targeted for rocks and sediments. The present study focuses on Raman spectroscopy using visible (VIS) excitation laser (514 nm and 532 nm) and a near IR (NIR) excitation laser (at 780 nm) of an assortment of mineral samples typical for rocks on Mars and the Moon, namely olivines, three types of plagioclase minerals (anorthite, bytownite, labradorite), and pyroxenes (augite and enstatite). We have also begun to study the effect of temperature on the vibrational modes for the same mineral samples over a temperature range 300 – 473 K under NIR excitation. Our preliminary data show, for example, that olivine samples from two different locations may exhibit the same typical symmetric and asymmetric stretch and bending vibrations for forsterite (Mg2SiO4); however, under increasing temperatures the peak intensities of ~ 820 cm-1 and ~ 845 cm-1 features exhibited by each sample differed. Our results also showed an enhancement of the Raman peak intensity for plagioclase samples as the temperature increased up to 373K, but a decrease at temperatures beyond that. *Acknowledgments: P. Misra and R. Coleman, Jr. acknowledge support from NASA Award # 80NCCS20M0019, NSF Award # PHY 1950379 & Howard University IDCR # U100189; and D. Bower would like to acknowledge the support of the Internal Research and Development and Fundamental Laboratory Research Programs at NASA Goddard.

Mantle transition zone (MTZ) is the layer between two seismic upper mantle discontinuities, which are generally believed to be caused by the transformation of high-pressure polymorphs of olivine occurred around depth of 410 km (d410) and 660 km (d660). The fine structure around the discontinuities, especially the undulation of them can be used to infer the variation of temperature and/or composition in the deep mantle. Seismic images have revealed a subhorizontally deflected slab beneath Northeast China, and thus the seismic mapping of the interaction between the slab and the deep mantle is important in understanding the mantle circulation as well as the upper mantle composition. Recently, a temporary NECsaids Array has been installed, and it consists mainly of two nearly orthogonal dense profiles with station spacing of 15–20 km, which provides us an unprecedented opportunity to obtain a high-resolution 3D topography of the MTZ. Considering the dipping and stagnant structure of the slab, we used a novel Ps scatter kernel migration technique to obtain the detailed image of the d410 and d660, which has been proved to be efficient and better for more complex structures. Our results show that the boundaries of the subducting Northwest Pacific slab can be clearly identified in the image, with a dip angle of about 30° in the MTZ. The depth of the d660 depressed 20 - 40 km around the stagnant part of the subducting slab, which could be explained with the lower temperature caused by the cold Pacific slab. Moreover, the d660 is uplifted about 5 - 15 km beneath northwest of Changbaishan and Longgang Volcano, suggesting that the upwelling feeding the volcanoes might originate from the MTZ. Apart from the prominent depression of the d410 observed in the volcanic area, we also observed a wide-distributed depression of the d410 with value ~10 km over most studied areas, seeming inconsistent with the thermal anomaly associated with the subduction environment. We argue that subtle variations of water and/or chemical composition contribute to the observed depth variation of the d410, and the apparent depressed “410” can be related to the wet/dry wadsleyite boundary, which might be caused by the high water content at the top of the MTZ.

The recognition of data as a natural resource has made headlines in the new era of industrialization. Companies now leverage this resource to enhance their services and products, often promising optimal outcomes. While data has always held significant value, recent advancements in AI and ML frameworks have brought this fact to the forefront. However, it was not until major scandals involving large corporations came to light that the critical issue of privacy was remembered. Consequently, the intersection of data, AI and ML frameworks, and privacy has emerged as a new area of research. Although numerous works have reviewed the development of various data protection techniques, it seems that most of them address the subject from a single perspective or attribute the entire concept of data privacy to a specific technique. This review aims to present an overarching view of the topic. It offers a systematic guideline that establishes a proper connection among the three elements: data, AI & ML frameworks, and privacy. The paper delves into each element from both an abstract and concrete standpoint, presenting the latest techniques to address data privacy concerns, including numerous lab simulations. It also recommends tools and resources for further study. Ultimately, it wraps up the central topic by outlining the challenges and prospective future research directions. Keywords: Data Privacy; AI & ML; Differential Privacy; Federated Learning;

