This work presents system concepts, integration efforts and results of the incorporation of recent advances in geospatial technologies, including augmented reality, virtual reality and unmanned aerial systems (UAS), into teaching and learning in the geosciences. Descriptions include the exploration of multiple technological alternatives and introduce system design and integration to enhance and innovate instructional materials in classrooms. The 3D Immersion and Geovisualization (3DIG) system, implemented at the Center for Geospatial Research at the University of Georgia incorporates augmented/customized commercial-off-the-shelf solutions for data acquisition, visualization and human-machine interaction. Through the immersive capabilities of 3DIG, students can be involved in a full data acquisition-processing-analysis workflow. Data streams are used for system integration, with emphasis to model generation/manipulation and remote sensing applications, including multispectral data acquisition/analyses, structure-from-motion based point-cloud/model generation, DEM and texture extraction, and orthomosaics. Resulting products are used with virtual and mixed reality holographic devices, a Geographic Information System (GIS) and with game engines (Unreal Engine and Unity) to create realistic multi-scale multi-theme 3D reconstructions of planet Earth, landscapes and/or objects. Among other system components, an augmented reality digital sandbox equipped with two depth cameras supports experiential learning and experimentation involving scaled down replicas of landscapes or user-defined topographies. The system allows for fast representation of landscape changes (near-real time response), which simulates fluid flow over modified terrain, as well as quantitative analyses, modeling and what-if scenarios through the integration with a GIS. The 3DIG system has been incorporated into classwork and results have been evaluated. This work introduces the interconnected and complementary technologies of 3DIG; presents lessons learned during system design; introduces system implementation and evolution (including the recent integration of new components); describes system use for hands-on and immersive experiential learning; and discusses system evaluation.

Challenges in remote sensing, including remote sensing of vegetation, include the spectral characterization of objects over space and time. One key aspect for this characterization involves the geometry of data acquisition and positional relationships between light source, the target and the remote sensor. Several configurations of goniometers have been used to acquire spectral data as a function of this geometry and this strategy has been particularly efficient when applied to the study of short canopies (e.g., grasses). Tall canopies present logistical challenges when conducting these analyses, which can be resolved by replacing physical structures (rails) with flying systems capable to conform to different canopy geometries and data acquisition requirements. This work (the Droniometer Experiment) investigates anisotropies of a forest using radiometrically calibrated images from a multispectral camera (MicaSense RedEdge) mounted on a rotary-wing unmanned aerial system programmed to follow a planned flight that simulates data acquisition by a goniometer assembled over tall canopy. The experiment used multiple planned flights, conducted to represent changes in illumination, considering sun azimuth and elevation (multiple flights per day and over the course of months). Multi-angle data acquisition was addressed by controlling aircraft position and camera pitch at regular intervals. This work presents the integration of the droniometer system, including platform and camera requirements and control, data acquisition and processing, and analyses of results for target/vegetation characterization and to support information extraction and multi-angle remote sensing. A radiative transfer model, the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) was used for comparative analysis and to further describe anisotropies in spectral responses of tall canopies.

Seismic full-waveform inversion (FWI) can produce high resolution images of the Earth’s subsurface. Since full waveform modelling is significantly nonlinear with respect to velocities, Monte Carlo methods have been used to assess image uncertainties. However, because of the high computational cost of Monte Carlo sampling methods, uncertainty assessment remains intractable for larger data sets and 3D applications. In this study we propose a new method called variational full-waveform inversion (VFWI), which uses Stein variational gradient descent (SVGD) to solve FWI problems. We apply the method to a 2D synthetic example and demonstrate that the method produces accurate approximations to those obtained by Hamiltonian Monte Carlo (HMC). Since variational inference solves the problem using optimization, the method can be applied to larger datasets and 3D applications by using stochastic optimization and distributed optimization.

The impact of high-frequency atmospheric forcing on the Yellow Sea (YS) circulation with emphasis on the Yellow Sea Warm Current (YSWC) was investigated by comparing model simulations with and without high-frequency atmospheric processes. By including the high-frequency atmospheric forcing at the synoptic scale in an atmosphere reanalysis used to force the ocean model, the simulated intensity of the mean YSWC is increased by 40-100%. The mean temperature is decreased by up to 1°C, and the mean salinity along the YSWC pathway is increased by up to 0.2-0.5 psu. Additional simulations in which either the wind or other atmospheric fields were filtered revealed that the high-frequency wind forcing is more important in the YSWC and relates mean temperature with the other atmospheric variables that play relatively minor roles. In winter, the high-frequency wind forcing associated with frequent winter storm bursts and relaxation is able to excite coastal trapped waves propagating cyclonically around the Bohai Sea and Yellow Sea coast; this forcing is a very important factor influencing the synoptic variability in the YSWC and drives intermittent warm and salty water intrusion into the southern YS. The results from this study provide a basis for a new understanding of how transient atmospheric phenomena, such as winter storms, impact regional circulation and water transport in the YS.

