PVGeo is an open-source Python package for geoscientific visualization and analysis harnessing an already powerful software platform: the Visualization Toolkit (VTK) and its front-end application, ParaView. The VTK software platform is well-maintained, contains an expansive set of native functionality, and provides a robust foundation for scientific visualization, yet the development of tools compatible for geoscience data and models has been very limited. As a software extension package to VTK and ParaView, PVGeo addresses the lack of geoscientific compatibility by creating a framework for geo-visualization. This framework is a set of tools for visually integrating geoscience data and models directly within ParaView’s graphical user interface, simplifying the required routines to make compelling visualizations of geoscientific datasets. The PVGeo package is available for download on PyPI (pip install PVGeo), documented online (http://pvgeo.org), and open-source on GitHub (https://github.com/OpenGeoVis/PVGeo) for community-driven developments.

A great deal of paleoenvironmental research on tropical alluvia and slope sediments assumed the long-term persistence of stable climates and associated rainforest vegetation in the Congo basin during the Quaternary. Geomorphological, paleo-hydrological and ecosystem history research in the eastern Congo basin (Kivu, DR Congo) has provided evidence of the frequent occurrence of multi-layered alluvia and fans in river valleys and plains as well as stratified slope deposits (hill-wash, stone-lines, pedo-sediments) that reflect former modifications of the environment. Numerous radiocarbon data indicate that ecosystems within the Congo basin are highly sensitive to climate change through modification of surfaces and run-off dynamics. A stratigraphic record characterized by variable sediment layers of 2.0–5.5 meters thick spans the Holocene and the Pleistocene back to 50 ka BP (completed by finds from Cameroon and Central African Republic). Buried stone-lines and paleo-soils indicate once drier, more open landscapes under alternating wet and dry climates in currently humid and semi-humid regions. A conceptual morpho-dynamic model is presented and summarizes process related response to former environmental modifications.

In this study, we re-analyze geophysical survey data in order to image intermediate to deep geoelectric structure and to enhance understanding of subsurface structures in the region of South-Eastern part of Korea. This region in South Korea becomes geologically more important after an earthquake in Gyeongju (ML 5.8) in 2016, since it is the largest event among those which have been instrumentally recorded in South Korea. To obtain an image of geoelectric structures, we reprocess magnetotelluric (MT) sounding data, which were originally acquired during 2012~2017 in the region near south Gyeongju and north Ulsan for geothermal resources exploration. The area where MT sounding were performed includes the major faults zones, Yangsan, Milyang Moryang, and Ulsan Faults developed in this region, in NNE direction. The purpose of the MT surveys lies in delineating the extension of the major faults. The total number of MT measurement points are 67, and the frequency band of MT signal are ranging from 0.01 to 1,000 Hz. The strike directions of major faults are approximately NNE, so the 2D MT lines for interpretations are set up along 110 degree from North. The successive 2D inversions are done for TM mode and joint mode of TM and TE data. In addition, 3D inversion domain encompassing the whole area of MT stations are also performed to obtain detailed image of the area. We also recompile airborne magnetic data to compensate for geologic model. The geoelectric image clearly shows fault zone conductors and their extensions over the region and will help understand subsurface structure of major faults zone in South-Eastern part of South Korea.