The importance of measurement and modelling of the coronal magnetic fields has been appreciated for a long time, and in view of the fact that magnetic fields of Coronal Mass Ejections (CMEs) play a crucial role in determining their geo-effectiveness, this also has considerable societal impact. The coronal magnetic fields are, however, very hard to measure directly. Unlike those at play in X-ray, EUV and optical regimes, radio emission mechanisms are sensitive to the local magnetic fields and hence can potentially lead to their measurements. In practise, however, this potential has been hard to realize. To the best of our knowledge, in the past eighteen years, there have been only three published instances of radio detection of CME like structures. Only for two of these instances, it was possible to estimate coronal magnetic fields by fitting the measured spectra with gyrosynchrotron models. Using data from the Murchison Widefield Array (MWA), for two CMEs we have detected radio structures resembling the CME morphology. These structures are cospatial and cotemporal with the white light coronagraph images of the CME and we can convincingly demonstrate that this is not plasma emission. The maximum heliocentric distance where we can detect such emission is 4.74 solar radii, and the flux densities we measure are among the lowest reported. We note that these detections have been enabled by the confluence of the availability of data from the modern arrays like the MWA; and an automated interferometric solar radio imaging which we have developed. The MWA provides unprecedentedly dense sampling of Fourier (uv) plane, an essential pre-requisite for high imaging fidelity; and the imaging pipeline is tuned to extract the best imaging performance from these data. We present the hypothesis that gyrosynchrotron emission from CMEs intrinsically is not rare, it only appeared so because the dynamic range and imaging fidelity of available solar radio images was typically insufficient to convincingly detect this emission. If true, this then provides an exciting opportunity for routine estimation of the CME magnetic fields at coronal heights and a host of other coronal diagnostics, uniquely associated with gyrosynchrotron emissions.

Geoscience education is, itself, a dynamical sub-system in the science, technology, engineering and mathematics (STEM) learning ecosystem. Both the larger STEM environment and the geoscience ecosystem are shaped by the ever-changing relationships among people, places, science content (and its communication), teaching practices, and shifting roles for each of these. In answer to calls in the literature for research on professional learning in which the faculty member is central to the process of change, the study reported here addressed the question: What is the nature of college science faculty readiness for change in instructional practices? The setting was a professional development experience in oceanography/marine science and paleoclimatology among 32 faculty from 2- and 4-year colleges. Ten of the 32 participated in interviews and all provided survey responses and documents used in analysis. Qualitative research methods resulted in three example cases to illustrate a new framework for exploring faculty readiness for professional change in teaching. This framework blends the Clarke and Hollingsworth (2002) model of a professional change environment with research from health sciences on readiness for behavioral change (Dalton & Gottlieb, 2003). From the first model came the multi-part foundation of personal, external, professional, and consequence domains of experience and from the second came how an instructor draws on those domains to: (a) see an instructional challenge as requiring intentional action to be resolved; (b) notice new significance (for the instructor) in some aspect of instructional practice; (c) feel able to manage instructional stressors/challenges; (d) have commitment to initiate/sustain change; (e) perceive adequate support in undertaking change. Three profiles of readiness for change are represented by three composite instructor cases named Lee, Pat, and Chris. In the case of Lee, factor c drove change efforts, for Pat, factors a and b were in the forefront, and for Chris it was factors d and e. Building a healthy learning ecosystem includes attention to faculty as learners. The three cases are valuable both as illustrations of the framework in use and as touchstones for future research and development related to post-secondary professional learning for teaching.