To study the role of the Atlantic meridional overturning circulation (AMOC) in climate change, we perform an abrupt CO2-doubling experiment using a coupled atmosphere-ocean-ice model with a simple geometry that separates the ocean into small and large basins. As in observations and high-end climate models, the small basin exhibits a MOC and warms at a faster rate than the large basin. In our set-up, this contrast in heat storage rates is 0.6 +/- 0.1 W/m^2, and we argue that this is due to the small basin MOC. However, the MOC weakens significantly, yet this has little impact on the small basin’s heat storage rate. We find this is due to the effects of both compensating warming patterns and interbasin heat transports. Thus, although the presence of a MOC is important for enhanced heat storage, MOC weakening is surprisingly unimportant.

The vertical profiles of temperature and water vapour from the Atmospheric InfraRed Sounder (AIRS) have been validated across various regions of the globe as an effort to provide a substitute for radiosonde observations. But there is a paucity of inter-comparisons over West Africa where local convective processes dominate and RAOBs are limited. This study validates AIRS temperature and relative humidity profiles for selected radiosonde stations in West Africa. Radiosonde data was obtained from the AMMA and DACCIWA campaigns which spanned 2006 - 2008 and June-July 2016 respectively and offered a period of prolonged radiosonde observations in West Africa. AIRS performance was evaluated with the bias and root mean square difference (RMSD) at seven RAOB stations which were grouped into coastal and inland. Evaluation was performed on diurnal and seasonal timescales, cloud screening conditions and derived thunderstorm instability indices. At all timescales, the temperature RMSD was higher than the AIRS accuracy mission goal of ±1 K. Relative humidity RMSD was satisfactory for the entire troposphere with deviations < 20% and < 50% respectively. AIRS retrieval of water vapour under cloudy and cloud-free conditions had no significant difference whereas cloud-free temperature was found to be more accurate. The seasonal evolution of some thunderstorm convective indices were also found to be comparable for AIRS and RAOB. The ability of AIRS to capture the evolution of these indices imply its applicability for determining the thunderstorm probability over West Africa under the Global Challenges Research Fund African Science for Weather Information and Forecasting Techniques project.

Similar to most West Antarctic ice shelves, those in the Bellingshausen Sea have rapidly thinned by hundreds of cubic kilometers over the last decades yet they remain under-studied compared to other regions. The increased melting rates in the West Antarctic Peninsula (WAP) have been linked to warm Circumpolar Deep Water (CDW) that is able to access the continental shelf due to the absence of the Antarctic Slope Current. The exact pathways of CDW flowing on to the shelf and of meltwater flowing away from the ice shelves are essential to understanding the dynamics in this region and how it will change in the future. Here, we propose that the Bellingshausen Sea plays an important role in connecting circulation between the Amundsen Sea and the WAP and may influence water properties that circulate under floating ice shelves throughout the West Antarctica. Using a combination of hydrographic and isotopic data from a recent cruise to the Bellingshausen Sea (December 2018 to January 2019), multiple methods are applied to identify circulation pathways and to quantify glacial meltwater fractions. The meltwater measurements show that the Belgica and Latady Troughs are important pathways for CDW to reach the ice shelves, though almost twice as much meltwater is transported off the shelf via the Belgica Trough. CDW enters the shelf at the deepest part of the Belgica Trough, moving towards the coast along the trough’s eastern side. The largest meltwater fractions are found along the western flank of the Belgica and Latady Troughs. The meltwater signature can be tracked to the western edge of the Bellingshausen Sea, where it is then entrained into a boundary current system that flows over the continental slope towards the Amundsen Sea.