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue the warnings to the public on the basis of forest fire danger index classes. There is no operational forest fire danger index for the country India due to the sparsely distributed meteorological stations. The Fire Danger Rating System (FDRS) is a Decision Support System, which takes into consideration of all the factors affecting the fire danger such as fuel type, weather parameters and terrain characteristics and indexing into different classes of fire danger for the purpose of issuing warnings to the public, implementing the mitigation measures for controlling fires. The Static Fire Danger Index (SFDI) is a constant over the study area, computed from the MODIS Land cover type yearly L3 global 500 m SIN grid (MCD12Q1) and ASTER GDEM datasets. In this study, we used the Near Real Time datasets and these Near Real time datasets are available within 3 hours of the observation time of satellite overpass, downloaded through an FTP site. Dynamic Fire Danger Index has been calculated from the Near Real Time (NRT) Level 2 MODIS Terra Land Surface Temperature datasets (MOD11_L2) and MODIS TERRA NRT surface reflectance dataset MOD09 by using three parameters, i.e. Potential surface temperature, Perpendicular Moisture Index (PMI) and Modified Normalized Difference Fire Index (MNDFI). Forest Fire Danger Index (FFDI) has been computed by integrating the static fire danger index and individual dynamic forest fire danger index on each day during the major fire episode of Uttarakhand in 2016. The FFDI has been categorized into 5 fire danger classes such as Very high, High, Moderate, Low and No fire danger and MODIS active fire product MOD14 has been used for the validation. Estimated accuracy was ranging from 72% to 91% and the overall accuracy was around 81.27%. Therefore, the developed Forest Fire Danger Index will be useful for the disseminating the danger maps daily in near real time basis using the MODIS TERRA Near Real Time datasets so that the fire officials to take necessary actions to control the spreading of forest fires.

Space weather events can impact satellite communications, astronaut health, and the electric power grid. It is thus of utmost importance that we develop efficient, reliable tools to determine when space weather events, such as solar flares, will occur and how strong they will be. The SWx TREC Deep Learning Laboratory has developed several state-of-the-art machine learning projects to improve solar flare prediction through the use of deep learning models, generative adversarial network data augmentation, and explainable artificial intelligence techniques. In particular, we compared two generative adversarial networks (GANs) to super-resolve the Solar and Heliospheric Observatory’s Michelson Doppler Imager (SOHO/MDI) magnetogram data to match the quality of the Solar Dynamics Observatory’s Helioseismic and Magnetic Imager (SDO/HMI) magnetogram data. We find that both GANs are able to preserve key features of the original SOHO/MDI magnetogram data while achieving better resolution to match the SDO/HMI data. In the future, we will use the combined, augmented dataset in a Long Short-Term Memory model for solar flare prediction to see if training on the expanded dataset results in improved predictive power compared to training on the SDO/HMI dataset alone. In addition to data augmentation, we have used Local Interpretable Model-Agnositc Explanations (LIME) on our existing solar flare prediction model to provide more insight into specific predictions. This is an important step in building trust in our model and understanding what features are driving the model’s predictions. In this presentation, we will discuss these recent projects as well as future work that the SWx TREC Deep Learning Laboratory will tackle in order to advance the field of machine learning in space weather, including: improved hardware, better visualization capabilities, cutting edge models, software tools, and community resources.

With the COVID-19 pandemic still active, the Boulder Solar Alliance Research Experience for Undergraduates (BSA REU) decided to keep the program remote for a second consecutive year. Our coordination team took lessons learned from the 2020 virtual BSA REU program and adapted the research experience to suit a virtual environment, especially with respect to increased technological support. The primary changes, as well as the reasons for implementing them, are outlined below. Due to the virtual nature of the program, all of the projects relied more heavily on coding. In response, the BSA REU team invested more time and resources in programming tutorials and weekly programming help sessions in Python, IDL, and MATLAB. The participants also faced unequal access to high-quality hardware resources in a remote environment. As a result, students received a technology stipend to help them upgrade their computer and internet resources. Additionally, with an increase in the focus on programming, a higher number of projects in 2021 involved machine learning and data science techniques compared to previous years. However, many of the students were unfamiliar with machine learning (ML) concepts. The coordination team provided an introductory ML lecture and tutorial during boot camp and hosted a weekly ML sub-group meeting to provide support and resources for students involved in ML projects. Finally, without being able to present results in person, it was important to provide an interactive online experience for the poster presentation session. To make the final poster presentation more engaging in a virtual environment, we used Gather Town, an online service where participants create avatars that can interact with the virtual environment. In this presentation, we will discuss how the adjustments to the BSA REU program in a virtual environment, including those listed above, and how we think REU programs can adapt to future remote and hybrid options. We will also discuss what elements of a remote program can be carried forward into an on-site program to enhance the on-site experience.