The microstructures, textures and seismic anisotropy of a foliated blueschist-eclogite rock from Naga Hills Ophiolite (NHO) complex, North-East India has been investigated to understand the fabric relationship and deformation mechanism prevailed during the metamorphism and subduction of the Neo-Tethys oceanic crust. The Naga Hills blueschist-eclogite rock essentially contains omphacite, phengite, glaucophane, and garnet, representing an oceanic crust that experienced P-T stability field of blueschist and further eclogite metamorphism before it resurfaced accreting in the Naga Hills. Omphacite and glaucophane show weak shape preferred orientation (SPO), while phengite displays a strong SPO. The Crystallographic Preferred Orientations (CPO) of omphacite is characterized by the [001]-axes gridles within the foliation, and the (010)-poles concentrated sub-perpendicular to the foliation. For glaucophanes, the [001] axis aligns parallel to lineation and the [100] axis and (110) pole plunge perpendicular to foliation. These CPOs correspond to SL-type fabrics, related to a deformation geometry within the plain strain field and they developed from plastic deformation through dislocation creep. The seismic anisotropies of the individual minerals, blueschist-eclogite domains and their contributions in the bulk rock anisotropy has been discussed. The calculated seismic anisotropies (AVP and AVS) of bulk rock are 12.8% and 8.1%, respectively. This strong seismic anisotropy is due to the presence of phengite and glaucophane and can contribute to the observed seismic anisotropy in the subduction zone. The average low P-wave velocity of whole rock from NHO compared to blueschist is probably due to the low P-wave velocity of phengite (avg VP: 6.2 kms-1). Therefore, the low seismic velocity in the upper layer and the strong seismic anisotropy of the subducting oceanic crust can be attributed to the presence of glaucophane and phengite.

The NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project provides an extensive data set of aircraft in-situ meteorological, aerosol, and total water heavy isotope ratio measurements in the southeast Atlantic (SEA) lower troposphere over the months of Sept. 2016, Aug. 2017, and Oct. 2018. These months are during southern hemisphere spring, which is the agricultural burning season in subtropical southern Africa. During this season, biomass burning aerosol (BBA) loaded air in the African planetary boundary layer is carried out over the SEA by lower troposphere easterly flow. The goal of ORACLES was to study the effects of aerosol loading in the lower troposphere and as they mix into the semi-permanent cloud deck over the SEA. During ORACLES, water isotope ratio measurements D/H and 18O/16O were taken using Picarro brand cavity ring-down spectroscopic analyzers integrated into our Water Isotope System for Precipitation and Entrainment Research (WISPER). With over 300 hrs (~140,000 linear km at the typical aircraft speed) of 1 Hz sampling between 22°S to the equator and from 70 m to 6 km, this is a large and vertically resolved isotope dataset. In this presentation, we first use the coupled measurements of total water concentration and its heavy isotope ratio D/H to distinguish BBA plumes which have experienced prior precipitation vs those which have not. The findings are supported with lagrangian back-trajectories, MERRA surface temperatures and moisture, and isoCAM surface isotope values, which are combined to constrain simple isotope models. We find that BBA air sampled in 2016 experienced almost no precipitation, while BBA air and moisture in 2017 follow a model of convective outflow and most of the moisture loss is due to precipitation. Finally, we show the strong agreement between D/H evidence of precipitation amount and an indicator of aerosol removal via precipitation: the ratio of black carbon to carbon monoxide. In contrast, no correlation between total water concentration and aerosol removal is found, demonstrating the additional information contained in the isotope ratio measurements. This also provides an example of isotope ratio measurements supplementing other variables during a multifaceted field project to address science questions.

Though spurious geomagnetic activity lead to terrestrial storms, which in turn impacts space weather as a result of interaction between Solar wind (SW) plasma carried by Solar wind moving towards Earth due to Coronal Mass Ejection (CME) and interplanetary magnetic field, the interplay linking the Solar plasma to the Earth’s magnetic field is yet to be understood thoroughly, and hence modelling the space weather is still perceived as a challenge. To address this issue, we resort to geometrical analysis of the phenomena using state-of-the-art Multi Fractal Analysis (MFA) techniques as well as Wavelet Transforms of SW and Earth’s magnetic field parameters during the period starting from few days before up to few days after the last Solar Eclipse on June 21, 2020, in order to predict abnormal behaviour in terrestrial environment, may or may not be leading to geomagnetic storms, well before the occurrence of the event.