Generation and propagation of lower hybrid drift wave (LHDW) within and near the electron diffusion region (EDR) during guide field reconnection at the magnetopause is studied with data from the Magnetospheric Multiscale mission and a theoretical model. Inside the EDR where the electron beta is high (β ~ 5), the long-wavelength electromagnetic LHDW propagating obliquely to the local magnetic field is observed. In contrast, the short-wavelength electrostatic LHDW propagating nearly perpendicular to the local magnetic field is observed slightly away from the EDR, where β is small (~0.6). These observed LHDW features are explained by a local theoretical model only after including effects from the electron temperature anisotropy, finite electron heat flux and parallel current. The short-wavelength LHDW is capable of generating significant drag force between electrons and ions.

Oceanic mesoscale eddies play an important role in preconditioning and restratifying the water column before and after mixing events, thereby affecting deep water formation variability. In the Labrador Sea, where deep convection occurs regularly, observations and models indicate a complex interplay of turbulence and associated tracer fluxes. Results from a realistic eddy-resolving (~5 km local horizontal resolution) ocean model in quasi-equilibrium (~300 years integration) suggest that small-scale temperature fluxes due to turbulent potential to kinetic energy conversion are the main driver of mixed layer restratification during deep convection triggered through atmospheric forcing. In addition to these baroclinic instabilities, buoyant water masses must be provided by the boundary current, where barotropic turbulence is equally important. Only acting together, the destabilizing forcing can be balanced. In a low-resolution control simulation (~20 km) the modeled turbulence is strongly reduced and the associated modeled and parameterized heat fluxes too weak to increase stratification.

The Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) launched in Jan. 2009 has demonstrated accurate and precise CO2 and CH4 distribution measurements from space. The globally acquired data have contributed to reduce the uncertainties in global and regional flux inverse estimates. In response to the urgent need for monitoring carbon emissions from intense localized sources, such as cities and power plants, we have been developing a next generation instrument that should be able to detect and map plumes from the intense sources with a 1km spatial resolution. We design our system to implement both targeted observations for intense local sources with high spatial resolution and wide-swath observations for covering the earth’s entire surface with 2-axes pointing system and two telescopes. We have developed airborne imaging spectrometer suites with three imaging spectrometers: 0.47 µm for nitrogen dioxide (NO2), 0.76 µm for oxygen (O2) and solar-induced chlorophyll fluorescence (SIF) and 1.6 µm for carbon dioxide (CO2) and methane (CH4). The airplane observations successfully recorded CO2 and NO2 enhancements over a power plant in Greater Nagoya Area. In our presentation, we will present our first emission estimates based on the simultaneous CO2 and NO2 observation.

Hydroxyl (OH) and hydroperoxyl (HO2) drive the atmosphere’s oxidation of gases emitted from Earth’s surface and the formation and aging of aerosol particles. Thus, understanding OH and HO2 chemistry is essential for examining the impact of human activity on future atmospheric composition and climate. Using the OH and HO2 dataset collected with the Penn State Airborne Tropospheric Hydrogen Oxides Sensor (ATHOS) during nine aircraft missions over the past 20 years, we compare observed OH and HO2 to that modeled using the same near-explicit photochemical box model. In general, the agreement is well within the uncertainties of the observations and models, even when the model is constrained only with a common data set of simultaneous measurements. However, in regions influenced by anthropogenic or biogenic volatile organic compounds, the model chemical mechanism and size of the data set of constraining measurements do matter. In cleaner regions, the differences between observed and modeled OH and HO2 found in previous studies generally remain and do not appear to be systematic, indicating that the differences are driven by measurement issues for ATHOS and/or other instruments. Thus, these comparisons indicate that the oxidation chemistry in most of the free troposphere is understood to as well as current measurements can determine. The focus of future research needs to be on regions rich in volatile organic compounds, where observed-to-modeled differences are more persistent, and on improving measurement consistency.