Superparameterized (SP) global climate models have been shown to better simulate—as compared to conventional models—various features of precipitation, including diurnal timing as well as extreme events. While various studies have looked at the effect of differing microphysics parameterizations on precipitation within limited-area cloud-resolving models, we examine here the effect on continental-US extremes in a global SP model. We vary the number of predicted moments for hydrometeor distributions, the character of the rimed ice species, and the representation of raindrop self-collection and breakup. Using a likelihood ratio test and accounting for the effects of multiple-hypothesis testing, we find that there are some regional differences, both in the current climate and in a warmer climate with uniformly increased sea-surface temperatures. These differences are most statistically significant and widespread when the number of moments is changed. To determine whether these results are due to (fast) local effects of the different microphysics or the (slower) ensuing feedback on the large-scale atmospheric circulation, we run a series of short, 5-day simulations initialized from reanalysis data. We find that the differences largely disappear in these runs and therefore infer that the different parameterizations impact precipitation extremes indirectly via the large-scale circulation. Finally, we compare the present-day results with hourly rain-gauge data and find that, for the model configuration and resolution used, SP underestimates extremes relative to observations regardless of which microphysics scheme is used.

Every breath we take reminds us we require oxygen. Crucially, whilst there is wider research into the oceanic impact of climate change, including warming and acidification, and on oxygen content of oceans, there is very little research into the specific impact of acidification and related carbon dioxide changes on marine photosynthetic oxygen production. Oceans are underappreciated. Marine sources provide 40-70% of our oxygen. Many post-Cambrian explosion historic extinction events appear to be related to oxygen levels or anoxic ocean die-off. Were research to be commissioned, that provides clear evidence of risk to oceanic oxygen production and therefore atmospheric oxygen levels, the conclusions could be far-reaching, including identifying potential tipping points that may result in human extinction. The probability of an inadequate human response to risks is increased by; time lags of “many hundreds, if not thousands, of years” in ocean warming and in acidification; underestimation of the greater interdependence of marine ecosystems; the non-visibility of change, combined with lack of public comprehension that once major change ‘hysteresis’ has taken place, it may be impossible to reverse the ecological ‘regime change’. Importantly, in the current ‘Anthropocene’, net oxygen production from land sources is now limited due to fossil fuel use, as well as farming and logging practices, which recycle and or exploit reserves rather than sequestering net carbon. The health of net oceanic global oxygen production cannot be assumed from the presumed dominance of photosynthetic marine bacteria in ocean blooms as the impact of mixotrophs and anoxygenic bacteriochlorophyll photosynthetic bacteria is often not represented in related system modelling. Currently, most public focus is on global warming as reflected in weather and related consequences, which are not viewed as immediate threats to day-to-day life, living standards or indeed survival. In contrast, were research to bear out the possibilities of oxygen depletion or potential for an oxygen-related extinction risk, and particularly in relation to ocean acidification, the danger may be better understood leading to more cohesive public demand for action in finding alternative fuel sources.

For decades, agricultural expansion in Southern Amazonia has relied on deforestation to increase the national and global supplies of cattle and soybean products. Research on the use of land and water for increasing the region’s pasture and cropland outputs has provided important insight into the role of producers in managing these resources. However, the roles of other actors on cattle and soybean production systems (e.g. traders) have been less apparent. For instance, some private initiatives, such as the Cattle Agreement or the Soybean Moratorium, have been proposed to curb deforestation through the cattle and soybean supply chains as a means to influence the production process. Here, we highlight the role of producers and consumers in the use of land and water resources for agricultural production in the state of Mato Grosso, Brazil, by combining field measurements and modelling with trade mapping. We use the Transparency for sustainable economies platform (Trase, https://trase.earth) to highlight the role of trade actors by combining high resolution trade information (e.g. custom declarations) with up-to-date deforestation and water scarcity maps. In 2015, up to five traders with zero deforestation commitments exported over 40% of soybean produced in the state of Mato Grosso. Within this context, producers can increase agricultural output by irrigating cropland, and/or concentrating cattle production on current pastureland, but this combined system has the potential to increase water scarcity in the dry season. Our analysis provides additional information on the drivers shaping the Brazilian agricultural frontier and attempts to bring producers and consumers closer together in agricultural supply chains.

