Due to its inherent ability to estimate the background error covariances, an ensemble Kalman filter (EnKF) is thought to be a practical approach to the strongly coupled data assimilation problems, where an entire coupled model state is estimated as if it was a single integrated system. However, increased complexity and the multiple time scale of the coupled system aggravate the rank-deficiency and spurious correlation problems caused by limited ensemble size available for the analysis. To alleviate these problems, a distance-independent localization method to systematically select the observations to be assimilated into each model variable has been developed and successfully tested with a nine-variable coupled model with slow and fast modes. This method, called correlation-cutoff method, utilizes the mean squared ensemble error correlation between each observable and model variable to identify where the cross-update should be used, and we cut off the assimilation of observations when the squared error correlation becomes small. To implement the method on a more realistic model, we thoroughly investigate inter-fluid background covariances in an atmosphere-ocean coupled general circulation model where the spatiotemporal scales of coupled dynamics significantly vary by latitudes and driving processes.

Over the past decades, Arctic sea ice has declined in thickness and extent and is shifting towards a seasonal ice regime, with accelerated ice drift and an increase in the seasonal ice zone. The changing Arctic ice cover will impact the trans-border exchange of sea ice between the Exclusive Economic Zones (EEZs) of the Arctic nations, with important implications for ice-rafted contaminant transport. To investigate projected changes to transnational ice exchange, we use the Lagrangian Ice Tracking System (LITS) to follow ice floes from the location of their formation to where they ultimately melt. We apply this tool to output from two ensembles of the Community Earth System Model (CESM): the CESM Large Ensemble, which uses a high emission scenario (RCP8.5) that leads to over 4°C global warming by 2100, and the CESM Low Warming ensemble, with reduced emissions that lead to a stabilized warming of 2°C by 2060. We also use the National Snow and Ice Data Center Polar Pathfinder and Climate Data Record products to evaluate the fidelity of the CESM present-day tracking simulations. Transnational ice exchange is well represented in CESM except for ice traveling from Russia to Norway, with twice as much ice following this pathway compared to observations. Initial results suggest this might be due to a combination of internal variability and speed biases in the observational data. The CESM projects that by mid-century, transnational ice exchange will expand, with a large increase in the fraction of transnational ice originating from Russia and the Central Arctic. As the seasonal ice zone grows, ice floes accelerate and transit times decrease, eventually cutting off ice exchange between longer pathways. By the end of the 21st century, we see a large impact of the emission scenario on ice exchange: consistent ice-free summers under the high emission scenario act to reduce the total fraction of transnational ice exchange compared to mid-century. The low emission scenario on the other hand continues to see an increase in transnational ice exchange by 2100. Under both scenarios, all pathways have decreased to average transit times of less than 2 years, compared to a maximum of 6 years under present-day conditions and 3 years by mid-century, effectively bringing the Arctic nations closer together.

Clay deposits in playa lake/salar environments are now recognized as a significant deposit type for economic concentrations of lithium. Clayton Valley, Nevada, the location of North America’s only lithium brine producer, also has paleo-lacustrine lithium clay deposits of significance, including the Zeus property lithium clay deposit in the eastern part of the valley. This study presents new mineralogical studies on the Zeus property lithium clays to help understand their genesis and the potential economic extraction. The Zeus property (owned by Noram Ventures Ltd.) currently has an inferred resource of 17 million tonnes @ 1060 ppm Li (96,500 tonnes Li2CO3 equivalent). Initial XRD and glycolation techniques for two Zeus property bulk samples, one from the surface (relatively oxidized) and the other from ~35 m depth (relatively reduced) indicate that the clay fraction (~50%) includes smectite, illite/muscovite, chlorite, and a significant amount of amorphous matter believed to be poorly crystalline smectite + illite. The non - clay fraction has calcite, quartz and sanidine. Hectorite, sulfates, borates and halides have not been detected (yet). TIMA (Tescan Integrated Mineral Analyzer, using SEM backscatter and X-ray imaging), and petrography will help determine the mode and mineral chemical compositions in more detail, as well as addressing the relative abundance of authigenic vs. transported clay material. Initial indications are that the smectite and illite are authigenic, and formed in and below a playa lake with evolving lithium (and other cation) concentrations. The lithium budget is likely controlled by smectite and illite. Lithium adsorption onto illite and smectite surfaces and crystal edges is dependent on lithium concentration in brine solutions. Smectite adsorbs significantly higher lithium concentrations and has a higher cation exchange capacity than illite. The apparent absence of hectorite may indicate that smectite and illite formed at low temperatures, away from the influence of hydrothermal fluids and hot springs, which are present on the western side of Clayton Valley. Hectorite is relatively refractory and hinders lithium extraction in weak acid solutions. Leaching tests are underway to determine the most effective methods to extract lithium from the clays, and membrane filtration/ion filtration techniques will be tested for lithium solute concentration and purification.