Atmospheric concentrations of CH4 have tripled since the Industrial Revolution. One culprit of this increase is animal agriculture, contributing 8 to 10% of global greenhouse gas emissions primarily in the form of CH4. According to US Environmental Protection Agency greenhouse gas inventory estimates, the majority of the manure emissions are from manure management on dairy farms (53%). Most of these manure emissions are generated from liquid manure in anaerobic lagoons. Thus, accurate estimates of the emissions from these lagoons are essential for developing management strategies to reduce CH4 emissions. Emissions of methane from two manure lagoons, one in Southern California and the other in Central California, were estimated by fitting results from a state-of-the-art dispersion model to CH4 concentrations measured with a mobile monitor. The sampling was conducted by stationing the mobile monitors at several locations (29-42) around the lagoons for time intervals ranging from 10 to 15 minutes. A sonic anemometer provided micrometeorological measurements used by the dispersion model. Emissions were computed by fitting the time-averaged methane concentrations to model estimates. The 95% confidence intervals for the emissions were computed by bootstrapping pseudo observations created by adding residuals between model estimates and corresponding observations to the best fit model estimates. The coefficient of determination, r2, between model and measurements made at the Southern California dairy was over 0.86 and the geometric standard deviation (sg) was 1.1; the steady westerly wind direction was a major factor for this result. At the Central California dairy, the winds were light and variable resulting in an r2 of about 0.9 and a high sg of 1.4. The sensitivity of the emission estimates to wind direction was determined by running the dispersion model for different wind sectors. We found that the emission estimates were within 1.5 times of each other under all wind conditions. The dispersion model was cross-validated by estimating the emissions using only half the total receptors and then predicting the concentration at other receptors using this emission rate. This technique can be used to improve methane emission estimates in manure management and to assess the effectiveness of the different strategies to reduce emissions.

Dust impacts on spacecraft are commonly detected by antenna instruments as transient voltage perturbations. The signal waveform is generated by the interaction between the impact-generated plasma cloud and the elements of the antenna – spacecraft system. A general electrostatic model is presented that includes the two key elements of the interaction, namely the charge recollected from the impact plasma by the spacecraft and the fraction electrons and cations that escape to infinity. The clouds of escaping electrons and cations generate induced signals, and their vastly different escape speeds are responsible for the characteristic shape of the waveforms. The induced signals are modeled numerically for the geometry of the system and the location of the impact. The model employs a Maxwell capacitance matrix to keep track of the mutual interaction between the elements of the system. A new reduced-size model spacecraft is constructed for laboratory measurements using the dust accelerator facility. The model spacecraft is equipped with four antennas: two operating in a monopole mode, and one pair configured as a dipole. Submicron-sized iron dust particles accelerated to > 20 km/s are used for test measurements, where the waveforms of each antenna are recorded. The electrostatic model provides a remarkably good fit to the data using only a handful of physical fitting parameters, such as the escape speeds of electrons and cations. The presented general model provides the framework for analyzing antenna waveforms and is applicable for a range of space missions investigating the distribution of dust particles in relevant environments.

The usefulness of satellite multisensor precipitation products such as NASA’s 30-minute, 0.1° Integrated Multi-satellitE Retrievals for the Global Precipitation Mission (IMERG) is hindered by their associated errors. Reliable estimates of uncertainty would mitigate this limitation, especially in near-real time. Creating such estimates is challenging, however, due both to the complex discrete-continuous nature of satellite precipitation errors and to the lack of “ground truth” data precisely in the places—including complex terrain and developing countries—that could benefit most from satellite precipitation estimates. In this work, we use swath-based precipitation products from the Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR) as an alternative to ground-based observations to facilitate IMERG uncertainty estimation. We compare the suitability of two DPR derived products, 2ADPR and 2BCMB, against higher-fidelity Ground Validation Multi-Radar Multi-Sensor (GV-MRMS) ground reference data over the contiguous United States. 2BCMB is selected to train mixed discrete-continuous error models based on Censored Shifted Gamma Distributions. Uncertainty estimates from these error models are compared against alternative models trained on GV-MRMS. Using information from NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis, we also demonstrate how IMERG uncertainty estimates can be further constrained using additional precipitation-related predictors. Though several critical issues remain unresolved, the proposed method shows promise for yielding robust uncertainty estimates in near-real time for IMERG and other similar precipitation products at their native resolution across the entire globe.