The year 2020 will mark the onset of Paris Agreement, whose main aim is to limit global warming to 2° C. This has set a limit on the amount of Greenhouse gases the world can still emit in order to be safe. Now, different principles (like inertia, equity, blended) for allocating a justified share of carbon emissions to the countries are being actively discussed in research communities. Current study determines the global carbon share of India using these principles. Future cumulative emission quota of India starting from the year 2015 and from 2020 to meet the warming limits of 2°C and 3°C associated with a 66% and 50% probability has been calculated. Methods proposed in previous studies are referred. Results show that the share for India based on the principle of inertia and equity are 9.58% and 16.03% respectively. While the principle that accounts historical accountability (called inclusive sharing) is the most promising one; ranging from 17.98 to 22.46%. The results with inclusive sharing are more than double that of inertia sharing and significantly higher than equity sharing. With these results, we advocate that neither the principle of inertia nor equity alone can be ascertained as fully justified criteria for countries like India in determining the future emissions. On the other hand, ‘inclusive sharing’ that accounts for historical accountability of the country is suggested as a more justified principle for carbon sharing. Additionally, this outcome also questions the general assumption that per capita based emission allocation method is most advantageous for a highly populated country. In the end, we conclude by defending the factor of historical accountability for rationalizing the emission rights of countries that represent high share of world population and significantly low per capita emissions. This study finds importance in climate science research in determining how rational are the different sharing principles and how they can affect the development of different countries in the world.

Downslope and gusty winds in the lee of the Santa Ynez Mountains (SYM) greatly influence the Santa Barbara (SB) coast in California. These winds, known as ‘Sundowners’ due to their typical peak after sunset, are characterized by northerly cross-mountain flow and strong stability at mountaintop level. The National Weather Service (NWS) defines a Sundowner when sustained cross-mountain winds greater or equal to 30 mph or gusts greater or equal to 35 mph are observed at surface stations located downwind of the SYM. In addition to gale-force winds, temperatures above 100°F and relative humidity below 15% are not uncommon during sundowners, and the marine layer influences the spatial extent to which these strong winds may reach. These conditions have led to turbulence which effects aviation at the local airport and the rapid spread of multiple destructive wildfires in the region, including the Jesusita Fire (2009), Sherpa Fire (2016), and Thomas Fire (2017).

The Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) was launched in Jan. 2009 to monitor global CO2 and CH4 distribution from space. The wide-spectral-range data by FTS can measure the partial-column density of the lower troposphere using sun-light reflected from the surface and thermal emission from the atmosphere. In addition to nominal global grid-observation, TANSO-FTS has an agile pointing system to target various CH4 point-sources as well as reference points every three days over years, and can capture the entire flux emitted vertically and horizontally from the source. We demonstrated the monitoring capability by using the natural-gas blowout event at Aliso Canyon, CA and tried to estimate the CH4 flux from a dairy farm in Chino, CA with the Weather Research and Forecasting model. The GOSAT footprint, which is much larger than the point-source area, reduces the enhancement of the retrieved column density close to the detection level. As the single-pixel data and acquisition time of 4 s by FTS limits the number of sampling points near the emission source, careful screening with wind speed and direction is required to acquire reference and source dataset for analysis. The largely fluctuated single data requires to be averaged to improve the precision, but the GOSAT data is spatially too sparse. We calculated the local CH4 flux from Chino using the correlation between wind speed and density, but the lack of a proper reference result in large errors. To solve the above-mentioned issues, we manufactured airborne imaging-spectrometer suites comprising two bands to measure CH4 and CO2 at 1.6 μm and O2 at 0.76 μm. They have 4,000 times more data than GOSAT, increase enhancement with higher spatial resolution, and select proper upwind reference with imaging capability. In Feb. 2018, we flew over greater Nagoya with the mixture of possible emission sources such as energy production, waste water, dairy farm, and agriculture. The spectral image of spatial resolution <100 m has clearly detected enhancement from individual local sources of CH4 and CO2. We add one more spectrometer to measure short-lived NO2 to detect plume orientation. Our goal is to individually estimate city-level CH4 flux from different source sectors.