Reliable, accurate, and affordable linear motion systems for agricultural applications are currently not easily accessible due to their elevated cost. Most systems available to the public have price tags in the thousands and their dimensions cannot be easily customized. Current systems have a max length of about ten meters and for a typical greenhouse application, the length may not be sufficient. The price of the system increases with an increase in length and with a base price in the thousands it becomes almost impractical to buy a system for such application. The HyperRail is a modular linear motion system with a repeatability of 2mm and current top speed of 100mm/s. An advantage this system has is its ability to increase or decrease the length of system with minimum effort and nominal increase in price. The HyperRail can be mounted on a set of tripods or directly on the structure of a building such as a greenhouse. The base price for a three-meter system, on tripods, is US$240 and an additional US$45 for each additional one-and-a-half meter. The HyperRail was designed for the use of hyperspectral imaging but can be adapted for other sensor systems. We report on a nine-meter study over pine seedlings infected with a virus. A push-broom hyperspectral camera (Headwall Nano) was mounted on the carriage of the system imaging the seedlings. The rail is currently being adapted to an environmental sensor suite that will monitor CO2, luminosity, humidity, temperature, and the concentration of dust. The HyperRail also includes bidirectional-wireless communication between the drive and the carriage; this means that the sensor suite can operate autonomously and communicate to the HyperRail drive to move to a specific location and take measurements. This system includes a graphical user interface for users who are unfamiliar with programming but could also be used through a command line interface for individuals that want to work the code and see the effects of the changes immediately. This system was developed at Oregon State University’s OPEnS Lab, here is a link to the project page for more detailed information. URL for project page: http://www.open-sensing.org/hyper-rail/

The ability to access time series data with one API would significantly enhance science data interoperability. The Heliophysics Application Programmers Interface (HAPI) is a simple, standardized mechanism for exposing time series data through a service. HAPI is being adopted by data centers within the Planetary and Heliophysics communities, especially for plasma, particle and field datasets. At the recent COSPAR meeting, the Panel on Space Weather passed a resolution encouraging data providers to have at minimum a HAPI server to deliver time series data. The COSPAR is now considering the resolution for full organizational endorsement. HAPI standardizes the two key parts of a data service: the request interface and the result format. The request interface is very simple and captures the common features of many existing data access services. For result formats, the HAPI specification allows several options, all of them streaming. Servers must provide a Comma Separated Value (CSV) result format, but may optionally provide a JSON or binary stream as well. The details of the request and result formats are described in the current version of the specification document, which is available at GitHub: https://github.com/hapi-server/data-specification. Several institutions have recently added HAPI-compliant access. These include the large Heliophysics archive at Goddard’s Coordinated Data Analysis Web (CDAWeb), as well as the Planetary Plasma Interactions node of the Planetary Data System, the Laboratory for Atmospheric and Space Physics at CU Boulder, the University of Iowa, George Mason University, and the Johns Hopkins University Applied Physics Lab. Multiple client options are available for accessing HAPI data from the growing number of servers. Autoplot (Faden, et al, 2010) and SPEDAS (http://spedas.org/wiki) both read HAPI data, and other clients (Java, Python, Matlab, IDL) can be downloaded from the HAPI Github project. The ease with which various providers have adapted existing servers to create a HAPI-compliant capability shows that it does capture a useful way to represent time series data. Because clients for reading HAPI data are also easy to create, we anticipate significant growth and interest in this emerging standard. Faden, et al, Earth Sci Inform (2010) 3:41–49, DOI 10.1007/s12145-010-0049-0

