The study of lunar plasma environment’s response to the extreme solar wind condition is the main subject of our investigation in this report. The computational model includes the self-consistent dynamics of the light (H_2+) and (He+), and heavy (Na^+}) pickup ions. The electrons are considered as a fluid. The lunar interior is considered as a weakly conducting body. The input parameters are taken from the ARTEMIS observations. The modeling demonstrates a formation of the various plasma structures near the Moon: (a) bow shock wave with split shock transition in case of extreme solar wind density and standard bulk velocity; (b) hyper-sonic/Alfvenic Mach cone in case of extreme solar wind bulk velocity and moderate solar wind density. The modeling shows a strong asymmetry in the solar wind ion VDF which connected with a plasma compression and ion reflection at the bow shock wave/Mach cone front.

The South American Monsoon System (SAMS) is the dominant mode of hydroclimatic variability in South America and the associated dynamics have an important influence on atmospheric organization during pluvial and drought periods. Paleoclimate records from the monsoon domain have helped to describe the isotope hydrology of this region over the last millennium, but are insufficient to explain why the system has changed. Utilizing transient simulations of last millennium climate from isotope-enabled global climate models as well as experiments forced by isolated external forcings made available from the NCAR Community Earth System Model Last Millennium Ensemble (CESM-LME) project, we explore the dynamical drivers of variability documented in the isotopic record. We find that while models continue to underestimate the temporal variability of regional climate, they are able to reproduce documented spatial modes of variability. A Monte Carlo empirical orthogonal function (MCEOF) analysis is used to decompose major modes of isotope variability from both isotope proxy records and isotope-enabled global climate models over the last millennium (850 – 1850 CE). The principal component time series from the isotopic records show clear mean state departures during the early and late part of the record, corresponding to the Medieval Climate Anomaly and Little Ice Age periods. We explore the role of internal variability and different external forcings in driving these departures, and use the model space to describe changes to regional circulation patterns during these periods of extreme isotopic change. This enhanced understanding of the drivers of variability over the last millennium could provide a foundation upon which to interpret the sensitivity of this system to future changes due to external forcings, such as anthropogenic activity.

The 2020 COVID-19 pandemic provided a unique opportunity to sample atmospheric gases during a period of very low industrial/human activity. Over 1000 Whole Air Samples were collected in over 30 cities and towns across the United States from April through July 2020 as part of the NASA Student Airborne Research Program (SARP). Sample locations leveraged the geographic distribution across the United States of the undergraduate and graduate students, faculty, and NASA personnel associated with the internship program (44 people total). Each person collected approximately 24 air samples in their city/town with the goal of characterizing local emissions with time during the pandemic. Samples were collected in 2-Liter stainless steel evacuated canisters at approximately 2 meters above ground level. The canisters were shipped to the Rowland/Blake Laboratory at the University of California Irvine and analyzed for methane, carbon dioxide, carbon monoxide, non-methane hydrocarbons, and halocarbons using the gas chromatographic system described in Colman et al. (2001) and Barletta et al. (2002). Initial samples collected in April coincided with the peak of stay-at-home/social distancing orders across most of the United States while samples collected later in the spring and early summer reflect the easing of these measures in most locations. Overall trends in emissions with time across the United States during the pandemic (in several large metro areas as well as rural locations) will be discussed.

ICESat-2, the first photon counting satellite, maps the earth’s surface with unprecedented details and accuracy. However, the resulting photon clouds, distributed as the ATL-03 geocoded photon product, are vast, unstructured, and noisy datasets. The high density and four-dimensional nature of the photon dataset (location and time), coupled with different responses over different surfaces (e.g., ice, forest cover, water), pose a unique and challenging problem regarding surface detection’s overall objective of intelligently reducing the data volume. Multiscale models uncover hidden structures in data due to their ability to analyze the underlying processes at multiple scales. Besides the traditional wisdom of using multiple scales for improving local and global approximations, in this work, we show their application as an intelligent sampling mechanism for redundant and noisy datasets. Our proposed approach’s fundamental idea is the generation of data dependent and multiscale basis functions and corresponding representative sparse representations, which retain points essential for minimizing the error of reconstruction. Thereby, points associated with rapid spatial change are chosen, while those that are easily reconstructed using the smooth basis functions are discarded. As the final output, the algorithm provides an efficient sparse representation of the data that captures all relevant features for modeling and prediction with quantified uncertainty. Our presentation includes a detailed description of the algorithm and theory as applied to process the ATL-03 geolocated photon product of the ICESat-2 mission. We will demonstrate the efficiency of our approach by examples of different ice sheet surfaces, including heavily crevassed glaciers, that pose a challenge for currently used change detection methods.