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science chemical transport model (CTM) capable of simulating the emission, transport and fate of numerous air pollutants. Similarly, the Weather Research and Forecasting (WRF) model is a state-of-the-science meteorological model capable of simulating meteorology at many scales (e.g. global to urban). The coupled WRF-CMAQ system integrates these two models in a “two-way” configuration which allows feedback effects between the chemical (e.g. aerosols) and physical (e.g. solar radiation) states of the atmosphere and more frequent communication between the CTM and meteorological model than is typically done in uncoupled WRF-CMAQ simulations. In this study we apply the various cumulus parameterization (CP) options available in WRF at horizontal grid spacings ranging from regional scale (i.e. 12-km) to urban scale (i.e. 4 and 1 km), focused on the July 2011 DISCOVER-AQ campaign that took place over the Baltimore-Washington D.C region. Of particular interest is the evaluation of the WRF simulated clouds, as analysis of previous WRF-CMAQ simulations using a “standard” 12-km configuration for the model suggest that WRF has difficulty predicting clouds (particularly fair-weather clouds), with decreasing skill at finer horizontal grid spacings. Here we will examine the impact that the WRF CP options have on cloud predictions, using available satellite data to evaluate model the performance. We then examine how changes in the WRF simulated clouds affect CMAQ predictions of ozone and PM2.5 at the various scales.

Non-proteinaceous and proteinaceous biological aerosols are abundant within the atmosphere and have the potential to impact the climate through cloud and precipitation formation. In this study, we present the differences in the laboratory-measured freezing capabilities of the non-proteinaceous and proteinaceous biological materials to determine which has more potential to impact the ice nucleation in the clouds. As non-proteinaceous surrogates, we examined multiple cellulose materials (e.g., microcrystalline and nanocrystalline cellulose) whose sizes range from ~100 nm to >100 μm (according to manufacturer report). For proteinaceous proxies, we looked at different gram-negative bacteria, such as Pseudamonas aeruginosa, Escherichia coli, Serratia marcescens, Citrobacter freundii, and Snomax, (which contains P. syringae) that can be found around the proximity of the Texas Panhandle. By using the Cryogenic Refrigeration Applied Freezing Test (CRAFT) system, we estimated immersion freezing efficiency (i.e., ice nucleation activity scaled to a unit of mass) of each sample at the temperatures greater than -30°C. We have observed that not all gram-negative bacteria has high immersion freezing activity, but the few do have a warmer temperature onset (>-20 °C) than the cellulose used. For those that did not exhibit substantial freezing efficiencies, they had similar freezing properties as the broth, in which the bacteria were incubated, as well as the cellulose materials examined. These observations suggest the presence and potential importance of bacterial cellulose in the atmospheric ice nucleation. From here, we need to conduct more in-depth investigation in the effects of a wider variety of atmospherically relevant biological aerosols to get a better understanding of the effects of said aerosols on overall aerosol-cloud interactions. Acknowledgments: K. Cory would like to acknowledge NSF-EAPSI and JSPS Summer Program for the travel fellowship support. N. Hiranuma acknowledges financial aids by the Higher Education Assistance Fund (HEAF), WTAMU Office of Graduate School and Killgore Research Center.

Carbon distribution in soil is intricately linked to soil health. However, repeatable measurements of carbon distribution typically require destructive sampling and laboratory analyses. Soil carbon distributions in both natural and managed landscapes significantly vary due to numerous factors related to topography, mineralogy, hydrology, land use history, and vegetation. In order to accurately inventory soil C distributions and dynamics over time, we are developing a new technique that relies on neutron inelastic scattering to measure elemental distribution. This approach can be used to image a volume of approximately 50 cm × 50 cm × 30 cm (depth) with a few centimeters resolution, for example the root zone of a plant. To achieve this, we use neutrons created in a deuterium-tritium fusion reaction. The products of this reaction are an alpha particle and a neutron. Due to momentum conservation, both particles are emitted in opposite directions in the center-of-mass frame. This allows us to measure the neutron direction by detecting the alpha particle with a position sensitive detector. The neutron can then induce an inelastic scattering reaction on a carbon nucleus present in the soil, and this event produces a gamma ray with a characteristic energy for the carbon isotope. Using a gamma detector, we measure these gamma rays, which allows us to perform time-of-flight analysis between arrival times of the alpha and gamma particles. Using the information from both measurements (alpha and gamma), we can reconstruct the spatial distribution of the carbon atoms and other elements in soil. We will report on the design, potential applications, and limitations of the instrument. We will also report on initial results from laboratory experiments and progress towards future field experiments. The information, data, or work presented herein was funded by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Contract No. DE-AC02-05CH11231.