A planetary surface’s thermal infrared (TIR) emissions provide insight into the surface’s composition. Different minerals can be identified by their characteristic TIR spectral signatures. Therefore one can retrieve surface mineral composition by comparing TIR observations of a planetary surface against a library of known mineral TIR spectra measured on Earth. However for airless bodies such as the Moon, creating such a spectral library poses a challenge: minerals exhibit different TIR characteristics when measured in typical terrestrial conditions versus in lunar surface-like environments. We work to overcome this challenge by measuring TIR emission spectra of mineral samples in a chamber that simulates the lunar environment. The Simulated Airless Body Emission Laboratory (SABEL) chamber heats particulate samples under vacuum to generate a thermal gradient akin to that found in the upper regolith (i.e. epiregolith) of airless bodies. The presence of this thermal gradient—modeled to be as steep as ~60K/100 μm for the Moon—is due to airless bodies lacking the convective heat transfer provided by an atmosphere. This thermal gradient is responsible for the altered TIR spectral emission characteristics of the lunar surface, so simulating it in SABEL allows us to measure TIR spectra that are directly comparable to remotely sensed TIR observations from the Diviner Lunar Radiometer (Diviner) instrument aboard the Lunar Reconnaissance Orbiter (LRO). The work presented here focuses on one particular application of SABEL: characterizing the TIR emission spectra of silicate mineral mixtures with the endmembers plagioclase, pyroxene, and olivine. These endmembers bound the typical mineral compositions of the lunar surface. By understanding the TIR characteristics of these endmembers’ mixtures, and in particular how the wavelength position of the Christiansen feature—an emissivity maximum sensed by Diviner—changes for different mixtures, we can better interpret TIR data and their implications for surface composition.

Reconstructing patterns of topographic evolution is key to our understanding of the various processes responsible for landscape development. Suites of existing geodynamic models suggest the North American landscape has been influenced by a history of evolving dynamic support. This study investigates the extent to which this process has played a role in generating the elevation and long-wavelength topographic relief observed. Review of studies investigating distribution of magmatism, marine sedimentary rocks, sediment flux, thermochronology models, paleoaltimetry and geomorphic analyses all point towards a staged uplift history of North America since the Late Cretaceous. Another way to investigate regional uplift is to use deposits of known age, containing paleo-water depth indicators, as a datum against which post-depositional uplift can be measured. Compilations of paleobathymetry from interpreted biostratigraphic and stratigraphic markers, compared to their present-day elevations, are therefore exploited to give detailed geologic constraints on surface uplift. Our results indicate > 2 km of long-wavelength differential uplift has developed in the continental interior during the Cenozoic. In conjunction with these datasets, the uplift history of North America can be calculated by considering the geomorphic evolution of continental drainage. Results of a calibrated inverse stream-power model are presented, where > 4000 river longitudinal profiles are used to calculate best-fitting smooth spatio-temporal histories of uplift rate. The resulting model also points towards a staged uplift history in most regions of high elevation. Evaluation of results using the biostratigraphic and stratigraphic databases shows the model is broadly consistent with the geological record. As a further validation of the inversion we present a continental landscape evolution model, fed with the uplift history and erosional parameters from the inversion. This outputs elevation, discharge, denudation and sedimentary flux histories that are consistent with our inverse modeling schemes and compiled datasets of sediment flux and low temperature thermochronology. Data and modeling results are in agreement with geodynamic models predicting > 1 km dynamic support of the North American continent.