The increase of vegetation greenness in the Northern latitudes suggests a rise in the fixation of CO2 by photosynthesis, but the observed upward trends in respiration could compensate for elevated uptake by photosynthesis, necessitating the monitoring of variation in vegetation structure and carbon (C) storage at very high spatio-temporal resolution. Compared to passive optical remote sensing, Light Detection and Ranging (Lidar) scanners may improve the quantification of C sink by providing 3D information of plant structures without apparent sign of saturation of spectral response over dense canopies. We evaluate a novel approach to precisely map C sequestration and key metrics describing the 3D canopy structure of a temperate agricultural expanse by implementing drone-borne Lidar scanner technology and deep learning (DL) architectures potentially capable of detecting individual plants and associated geometrical properties while deriving their above ground biomass (AGB) from point cloud datasets originating from the scanner. An intensive aerial and field campaign was carried out over an Integrated Carbon Observation System (ICOS) class 1 station site (60 ha) in Denmark to remotely measure the horizontal and vertical canopy structure at 15-day intervals during the vegetation growing period, and to collect ground truth data of crop growth in terms of height, density, AGB and green area index of more than 1200 plants. The point cloud data are processed using pattern recognition tools to remove noise and classify them to ground and non-ground points. Two DL models specifically designed to handle the irregular structure of raw point clouds are trained to extract features of vegetation by labeling the processed point cloud data; DL’s suitability for assigning semantic information on 3D data representing cropland is assessed by validating them with the field-based observations. In combination with tower-based flux data, the application of Lidar and DL technologies appear to offer a characterization of the dynamic interaction between climatic conditions, vegetation growth, C sink, water and CO2 fluxes suitable to the challenge of assessing the rapidly changing northern landscapes.

The Canada Basin has exhibited a significant trend toward a fresher surface layer and thus a more stratified upper ocean over the past three decades. Here, we explore the extent to which the Community Earth System Model (CESM) accurately simulates the observed surface freshening and seasonal processes that contribute to the freshening. We examine 30 simulations from CESM1 (used in the IPCC AR5), 3 simulations from CESM2 (IPCC AR6), and ocean observations from 1975 and 2006-2012. In contrast to the observations, the models simulate salinity profiles that show relatively little variation between 1975 and 2012. We demonstrate that this bias can be partly attributed to the model’s tendency to mix freshwater too deep, creating a surface layer that is saltier than observed. The results provide insight for climate model improvement that could have wide-reaching implications because upper-ocean stratification influences the vertical transport of heat and nutrients.

Seismic velocity structures have been estimated using ambient noise records observed by distributed acoustic sensing (DAS) technology. Previous studies have obtained S-wave velocity (Vs) structures along submarine cables using surface waves; however P-wave velocity (Vp) structure have seldom been constructed because ambient noise primarily contains surface waves. Here, we show P and Rayleigh wave extractions from ambient seafloor noise observed by DAS, and also estimate the Vp and Vs structures along a submarine cable deployed off Cape Muroto in the Nankai subduction zone, Japan. To extract the P waves, we calculated the cross-correlation functions (CCFs) of the ambient noise and applied a frequency-wavenumber filter to the CCFs to remove the effects of slower surface waves. The results indicate that similar features can be obtained in the Vp and Vs profiles, and that the P wave extractions can be performed on days with poor weather conditions.

West African precipitation is characterized by a strong variability from intra-seasonal to climatic scales. In this region, it typically rains from March to October with differences in the annual cycle of precipitation (e.g. Sow et al., Atmosphere 2020). Studies based on the Coupled Model Inter-comparison Project (CMIP) phase 3 and 5 show that the representation of the West African Monsoon (WAM) mean state and its response to global warming, are both highly model-dependent (Roehrig et al., J. Atmos. Sci. 2013). CMIP5 simulations display large inter-model spread in both the mean precipitation climatology and higher-frequency indices by overestimating the frequency of wet days (Sow et al, 2020). Likewise, strong uncertainties are found in the simulation of the consecutive wet and dry days (CWD and CDD respectively) over west Africa. In this study we analyse the added-value of the new generation models available from the CMIP6 archive in simulating precipitation and its extreme indices over West Africa. The same group of CMIP5 and CMIP6 models is evaluated with CRU and REGEN datasets for a long time period (more than 30 years) as well as more than four other observational datasets in a shorter time period (15 years). Results show that the inter-model spread as well as the overestimation of these indices slightly decreases in most CMIP6 historical simulations compared to their CMIP5 counterpart. When considering experiments where sea surface temperature (SST) is prescribed over a long period (1870-2014), performed in the Global Monsoons Model Intercomparison Project (GMMIP), we find more improvement in the annual structure of precipitation over the Guinean region with less inter-model spread compared to observations. The inter-annual variability of precipitation is then relatively well captured by all CMIP6 models with significant correlations. Almazroui et al. (2020) found that precipitation in CMIP6 models will increase in the central part and decrease in the western part of west Africa during the twenty-first century under Shared Socio-economic Pathways (SSP). In this study we further analyse how extreme precipitation indices are represented in SSP5-8.5 over West Africa.