Lithospheric yield stress is a key parameter in controlling tectonic processes. We calculate yield stress for a range of conditions appropriate to the Archean Earth, including hotter mantle potential temperatures and a range of Moho temperatures using 2D high resolution numerical geodynamic modelling techniques. This range of conditions are evaluated for generating felsic, tonalite-trondhjemite-granodiorite (TTG), crust with the results bench marked against the preserved rock record. The model results indicate that lithospheric yield stress slightly lower than the present-day Earth values (i.e. < 100 MPa) generates TTG melt volumes similar to those preserved in the rock record. In particular, large volumes of TTG melts form in the tails of lithospheric drips. Melting occurs profusely within the thinner portions of the drips as these regions are more ef�ciently heated by the enclosing hotter mantle. In contrast, only limited melting occurs in regions of thickened crust, in part because the weaker lithosphere cannot sustain crustal thickening for long time periods, resulting in its removal through drips. Our models highlight the dominance of non-plate tectonic mechanisms in producing TTGs under the conditions that operated on the hotter Archean Earth.

Rotary shear (RS) experiments have been used to characterize the deformational behavior of materials and attempt to understand earthquakes. Typical RS experiments test materials under a prescribed slipping velocity and normal load. Yet, in natural earthquakes, fault nucleation, growth, termination, and slipping velocity are not predefined, but a result of the stored and released energy around the seismic fault. Here we present new measurements performed with a RS apparatus designed to be more representative of a natural system. The device uses a clock spring that when loaded by a motor imposes a linearly increasing torque to the sample. Thus, events occur spontaneously when the shear stress exceeds the static shear stress acting on the surfaces in contact. We report the results of experiments using solid poly(methyl methacrylate) (PMMA) and granular samples of polyvinyl chloride powder (figure), glass microbeads, silica powder, and crushed quartz. PMMA experiments were started at a spring loading rate (SLR) of ~2.5 RPM, where we observed low amplitude stick-slip events occurring at regular recurrence intervals. The SLR was then increased to ~12.5 RPM where after a transition period, temperatures during slip events exceeded the melting point of PMMA (~160ºC). This formed a melt layer that cooled and bonded the slipping surfaces. The friction coefficient just before rupture and the amount of weakening increased as a function of the amount of melt produced. Granular experiments were conducted at a SLR of ~2.5 RPM and variable normal stresses (0.1-0.5 MPa). The granular samples show strain hardening just before rupture, followed by strain softening and marked changes in behavior with varying water content. Since the behavior of PMMA is comparable to that of rocks at depth (McLaskey and Glaser, 2011), results of PMMA tests yield insight into precursory and coseismic events, fault strengthening/weakening mechanisms, and perhaps, the formation of pseudotachylite glass. Experiments with granular samples allow us to characterize each material’s behavior in response to variable water content, SLR, and normal stress. We conclude that analog materials are valuable to simulate the behavior of the seismogenic brittle lithosphere. From such experiments, we can gain insight into stick-slip mechanisms relevant to earthquakes.

The American Geophysical Union (AGU) has extensive journal submission and annual meeting abstract data that we can use to study effects within the Earth and space science (E&SS) community. We had provided an initial analysis based on data through June of 2020 (Wooden, 2020), and here we provide journal submission data analysis through October 2020 and analyze author team demographics of both journal submissions and our annual Fall Meeting abstracts. This most recent analysis shows: 1) Monthly journal article submissions have increased in 2020, while the proportion submitted by women have remained at 23% with monthly fluctuations of +/- 1-3 points. 2) 2020 journal article submissions from China surpassed those from the U.S. in every month except April. However, AGU Fall Meeting abstracts from China decreased 57% compared to last year. 3) Submissions from men and women in their 20s decreased while submissions from women in their 30s and 50s and men in their 40s increased. 4) Among journal articles and meeting abstracts, average author team size has increased; the proportion of mixed gender teams have increased while all-male teams have decreased. All-female teams have remained steady. 5)The age of submitting abstract authors has decreased but surprisingly with a smaller proportion of students submitting and a larger proportion of early-career researchers submitting abstracts.