The Lithium Nevada Corp. Thacker Pass Project has a measured resource of 242M tonnes at 0.29% Li (~0.7M tonnes Li). The deposit is the southernmost of five known lenses hosted within intracaldera tuffaceous sediments along the west side of the McDermitt caldera, which formed by eruption of 16.4 Ma peralkaline-metaluminous tuff. Analyses of 9880 core samples (~1.5-m thick) give a mean of ~ 2500 ppm Li, with some >8800 ppm. Entire sedimentary sections in the southern and western parts of the caldera basin have high Li (1500+ ppm). Pre-caldera rocks, the McDermitt Tuff, late caldera volcanics, and unaltered tuffaceous sediments have relatively low Li (5 -140 ppm). Sediment deposition was mainly subaqueous in the closed caldera basin. Li-rich fill may have been deposited from airborne sources. Claystone comprises most of the Thacker Pass deposit and dominates Li-rich intervals. The rest is mostly feldspar-rich ash. X-ray diffraction (XRD) data from six core holes across the deposit show consistent lateral mineral zoning. A Li-rich zone (mean >4,000 ppm) mainly contains illitic clay and underlies a zone of lower Li with smectite + analcime. Based on XRD peak locations and chemistry, the closest analogs to the illitic clay are Li micas (e.g. tainiolite). Montmorillonitic clay and a basal silicified zone underlie the illitic zone. Some transitional rock of mixed-layer clay between the illite and smectite zones also has high Li. All clay has similar optical properties but SEM images show illite forms tiny hexagonal plates and smectite has no resolvable crystallinity. Calcite, albite, K-feldspar, and quartz occur in all zones, dolomite and fluorite near the top of the illite zone, and pyrite and bitumen in unoxidized rock. The Li-rich zone has relatively high K, Rb, Mo, As, and Sb. Secondary Kspar in the Li-rich zone gave an 40Ar/39Ar age of 14.87±0.05 Ma. No origin fully explains known characteristics. The presence of illite suggests a moderate temperature hydrothermal system. Release of Li from tuffaceous sediments and transport by alkaline groundwater are consistent with most mineralogy (characteristic of closed hydrologic system diagenesis) and with the Kspar date that long postdates caldera magmatism. Release of 10 ppm Li from the likely 50-200 km3 of intracaldera sediments would contribute ~1-4M tonnes Li.

We seek to calibrate the flow law for polythermal ice through shear strain analysis. In a warming climate, increased melting of glaciers and ice caps play a big role in sea level rise. Approximately 60% of the current contribution to sea level rise from ice loss is attributed to glaciers and ice caps, raising the urgency of sharpening mass balance change predictions in regions of streaming flow. Polythermal glaciers constitute a significant portion of these contributing glaciers, though our knowledge of their flow dynamics is incomplete. Thermally complex polythermal glaciers have both warm and cold ice which lead to weak wet-based beds, with significant amounts of basal sliding and deformable till. Consequently, polythermal glaciers experience significant shear strain as their lateral shear margins sustain the majority of the resisting stress. Most in-situ and in-lab studies of natural ice over recent years have focused on bodies of ice with frozen beds that experience minimal shear strain downglacier and across vertical planes (with depth) relative to the bed. The lack of studies on wet-based polythermal glaciers causes uncertainties in the flow law, as differences in flow law factors between polythermal ice and bodies of ice with frozen beds have the potential to induce more than an order of magnitude difference in ice velocity. To improve calibration of the flow law for polythermal ice, we seek to improve our understanding of their shear strain regimes. We developed and deployed tilt sensor systems on the polythermal Jarvis Glacier in Alaska, where we drilled multiple boreholes close to Jarvis’ shear margin and installed three boreholes with our tilt sensor systems. The tilt sensors measure gravity, magnetic and temperature data, and each system consists of multiple sensors connected along a cable and serially communicating along a common data bus with a datalogger. We have recently retrieved a year of Jarvis tilt sensor data and calculated the at-depth shear strain rates in the boreholes, allowing evaluation of the at-depth shear strain rate regimes of polythermal ice against theoretical models developed using Glen’s flow law. We present the development of our data collection methodology and the results of our shear strain analysis, with suggestions for potential calibrations of the flow law for polythermal ice.