Profiling water vapor in the marine boundary layer (MBL) is critical to marine weather prediction, maritime communications, and understanding feedbacks relevant to multi-decadal climate prediction, yet profiling the MBL remotely has proven extraordinarily difficult because of the spatial scales involved and the proximity of the ocean surface. Collocated radio occultation (RO) and nadir passive microwave (MW) soundings can be combined in retrieval to profile water vapor with the vertical resolution of RO and with super-refraction and the wet-dry ambiguity inherent to RO resolved by the MW. We have constructed a retrieval technique that considers collocated RO and MW soundings that yields profiles of water vapor in the MBL with unprecedented precision, accuracy, and vertical resolution. We have also performed RO and MW collocation studies that consider many current RO missions and MW instruments. The joint RO+MW retrieval technique mines the information in MW soundings for an inference of the microwave refractivity in the MBL surface air, removes the biasing effect of super-refraction following the approach of Xie et al. (2006, doi:10.1175/JTECH1996.1), and resolves the wet-dry ambiguity inherent to RO using the MW sounding as a constraint or a weather forecast as a prior in 1DVAR. We constructed a simulation-retrieval demonstration system that uses a multi-phase screen propagator to simulate RO amplitude and phase and the Optimal Spectral Sampler (OSS) to simulate AMSU-A radiances. In its current state, the retrieval technique is capable of producing MBL water vapor profiles with 2% accuracy and 100-meter vertical resolution. Our collocation study shows that existing RO satellites and orbiting MW instruments achieve approximately 1,300 collocations daily, defined with a temporal window of 10 minutes. To facilitate this study, we have formulated a time-dependent rotational transformation that is applied to RO geolocations. It is three orders of magnitude more efficient than a brute force approach to finding collocations and is valid to 4% precision. We have found that the greatest yield for collocations in low latitudes would come from RO satellites that would fly in tandem with the TROPICS MW CubeSats, potentially producing 1,500 daily RO+MW collocations in the Tropics and Subtropics.

The Track Zero Charitable Trust (http://trackzero.nz) was founded in early 2018 with the goal of inspiring action on climate change through engagement with the arts. The main motivation is that artistic expression connects on an emotional level in a way that direct communication of science does not. Also, around 80 percent of New Zealanders engage in some way with the arts, a much larger audience than those who take an active interest in science. This presentation discusses the first major activity of TrackZero, a series of public meetings around New Zealand where scientists partnered with local arts practitioners to interact with audiences on the science, on people’s feelings and concerns, and on ways forward to action on adapting to or mitigating climate change. Different communities had different concerns and the range of conversations was rich and varied. While no formal evaluation has been carried out, we provide some general reflections on impact and engagement, and ideas for future activities.

Over the last century, relative sea level has risen by more than 1 ft in Annapolis, Maryland. The rise in sea level has resulted in frequent flooding in the City of Annapolis. According to National Oceanic and Atmospheric Administration (NOAA), Annapolis has experienced a 925% increase in flooding days over the past 50 years. This flooding is directly affecting roadway transportation in Annapolis in terms of traffic disruptions, which may result in disconnected mobility among communities, road accidents and travel time delays, just to name a few. This study proposes a general modeling framework to identify roadway segments that are most likely to be flooded and by what depth, due to relative sea level rise and employs the model in the coastal area of Annapolis. To do this, roadway and bridge elevations extracted from LiDAR DEM and DSM respectively, were compared with the annual exceedance probability tide levels provided by NOAA. Roadway segment elevations below the selected tide levels (1%, 10%, and 99%) are considered to be inundated by 100-year, 10-year and 1-year sea level respectively. Results suggest that, out of 36 roadways (excluding local roads) examined, one minor arterial roadway segment may be flooded by a depth ranging from 0.075 inches to 7.22 inches, at a 99% tide level of 2.17 ft (0.66 m). Three minor arterial and one major collector roadway segments may be flooded by a depth ranging from 0.1 inches to 27.3 inches (2.27 ft), at a 10% tide level of 3.84 ft (1.17 m). Three minor arterial, four major collector, and two minor collector roadway segments may be flooded by a depth ranging from 0.002 inches to 52 inches (4.4 ft) at a 1% tide level of 5.97 ft (1.82 m). For the city’s transportation network to become more resilient against sea-level rise, these roadway segments should be prioritized for future capital investment in response to seal level rise.