Background and Methods: The study was performed to investigate the demographic factors impacting COVID-19 cases in Virginia Zip Code Tabulation Areas [VZCTA] in 5 of the Virginia’s health planning regions (VHPRs). The data was collected from the Virginia Department of Health (VDH), spanning 5/15/2020-8/6/2020 (3 months) during the state’s first COVID-19 peak. Kruskal-Wallis with Bonferroni correction was used to compare the distribution of COVID-19 cases and demographic factors between the VHPRs. Pearson correlation was employed to determine correlations between COVID-19 Cases and demographic factors in VZCTA and VHPRs. Results: Incidence of COVID-19 was the highest in the suburban Northern region and the lowest in the rural-predominant southwestern region, 1017 vs. 420 per 100K population (p <0.05) (Table 1 for details). Overall state-wide and in almost all the VHPRs, the VZCTA with predominantly Hispanics and Blacks ethnicity and high PCR testing rate were strongly associated with COVID-19 incidence in the univariate analyses. Interestingly, the younger age and household crowding (> 1.5 occupants/ room) were also associated with higher COVID-19 cases state-wide and in the Northern VHPR in the univariate analyses. In the multivariate analyses, Hispanic/Black ethnicity was strongly associated with a higher COVID-19 incidence, especially in the Northern region. Considering demographic factors alone, ethnicity, median household income, and household crowding were the most important predictor of the COVID-19 incidence in Virginia ZCTA communities in multivariate analyses with a few important regional differences. The multivariate model’s R-value is 0.819 in the Northern region. Conclusions: The study highlights ethnicity as an essential social vulnerability in the contraction of COVID-19, which is also modified by the other factors in a regional manner accounting for the disparity in COVID-19 incidence across VHPRs. The information can guide critical public health decisions, e.g. vaccine distribution or implementation of critical health policies based on the social vulnerability in smaller population units - ZCTAs. As Virginia represents the average U.S. population concerning overall health and COVID-19 cases, the findings are likely to be generalizable to the U.S. population at large.

Fine particulate matter smaller than 2.5 micrometers (PM2.5) at the surface presents large health risks to the public. Providing long-term, spatially continuous data set of PM2.5 surface concentrations can support decision making by health departments, air agencies, as well as other stakeholders interested in public health. In this work, a website-based decision support tool will be shown. The tool includes two main parts. The first part is a multi-year, daily, 3-km database for PM2.5 concentrations in California from 2006 to present in both map and tabulate formats. The second part is a daily, 3-km, near real-time update of PM2.5 surface concentrations in California in the last seven days. The near real-time data are generated within 24 hours after the NASA A-train satellite swath. For both products, PM2.5 surface estimates are generated based on a fusion of EPA ground-based monitored data and satellite-derived PM2.5 data from the NASA Aqua/MODIS satellite sensor. This decision support tool can be used to provide quick and easy visualization of certain episodic events (such as California wildfires) on a daily basis. It can also be used to conduct multi-year statistical analysis, such as identifying the higher PM2.5 concentration days above 95th percentile as the days being impacted by wildfires. These datasets and the decision support tool is publicly available and can be accessed via the SJSU HAQAST website: http://www.met.sjsu.edu/weather/HAQAST/home.html

The geospatial Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC; formerly GeoSPEC) pioneers an on-demand science data processing system (SDPS) producing user-customized Level 1 calibrated radiance to Level 3+ data products in anticipation for the 2017-2027 Earth Decadal Survey prioritized spaceborne global imaging spectrometer to advance the study of Surface Biology and Geology (SBG). SBG data volumes (~20 TB/day) of high dimensionality (>224 bands) would be infeasible to download and the breadth of applications of the data across dozens of disciplines presents a need to evolve the traditional NASA SDPS. ImgSPEC streamlines processing data into key SBG observables that have demonstrated algorithms at local-to-regional scales and may vary locally. As such, a traditional, monolithic SDPS could not fully exploit the information in SBG measurements. To remove this barrier to use, ImgSPEC demonstrates an on-demand SDPS prototype that improves imaging spectroscopy data discovery, access, and utility enabling shared knowledge transfer from advanced imaging spectroscopy users to less experienced users such as decision makers and the general public. We test three use cases: 1) standard data processing workflows, 2) customized variants of standard workflows, and 3) algorithm development of new workflows. We create collaborative algorithm development environments that offer services typically restricted to NASA SDPSs such as data product provenance and bulk processing. We leverage existing NASA-funded information technologies such as the hybrid on-premise/ cloud science data system (HySDS), the Multi-mission Algorithm and Analysis Platform (MAAP), ECOSIS – a crowd-sourced spectral database, and ECOSML – a crowd-sourced model database. We demonstrate ImgSPEC on the Terrestrial Ecosystem use case processing through to foliar traits and fractional cover, thus aligning with driving thrusts for the SBG Science and Applications Communities.