Machine learning algorithms have become a powerful tool in different areas of seismology, such as phase picking/earthquake detection, earthquake early warning and focal mechanism determination. Previously convolutional neural networks (CNN) have been applied to continuous seismic waveform recordings to perform efficient phase picking and event detection with good accuracy [Zhu et al., 2018]. However, the off-line training of current CNN requires at least a few thousands of accurately picked seismic phases, which makes it difficult to be applied to regions without sufficient picked phases. In this work, we will validate the transfer learning among different geographic regions. Our tests show that the phase picker trained on manually-labeled data acquired from Sichuan, China following the 2008 M7.9 Wenchuan earthquake [Zhu et al., 2018] works equally well on the continuous waveform acquired from Oklahoma, US [Zhu et al., 2018]. Specifically, using the CNN trained on the Wenchuan dataset, together with 895 local/regional catalog events recorded in central Oklahoma, we refine part of the networks to pick the arrival times of the local seismicity in Oklahoma. The refined CNN results are compatible with the matched filter results using the same catalog events as templates. Our next step is to extend our test to waveforms from different tectonic regions to demonstrate the generality of CNN-based phase picker. We plan to further use a New Zealand seismic dataset that includes more than 20 GeoNet stations in the North Island, where the matched-filter detected results are available to be compared with (Yao et al., 2018). Alternatively dataset include a subset of events in the waveform relocated catalog in Southern California. Updated results will be presented at the meeting.

Human activities have been changing deposition rate of the atmospheric reactive N over the last decades. The understanding of the process that rules the accumulation and deposition of reactive nitrogen in the environment still faces major gaps mainly in regions with lack of data as South America. In this work, we evaluated the atmospheric dry deposition of reactive nitrogen (Nr) using concentration measurements of different chemical species, in six sampling sites with different environmental characteristics in the state of São Paulo, Brazil, for a sampling period of approximately 24 months (2015-2017). We used the sampling system DELTA (Sampler for Atmospheric Sampling of Long Term), and ion chromatography method to determine the concentration of gaseous (NH3 and HNO3), and particulate (NH4+ and NO3-) chemical species in the atmosphere. The N-NH3 (gaseous) was the dominant form of Nr in the atmosphere at all sampling sites. The highest N-NH3 mean value was found near the urban area of the municipality of São Paulo (SP, 1.58 ± 0.73 μgN m-3) and the lowest mean value in the most eastern sampling site (0.26 ± 0.26 μgN m-3). The high values in the SP sampling site is related to the intense traffic in the metropolitan area and, also to waste management, industrial NH3, and human emissions as observed in other urban areas around the world. The particulate N-NH4+ is the second most common form of Nr in the São Paulo state atmosphere. The other forms of Nr in the atmosphere, the gaseous HNO3 and particulate NO3, represent only about 10% of the total Nr in the atmosphere each. The total gaseous Nr concentration was a factor of 1.7 larger than particulate Nr. The Nr deposition varied throughout the years. We did not observe a pattern of variation linked to meteorological characteristics of dry and wet season, as observed in other regions of the globe. However, we found good correlation with wind speed higher than 3.5 m s-1 and humidity. Our results provide the first spatial analysis of Nr deposition using in situ data in a Latin American region and will contribute to the understanding of nitrogen balance and to improve Nr deposition modelling approaches. This study was supported by the project Nitrogen cycling in Latin America: drivers, impacts and vulnerabilities (Nnet, IAI/CRN3005 and FAPESP 2012/06416-1), PCI Program of the MCTIC, and collaborators.

Terrestrial water storage (TWS) plays a key role in land-surface interaction and the hydrological cycle. Changes in the variability of its components, including surface storage, soil moisture and groundwater can lead to significant changes in local climate, water supply sources and agricultural production. In this study, we assess the impacts of climate variability and change in surface and subsurface hydrological variables over the Magdalena-Cauca basin (Colombia) using a distributed conceptual hydrological model (WMF) driven by precipitation and temperature from four GCMs (general circulation models). WMF is calibrated using daily precipitation products from the Tropical Rainfall Measuring Mission (TRMM). Monthly mean anomalies of surface and groundwater storage outputs show consistency with Gravity Recovery and Climate Experiment (GRACE) data. Past (1970-2001) and future (2021-2050 and 2071-2100) precipitation from GCMs are statistically downscaled using a quantile mapping method to a spatial resolution of 0.25 by 0.25 degrees. Mean precipitation projections over the country are highly dependent on the selected GCMs, but the evidence shows agreement in a decrease towards the lower part of the basin; these projections are also present in surface runoff simulations. Annual mean streamflow follows the sign of the mean rainfall change over the basin. On the other hand, soil properties, topography, and geomorphological characteristics condition the patterns of subsurface and groundwater storage change (magnitude and localization), with the upper part of the Colombian Andes and the river mouth presenting the greatest changes. Results also show that mean soil moisture decrease in all scenarios, associated with changes in precipitation, but also due to the influence of temperature and evapotranspiration. The latter could lead to changes in the soil-atmosphere interaction, energetic conditions of the ecosystems and the need for agricultural mitigation strategies.