Hydrogen cyanide (HCN) is a widely available molecule in planetary and interplanetary environments. It has been observed that the polymerization of HCN can lead to the formation of nucleobases and proteins (Matthews & Minard 2006). Thus, HCN and its reactivity are considered to be very important for prebiotic chemistry. Our evaluation covers several molecules and oligomers, most of which have been discussed in the literature (Ruiz-Bermejo et al., 2021), and ranks them based on thermodynamic preference. In our study, we compute the relative energies of a series of HCN-derived materials relative to HCN in liquid water. We perform an automated search with semi-empirical methods to extract the lowest energy conformers for each compound. Our work relies on density functional theory (DFT) calculations with thermal corrections coupled to an implicit solvation model to better emulate the polymerization environments. These methodologies allow us to discuss the impact of our results at relevant environments such as that of Saturn’s Moon Titan or the early Earth conditions. The most stable HCN-derived material in our set is the nucleobase adenine, computed to lie ~26 kcal mol-1 below HCN in a water solution. Our enumeration of thermodynamically plausible reaction products and the reaction routes for the abiotic formation of organic macromolecules starting from simple units of HCN offers extensive insights into the chemical and the physical limitations of suspected key prebiotic processes (Sandström & Rahm 2021). 1. C. N. Matthews, R. D. Minard, Faraday Discuss. 2006, 133, 393. 2. M. Ruiz-Bermejo, J. L. de la Fuente, C. Pérez-Fernández, E. Mateo-Martí, Processes 2021, 9, 597. 3. H. Sandström, M. Rahm, ACS Earth Space Chem. 2021, 5, 2152–2159.

Southern California has seen a resurgence of winegrowing regions in the past few decades, however the future of winegrape climatic suitability in the area has not been exhaustively explored. This study evaluated the future climate suitability for the cultivation of winegrape and potential global warming impacts on southern California’s winegrowing regions through a series of high-resolution surface air temperature and precipitation projections obtained with the WRF-SSIB regional climate model. Results reveal that by mid-21st-century the surface air temperature will increase by approximately 1.2 °C, while average precipitation will decrease by as much as 11% in the southern winegrowing areas under the Intergovernmental Panel on Climate Change high greenhouse-gas emissions scenario. Evaluation of bioclimatic suitability indices indicate increases in heat accumulation for all major winegrowing areas; including an increase of about 10% in growing-degree day, while morning low temperatures in September may experience increases of approximately 11% in the future, thus impacting negatively the ripening stage of grapevines and leading to changes in wine composition and quality. Additionally, the extent of areas classified under the cool to warm climate suitability categories could decrease by nearly 42% in the study area by 2050. Conditions in southern California are already warm and dry for viticulture and continuing heat accumulation increase, along with rainfall reduction, could potentially place additional stress to winegrape crop in the area, including advanced phenological timing and moisture deficit stress that could lead to decreases in yield. The projected decline in viticulture suitability highlights the need for adaptive capacity within this sector to mitigate the impacts of global warming. Possible mitigating strategies include planting hotter climate grape varieties, moving vineyards to regions that are more suitable in the future, and adopting dry-farming techniques.

In spite of more than 20 years of substantial advances, solar flare prediction remains a largely outstanding problem. This is partly because of the scarcity of major flares. Effective flare prediction, if ever achieved, would help mitigate a substantial projected economic damage, with a long-range magnitude of 1 to 2 trillion dollars for the US alone. Prediction could also help mitigate, or even prevent, serious health risks to astronauts exposed to flares’ electromagnetic radiation and particulate. While many recent flare prediction studies have opted to employ Machine Learning techniques to better tackle the problem, a lack of sufficient understanding of how to properly treat the data often leads to overly optimistic results. We use the recently generated GSU solar flare benchmark dataset, called Space Weather ANalytics for Solar Flares (SWAN-SF), to show how a ‘mediocre’ forecast model can turn into an ‘impressive’ one, by simply overlooking some basic practices in data mining and machine learning. The benchmark is a multivariate time series collection, extracted from magnetographic measurements in the solar photosphere and spans over eight years of the Solar Dynamics Observatory Helioseismic and Magnetic Imager (SDO/HMI) era. We briefly explain the data collection process, the sampling and the slicing of time series, and then outline a series of experiments using machine learning models to illustrate the common mistakes, fallacies and pitfalls in forecasting rare events. We particularly elaborate on how and why imbalanced datasets, in general, impact the models’ performance, and how different under- or over-sampling methodologies and weighting practices could introduce accurate but often weak models. Concluding, we aim to draw attention to the impact of these practices on the flare forecasting models and how to train models by accentuating the statistical robustness over a relative accuracy in prediction.