Launched in October 2018, the ESA/JAXA BepiColombo mission is currently in cruise to reach Mercury in late 2025. The Mercury Orbiter Radioscience Experiment (MORE) is one of the 16 instruments hosted on board the spacecraft. Testing general relativity is among the primary objectives of MORE. Superior conjunction experiments (SCE) will be performed during the interplanetary trajectory, with the aim of obtaining an accurate estimate of the post-Newtonian parameter γ. This is allowed by MORE advanced radio tracking system which provides precise range and Doppler data almost at all solar elongation angles, thus enabling an accurate measure of the relativistic time delay and frequency shift undergone by the signal when the spacecraft is in a superior solar conjunction (SSC). The rst BepiColombo SCE will take place in March 2021, and others will follow during the cruise phase. The nal objective is to place new limits to the accuracy of the general relativity as a theory of gravity in the weak eld limit, improving previous result from the Cassini SCE (Bertotti et al. 2003), which was able to determine that γ-1=(2.1±2.3)×10-5. Because of the proximity to the Sun, the spacecraft will undergo severe solar radiation pressure acceleration, and the effect of the random uctuations of the solar irradiance may become a major concern. We address the problem of a realistic estimate of the outcome of the SCE of BepiColombo, by including the effects of solar irradiance random variations in the dynamical model. We analyzed the experiment under different assumptions on the ranging system performances, observation coverage and solar activity showing their impact on the attainable result. We propose a numerical method to mitigate the impact of the variable solar radiation pressure on the scienti c result. Our simulations show that, exploiting data from multiple SSCs, the accuracy obtainable in the relativistic time delay measurement is 13×10-6 for a strong solar activity, and 6×10-6 for weak irradiance uctuations. We found that the latter result can be obtained by the rst SSC alone if the plasma noise calibration works until the impact parameter reaches 6 solar radii.

A key challenge in accurate simulations of desert dust emission is the parameterization of the threshold wind speed above which dust emission occurs. However, the existing parameterizations yield a unrealistically low dust emission threshold in some climate models such as the Community Earth System Model (CESM), leading to higher simulated dust source activation frequencies than observed and requiring global tuning constants to scale down dust emissions. Here we develop a more realistic parameterization for the dust emission threshold in CESM. In particular, we account for the dissipation of surface wind momentum by surface roughness elements such as vegetation, rocks, and pebbles, which reduce the wind momentum exerted on the bare soil surface. We achieve this by implementing a dynamic wind drag partition model by considering the roughness of the time-varying vegetation as quantified by the leaf area index (LAI), as well as the time-invariant rocks and pebbles using satellite-derived aeolian roughness length. Furthermore, we account for the effect of soil size on dust emission threshold by replacing the currently used globally constant soil median diameter with a spatially varying soil texture map. Results show that with the new parameterization dust emissions decrease by 20–80% over source regions such as Africa, Middle East, and Asia, thereby reducing the need for the global tuning constant. Simulated dust emissions match better in both spatiotemporal variability and emission frequency when compared against satellite observed dust activation frequency data. Our results suggest that including more physical dust emission parameterizations into climate models can lessen bias and improve simulation results, possibly eliminate the use of empirical source functions, and reduce the need for tuning constants. This development could improve assessments of dust impacts on the Earth system.

Woody plants vary greatly from tall trees to branching shrubs with increasing dryness. Variations in plant allometry are driven by both biotic and abiotic factors, reflecting different plant adaption strategies in different environments. While much is known about the response of plant functional traits to declining rainfall, less is known about how aboveground allometry (e.g. canopy size, height, stems, branching) of woody plants might respond to increasing dryness, limiting our ability to predict changes in woody plants and associated ecosystem functions under future climate change scenarios. Here, we explore how aboveground allometry of different woody genera responds to increasing dryness at 150 sites long an extensive aridity gradient from humid to arid areas. We used regression analyses and Structural Equation Modelling to explore the variation in woody allometry with increasing aridity, and the abiotic (resource availability) and biotic (competition) mechanisms driving such changes. Our results showed that plant height declined, but branching, and canopy width and depth increased with increasing aridity. Woody responses to dryness varied among genera, with increasing aridity associated with wider canopies in Eucalyptus and Callitris, thicker stems in Acacia, but no clear differences in Allocasuarina. Biotic and abiotic factors exerted different effects on the allometry of different genera, with Eucalyptus and Callitris spp. constrained by resource availability, while Acacia and Allocasuarina spp. were regulated mainly by competition. Our results highlight the genus-specific responses in allometric changes and driving mechanisms (resource availability cf. competition) with increasing dryness. Rather than merely shrinking, plants would allocate resources to either canopies or stems to cope with increasing dryness. Under predicted hotter and drier climates, increasing stem or canopy size, and altering branching might be a useful strategy for woody plants to compensate for biomass reduction and maintain ecosystem functions while growing shorter as dryness increases.