Changes in the geometry of ocean basins have been influential in driving climate change throughout Earth’s history. Here we focus on the appearance of the Greenland-Scotland Ridge (GSR) and try to understand its impact on the ocean state, including global circulation, heat transport, T and S properties and ventilation timescales, which will be useful for interpreting paleoproxies. To this end, we use a coupled atmosphere-ocean-sea ice model with idealized geometry and consider two geometrical configurations. The reference configuration (noridge) comprises two wide strips of land set 90° apart extending from the North Pole to 40°S, separating the Northern Hemisphere ocean into a small and a large basin. In the ridge configuration a zonally symmetric oceanic ridge, that extends across the Atlantic-like basin at 60°N, mimicking the GSR, is added. In addition, we consider two climatic limits of noridge: a warm case where the northern high latitudes are seasonally ice-free and a cold case where a perennial sea ice cover is present. In both cases of noridge deep-water formation occurs at the North Pole in the Atlantic-like basin. When the ridge is introduced, the flow of warm Atlantic water to the high latitudes is hampered and the ocean heat transport across 70°N decreases by ~60% which causes cooling and freshening north of the ridge. Downwelling shifts south of the ridge, thereby altering the structure of the upper overturning cell dramatically. Despite these changes, the Northern Hemisphere surface climate response is surprisingly small for the warm climate case. This is because the subpolar gyre circulation continues to transport warm water across the ridge, keeping the northern North Atlantic relatively warm and ice-free. In the colder climate case, however, the presence of sea ice provides a strong non-linear feedback, which amplifies the cooling induced by the ridge, and causes sea ice to expand. Our results highlight the possible disconnect between changes in the localization of deep-water formation, the structure of the AMOC and the properties of water masses and changes in Northern Hemisphere climate. Implications for the interpretation of paleoproxy records from the North Atlantic region will be discussed.

Sediment transport in rivers near the threshold of grain motion is characterized by rare but large transport events. This intermittency makes it difficult to relate average sediment flux to average flow conditions, or to define an unambiguous threshold for grain entrainment. Although intermittent sediment transport can be observed and characterized, its origins are unclear. In this study we investigate bedload sediment transport near the threshold of grain motion in an experimental flume to examine the origins of intermittency. We apply image-processing techniques to high-speed video of grains in a narrow flume, which allows us to track individual particles and measure statistics of particle motion. Bedload sediment transport near the threshold of grain motion is very low, allowing us to approximate the time evolution of the sediment flux via a polynomial expansion, including a linear growth rate and a nonlinear term which saturates the growth. We introduce a noisy coefficient to the linear growth rate term (“multiplicative noise”), rather than adding the noise to the equation, to model the inherent fluctuations in the system. We demonstrate that multiplicative noise near the threshold of grain motion can account for the observed intermittency. We use analytical results from bifurcation theory in the presence of multiplicative noise to analyze our experimental results, quantifying the noise responsible for the intermittency and calculating the critical shear stress for grain entrainment in a novel way that is consistent with the physics of grain motion at low transport stages.