The visual representation of data is at the heart of science. From weather forecasts, to hazard maps, to the topography of planets, the choice of colors is critical to conveying information. Yet, largely due to historical usage, default software options, and an apparent attraction to multiple bright colors, color maps such as rainbow-like “jet” are still widely used. These color maps are problematic from both a scientific and societal perspective. For instance, they can distort data because they use uneven color gradients, which lose meaning when printed in black and white, and color combinations are often applied that are unintuitive to the data they are trying to represent. From an inclusivity standpoint, such rainbow maps are also unreadable for the population with some form of color-vision deficiency. Here, we present the work that has been accomplished by the scientific (inc. visualization) community, as well as the readily available solution - “Scientific Colour Maps” (Crameri 2018, Zenodo; Crameri et al. (2020; Nature Coms); www.fabiocrameri.ch/colourmaps). This initiative features freely available, citable color map downloads for an extensive suite of software programs, and handy how-to guide, and discussion around data types and coloring options. There is a pot of scientific gold at the end of every rainbow. Crameri, F. (2018). Scientific colour-maps. Zenodo. http://doi.org/10.5281/zenodo.1243862 Crameri, F., Shephard, G.E. Heron, P.J. The misuse of colour in science. (2020 v11; Nature Communications) https://doi.org/10.1038/s41467-020-19160-7

Ultrasound Computer Tomography (USCT) is an emerging technique for breast cancer screening. Ultrasonic waves are propagated through the tissue and recorded by a set of transducers that are surrounding the breast. The experiment collects transmission and reflection data, which are used to obtain quantitative images of acoustic properties of the tissue (see Figure). This information is useful to characterize the breast tissue, and improves the specificity of standard imaging modalities. However, providing a diagnostic tool with high accuracy and clinically affordable time-to-solution (goal: ~15 min/patient) still remains a challenge. The goal of this work is to show that, despite the vast scale differences, experiments in seismology and USCT share many similarities. In both fields, the relative wave speed variations are comparable and the number of propagated wavelengths in the domain has the same order of magnitude. Because the wave equation is scale invariant, the cross-fertilization between both fields will benefit imaging methods on all scales. In this study, we present methods from seismic tomography that we have recently introduced to USCT: 1) We employ a linearized finite-frequency traveltime tomography approach for speed-of-sound reconstruction. Using the cross-correlation traveltime misfit functional, we compute analytically the sensitivity kernels using adjoint techniques. Our method can operate almost in real time while still including finite-frequency effects. It also can retrieve useful 3D information from 2D acquisition systems. 2) Similar to exploration geophysics, both speed-of-sound and reflectivity images are important for the interpretation. Here, we suggest a framework that combines full-waveform inversion for speed-of-sound and reverse time migration for reflectivity. 3) We apply the Sequential Optimal Experimental Design (SOED) method to optimize the position and number of transducers, in terms of accuracy and cost, to image both reflection and transmission information. Using the Bayesian approach, we define the quality of a design as the average of the posterior variances of the parameters. SOED provides cost-benefit curves that quantifies the information gain versus the computational cost. These are useful to control the trade-off between accuracy and practicality.

The Empirical-Canadian High Arctic Ionospheric Model (E-CHAIM) has been shown to reasonably reproduce important general features of the electron density distribution in the high-latitude ionosphere such as solar cycle and seasonal variations of the F layer peak and the location of the ionospheric trough. The utility of the model for practical applications, such as predictions of HF radio wave propagation, is less clear. In this study, we consider ground-scatter (GS) data for three SuperDARN radars at various latitudes to compare the observed skip distance against ray tracings based on E-CHAIM predictions. Monthly-averaged GS band locations in 2010-2018 are computed for every half-hour of the day, and are correlated with modelled skip distances. We show that the agreement is better during summer months and we discuss the quality of predictions at various latitudes.