Ice motion in land terminating regions of the Greenland Ice Sheet is controlled in part by meltwater input into moulins. Moulins, large near-vertical shafts that deliver supraglacial water to the bed, modulate local and regional basal water pressure and ice flow by influencing subglacial drainage efficiency on daily to seasonal timescales. Our previous modeling work found that the geometry of a moulin near the water line has substantial effect on subglacial water pressure variations. Here, we develop a new physically based moulin model which can help constrain moulin shape across the ice sheet and its influence on hydraulic head oscillation, and inform the englacial void parameter used in glacier hydrology modeling. The Moulin Shape (MouSh) model (in Matlab and Python) provides new insight into the evolution of subsurface moulin size and shape at hourly to multi-year timescales. The modeled moulin is initialized as a vertical cylinder. The moulin walls melt back above and below the water line due to the dissipation of turbulent energy, open or close due to viscous and elastic deformation, and freeze inward in winter when cold air temperatures and an absence of meltwater allow refreezing. We combine MouSh modeling results with geometric data from two moulins in Pâkitsoq, western Greenland, which we mapped to the water line. The moulins have heterogeneous shapes and volumes in the top 100 m. This suggests that the size and shape of the upper portion is controlled by local and regional pre-existing fractures, which provide preferential paths for water flow and melting, creating stochastic karst-like conduit shapes. Modeling results show that moulin geometry below the water line is influenced by the hydraulic head, which controls the depth-dependent elastic and viscous closure rates, and by the roughness of the walls, which enhances melt-out rates that oppose moulin closure. We show that subglacial water pressure across the ice sheet is likely influenced by moulin geometry, underscoring the need for including moulins in subglacial models.

Conventionally, radar-based nowcasting methods have been conducted by shifting or extrapolating the most recent estimated precipitation field along the estimated motion field to lead times up to 3 hr. However, such methods assume that these fields do not evolve during the lead-time period. A more recent nowcasting approach is the short-term ensemble prediction system known as STEPS. STEPS aims to filter small spatial scales of rain as they have an expected short life-time compared to large spatial scales. Besides, it perturbs the precipitation field during extrapolation so that the prediction uncertainties related to the evolution of precipitation is considered, resulting in an ensemble nowcast. In this work, the configuration, implementation, and performance of STEPS are studied for its radar-based ensemble nowcasting application in Germany. Attention is given to the spatial localization (i.e., the adjustment of parameters that control the spatio-temporal evolution of precipitation in large domains). For such purpose, the capability of STEPS is analyzed using multiple rain events collected by the German radar network. Preliminary analysis regarding the spatial scale filtering of STEPS (without perturbation) shows an improvement against conventional extrapolation of 20 to 30 % on the critical success index starting at lead times of 30 min at a rainfall threshold of 0.1 mm hr-1. In terms of the spatial structure, STEPS (without perturbation) provides a consistent nowcast for lead times up to 3 hr. Although small spatial structures are filtered, the spatial structure at scales of 32 km or larger is maintained. The skill of a probabilistic nowcast (STEPS with perturbation) was also analyzed. It is shown that for the probability of exceeding the 5.0 mm hr-1 threshold, the predictability of the nowcast is compromised for lead times on the order of 1 hr. However, the capability of identifying rain and non-rain areas is still valuable: the hit rates are still larger than the false alarm rates. Our preliminary results highlight aspects needed in the configuration of STEPS such as the evolution model and the perturbation model. In this manner, this study can serve as a basis for an improved nowcasting system in Germany and as a reference for forecasters to understand the characteristics of the examined nowcast system.