Space weather forecasting remains a national priority in the United States due to the impacts of events like solar flares to life on Earth. High energy bursts of radiation originating from solar flares have the potential to disrupt critical infrastructure systems, including the power grid and GPS and radio communications. The rise of machine learning and the development of higher-quality instruments has greatly improved solar flare prediction models over the past decade. However, the magnetogram data used for solar flare forecasting—taken by the Solar and Heliospheric Observatory/Michelson Doppler Interferometer (SOHO/MDI) and the NASA Solar Dynamic Observatory/Helioseismic and Magnetic Imager (SDO/HMI) instruments—are incompatible due to differences in the cadence, resolution, and size of the data. Furthermore, many studies only focus on data from a single instrument which disregards decades worth of potential training data that is necessary to understand solar cycles. In this work, we show Generative Adversarial Networks (GANs) can be used to super-resolve the historic lower-quality SOHO/MDI data set to match SDO/HMI quality to create a standardized magnetogram data set. The implementation of a Pix2Pix GAN produced some undesirable artifacts in the synthetic image while image translation methods CycleGAN and CUT preserved solar features present in the data more accurately, even in the absence of paired data. The resulting combined, higher-quality data set will be used to improve the predictive power of current solar flare forecasting models.

Geoscience employers have increasingly called for the future workforce (students) to demonstrate competence in non-technical skills, including teamwork. This descriptive qualitative study contributes to ongoing efforts to identify the specific practices, skills, habits, and knowledge that make up these desired teamwork competencies in the geosciences. We collected data from three online focus group discussions centered around teamwork. Focus group participants were hydrogeology and environmental geology employers and team managers from government, private industry, and non-profit organizations in the United States. Using the Marks et al. (2001) teamwork taxonomy model as our conceptual framework, we generated three categories of teamwork skills specific to environmental geoscience teams. First, our data indicate that these employers value team transition skills related to specifying goals, interpreting team tasks, identifying resources, and planning. The second category of desired teamwork competencies included action skills such as metacognition, coordination and mentoring. These skills directly impact successful task completion. The third category captured interpersonal skills such as emotional intelligence, proactive communication, and organization. A fourth category of desired teamwork competencies emerged from data analysis and include ethical skills related to trust, integrity and humility. This study provides a detailed description of teamwork competencies desired by environmental geoscience employers and suggests implications for how to prepare students for this workforce.

On October 20, 2020, NASA’s OSIRIS-REx spacecraft performed its Touch-and-Go (TAG) activity, in which it briefly contacted the surface of the asteroid Bennu and successfully collected a sample of regolith. Subsequent images of the sampling mechanism showed that thousands of small regolith particles were escaping from it, apparently in conjunction with movements of the mechanical arm and wrist joint by which the sampling mechanism is attached to the spacecraft. The escaping particles could be tracked from one image to another, and across multiple images, which allowed the OSIRIS-REx optical navigation team to detect, associate, and track particles using a combination of manual and automated techniques. The associated tracks each represent a unique particle that was further analyzed to estimate its ejection time, 3D trajectory, and velocity, as well as its photometric properties, which were used to compute its brightness, size, and mass. Compiling the aggregate photometric and physical data for all of the particles leads to an estimate of total sample mass lost during the post-TAG imaging sequences. These results further inform an understanding of the sample escape mechanisms and sample loss that occurred before the sample head was stowed in the return capsule on October 28, 2020. The material presented here is based upon work supported by NASA under Contract NNM10AA11C issued through the New Frontiers Program.