Over the past three years Stone Aerospace has developed a novel ice penetrating technology known as a Direct Laser Penetration (DLP). DLP uses laser light carried by an optical fiber to a vertically descending ice penetrator and emitted from the nose to melt the ice in front of it at extremely high power levels and melt rates. A penetrator can be made with an onboard fiber spool connected to a surface-based laser, allowing the hole to re-freeze behind, drastically increasing efficiency and providing isolation from the surface. A parallel spool can pay out a communications fiber to carry information from imagers, fiber-based sensors (e.g. temperature, pressure, seismic), and other optical sensors (e.g. fluorescence or Raman). Laser power levels of up to 100 kW (continuous) at 1070 nm wavelength are now available and can be coupled to these probes. Successful laboratory test results at Antarctic ice temperatures show that this approach could lead to the fastest ice penetration rate available to terrestrial targets, with access to any Antarctic sub-glacial lake in under 16 hours. In this way, DLP offers an alternative to traditional, logistically intense ice drilling: a small footprint system that is fast and can deploy sensor strings through the deepest ice in a short period of time. DLP also shows promise in addressing the ‘starting problem’ for extraterrestrial targets such as Europa or Mars where low pressures prevent the formation of water at the surface and thus heat transfer for traditional melt probe architectures. In order to test the effectiveness of this concept, a Europa environment ‘cryovac’ test facility has been built at Stone Aerospace in Austin, Texas. We will discuss quantitative results from initial lab and chamber tests of the DLP concept, including in an ice column at 100 K temperature subjected to vacuum.

Lava tubes can offer protection for human crews and their equipment on other solar system bodies, in particular from radiation threats and extreme surface temperatures. Developing strategies to survey regions of other terrestrial bodies (such as the Moon or Mars) for tubes suitable for potential habitation will likely become an important part in planning future space exploration projects. A variety of surface geophysical techniques, such as ground penetrating radar (GPR) have the potential to help recognize and map tubes. GPR shows promise for providing high resolution information on tube geometries. To investigate GPR’s capacity and limitations, we use GPR, as well as comparative methods of seismic and magnetic surveys, in conjunction with LiDAR mapping of tube interiors at the Lava Beds National Monument (LBNM) in California, USA. LBNM offers a wide variety of tube geometries and textures. We have collected 2D GPR profiles and small 3D GPR grids (of parallel 2D lines) with antenna frequencies of 100 and 200 MHz on four lava tubes with different geometries, textures and at different depths. Challenges in recovering tube geometries include wave scattering in fractured rock covering tubes, irregular and “drippy” ceilings and walls, and blocky floors. Our primary results show that the top of the LBNM tubes can generally be resolved in the GPR data, while resolving the bottom is more challenging. The utility of various GPR processing techniques can be directly assessed by comparing resolved GPR images against the LiDAR-measured tube geometries.

WindNinja is a high-resolution diagnostic wind model developed for use by operational wildland fire management. The original version of WindNinja employed a numerical solver to enforce conservation of mass (COM). Because the COM solver is fast-running, requires little technical experience to run, and predicts ridgetop speed up and channeling effects well, it is widely used by the operational fire community. The COM solver has limitations, however, in regions where momentum effects dominate the flow, such as in regions of flow separation, which can occur on the lee-side of terrain obstacles. Recently, a second solver has been incorporated into WindNinja which enforces conservation of mass and momentum using computational fluid dynamics (CFD) techniques based on the Reynolds-Averaged Navier-Stokes (RANS) equations. In this work we compare simulations from the CFD solver to measurements made during three field campaigns (Askervein Hill, Bolund Hill, and Big Southern Butte) as well as to simulations from the COM solver and an LES model. Evaluations focus on near-surface winds during high-wind periods, which are of particular interest to wildland fire managers.

We are at a unique time in the study of our place in space. On one hand, we operate in the same paradigm that has guided the study of space science for the past couple of decades, and on the other a rising dependence of our economic and social well-being on space demands a shift. Everywhere in our society ‘big data’ (defined by four V’s: volume, variety, veracity, and velocity) and the advent of sophisticated and efficient methods to explore these data (i.e., data science) present new opportunities for discovery, and the time is ripe for these methods to shift how we study the physics of space. We will first discuss the meaning of data science in the context of space science, and then demonstrate the potential for new discovery through a power use case: leveraging Global Navigation Satellite Systems (GNSS) signals for space weather prediction. In this use case, we take advantage of a large volume of data from GNSS signals, data science-driven technologies, and a machine learning algorithm known as the Support Vector Machine (SVM) to develop a novel predictive model for high-latitude ionospheric phase scintillation. This talk will conclude with a perspective on opportunities in space science through ‘big data’ and creating new scientific discovery at the intersection of traditional approaches and data science-driven innovation.