Distribution: A. Approved for public release – distribution is unlimited QuakeCast is a novel system for short-term earthquake prediction using global ionosphere Total Electron Content (TEC) data. In the last 20 years, earthquakes have caused over 800,000 deaths and $650 billion in economic damage. While current seismic early warning systems provide up to a minute’s notice by detecting an earthquake’s non-damaging P-waves prior to the damaging S-waves, earlier warnings could allow more significant emergency preparations. Electromagnetic ionospheric phenomena have recently been observed many days before major earthquakes, notably in 2015 in Nepal. QuakeCast explores whether such signals could be used to predict earthquakes in a global dataset of ionosphere and earthquake data. The ionosphere data consists of global TEC data from NASA’s Crustal Dynamics Data Information System (CDDIS), for the years 2005-2015 at 15 minute intervals. The earthquake data consists of over 10,000 events of at least M5 from the International Seismological Centre (ISC-GEM) Global Instrumental Earthquake Catalogue over the same time period. We used the dataset to explore the following classification problem: given a 24-hour sequence of ionosphere TEC data in a 30° latitude by 30° longitude window, is the sequence preseismic (an earthquake occurs in the next time step) or nominal (no earthquake)? We built two models to address this. The first is a classical logistic regression model trained on radially binned data. Analysis of the classifier weights indicate a localized effect in the ionosphere: TEC data spatially and temporally closer to the event contributes more significantly to the prediction. The second model is a deep learning ConvLSTM autoencoder trained to reproduce nominal TEC data sequences. Since autoencoders reproduce data which resemble their training data better than data which does not, we used the reconstruction error to classify sequences as anomalous (preseismic) or nominal. Both methods were found to perform significantly better than a random null classifier, indicating that the ionosphere contains information useful for predicting earthquakes at least 15 minutes in advance. While further research is needed to incorporate additional data features and address noise, we believe that these results are a promising development toward forecasting earthquakes using geoelectric signals.

Public health communication strategies, including entertainment-education, can effectively change human behavior, improving health outcomes from climate change. Tools from social psychology, including social modeling and building self and collective efficacy, can help us to create a new model for current, culturally-relevant stories that can help communities adapt to climate change. As an example we will share key learnings from Rhythm and Glue, an applied television prototype, based on research from an NSF Advancing Informal STEM Learning submission. Best practices for climate communication include adaptations of entertainment-education techniques for culturally grounded representations of climate engagement positive outliers. As science communication progresses in adapting social psychology and sociology practices for climate communication, we would like to share how this prototype applies the methods and suggests some new directions that further adapt the practices to account for limited resources and media fragmentation challenges. While this work focuses on climate, it has broad implications for future science communication practices.

Direct radiative forcing is a major impact of atmospheric dust aerosols. Mineral dust is an important aerosol component that interacts with both incoming and outgoing radiation, modulating radiative fluxes on Earth and its atmosphere. Dust radiative impact directly depends on the particles physico-chemical characteristics (e.g. mineralogy, shape, size) which are the main source of uncertainties. Spectral signatures of dust particles in the visible/short-wave infrared (V/SWIR) and long-wave infrared (LWIR) are linked to mineral composition and variability. Obtaining the precise spectral signature and size distribution of dust particles can result in accurate derivation of refractive indices which are used as inputs to model radiative forcing. In this work, V/SWIR and LWIR reflectance spectra of heterogeneous dust samples from the United States, East Asia, and Middle East were analyzed for their mineral abundances. For this study we used global well-characterized soil samples with comparable mineral compositions to windblown dust. The soil samples cover a wide range of mineral compositions and represent both arid and semi-arid regions [J P Engelbrecht et al., 2016]. Our preliminary analysis used linear spectral mixing for both V/SWIR and LWIR reflectance spectra. This approach is the simplest method to determine mineral abundances from reflectance spectra. While this resulted in a very low RMSE for the fit between the sample and modeled spectra, modeled spectra did not match band centers and strengths for all features. We also converted reflectance spectra to continuum removed (CR) and mean optical path (MOPL) which have the potential to eliminate nonlinear effects (e.g. multiple scattering) in spectral mixing. These approaches, which modify the reflectance hull, significantly weakened or removed the calcite absorption features at 2375 nm. Because these samples are very fine grained (< 38 µm [J P Engelbrecht et al., 2016]) multiple scattering effects are expected to be important for both V/SWIR and LWIR spectral ranges and as our initial results show linear mixing is insufficient to produce reasonable mineral abundances. Our next efforts will include full radiative transfer models of the measured spectra which we will present at the meeting.