Snow and ice melt processes are a key in Earth’s energy-balance and hydrological cycle. Their quantification facilitates predictions of meltwater runoff as well as distribution and availability of fresh water. They control the balance of the Earth’s ice sheets and are acutely sensitive to climate change. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), that require imaging spectroscopy to map and measure. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and spaceborne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in an inversion that also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. A validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet gave uncertainties of ±16.4 μg/g_ice and less than 3%, respectively. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach’s potential and suitability for upcoming orbital imaging spectroscopy missions.

Excess nutrient loading to downstream waters has been a persistent environmental concern, especially in agricultural settings. Drainage water management (DWM) is a best management practice intended to reduce nitrogen export from fertilized lands by increasing groundwater levels, slowing the loss of nutrient-rich water and increasing its time in contact with the soil, thus creating greater opportunity for denitrification. This BMP has shown to be effective at reducing dissolved nitrate (TDN) export, but a question remains about potential unintended pollution swapping. The concern is that denitrification could result in nitrous oxide (N2O) emissions and that higher soil moisture could also create suitable conditions for methanogenesis and methane (CH4) emissions. Here we report on two years of monthly static soil gas chamber fluxes and hydrologic nutrient fluxes during a full corn/soybean rotation cycle on the Eastern Shore of Maryland. For N2O, there were significant interactions between season, crop type, and treatment, such as higher fluxes during the fertilization period in the corn year in the DWM treatment, which was consistent with our concern about pollution swapping. However, this brief additional pulse of N2O did not result in a statistically significant increase at an annual scale, nor was there an increase in annual CH4 emissions. At the same time, annual TDN load was significantly lower in the DWM ditches compared to the control. With no significant treatment effect on soil gas fluxes and a significant treatment effect on TDN export, we conclude that pollution swapping of nitrate reduction for greenhouse gases did not occur significantly in this application of DWM to a corn/soybean system. We did, however, find evidence of pollution swapping of phosphorus and nitrogen, as total phosphorus load was higher in the DWM. With more water in the field, the reduced conditions appear to cause a release of soil bound phosphorus. While greenhouse gas production may not be as much of a concern, increased phosphorus export represents a form of pollution swapping that must be accounted for in determining the value of this BMP.

The National Water Center (NWC), part of the National Weather Service’s (NWS) Office of Water Prediction (OWP), seeks to provide forecast flood inundation maps (FIM) as services at the national scale along 2.7+ million river reaches. The National Water Model (NWM) forecasts stream discharges along these reaches at varying time horizons. Building on the methodology of previous versions, OWP FIM 3.0 seeks to map NWM stream discharges to inundation extents with a modified version of the Height Above Nearest Drainage method for mapping stages. FIM 3.0 produces required datasets for generation of FIMs, which are relative elevations, reach-level catchments, rating curves and crosswalk tables, to convert the NWM discharge forecasts to inundation extents as a post-processing step. Computational tests estimate that producing these FIM required datasets for the continental U.S. will be around 600 total CPU hours. Only publicly available input datasets from the NWM or NHDPlusHR are required. The latest methods leveraged within FIM 3.0 include the use of consistent reach lengths and deriving FIM outputs at lower stream densities (downstream of River Forecast Center forecast points) to mitigate catchment boundary limitations. FIM 3.0 is primarily parallelized across Hydrologic Unit Codes (HUC) 4, 6, or 8 as processing units which can be selected by the user at run time. FIM capability improvements were measured by comparisons to engineering scale maps using contingency statistics. Future versions will support NWM geospatial dataset production, usage of Lidar elevations, and more advanced hydrological methods. FIM 3.0 is highly configurable, modular, and portable due to its software architecture and containerization. All software dependencies are open-source including GDAL/OGR, TauDEM, and RichDEM. The source code and Docker container are expected to be made available publicly for community review and development.

Human-mediated climate change over the past century has made significant impacts on global ecosystems and biodiversity including accelerating redistribution of the geographic ranges of species. In mountainous regions, the transition zone from continuous closed-canopy subalpine forests to treeless alpine tundra areas at higher elevations is commonly referred to as ‘Alpine Treeline Ecotone’ (ATE). Globally, warming climate is expected to drive the ATE upslope, which could lead to negative impacts on local biodiversity and modify ecosystem function. However, existing studies rely primarily on field-based data which are difficult and time consuming to collect. In this research, we define three critical characteristics of the ATE including 1) an abrupt spatial shift in vegetative activity as elevation varies, 2) reduction in vegetative activity as elevation increases, and 3) vegetative activity is at an intermediate level. Using the geospatial tools provided by Google Earth Engine, we construct an index (ATEI) to identify areas with the three ATE features based on the image gradients of vegetative activity and elevation datasets. Based on the ATEI and Google Earth imagery in 115 Landsat pixels, we establish a Logistic regression model to estimate the probability of whether or not a sampled pixel is located within the ATE. The prediction accuracy is approximately 80%. Furthermore, the ATEI-estimated ATE elevation is strongly correlated (r = 0.96) with a set of field-based data at 20 sampling sites from across the region. Based on the average annual ATEIs from 2009 to 2011, we estimate the average ATE elevation for each mountain range in the western U.S. The result varies from 1,183 m to 3,584 m. The detection metric developed in this study facilitates monitoring the geographic location and potential shifts of ATEs as well as the general impact of climate change in mountainous regions during recent decades. We also expect this approach to be useful in monitoring other ecosystem boundaries.

The distributions of mosquito vectors are expected to shift with rising temperatures due to climate change. But other global change patterns, like land cover change and human population growth, are simultaneously occurring. How will these changes interact to shift the future distributions of these vectors? Here, we analyzed how climate, land cover, and human population density regulate and predict habitat for two mosquito species, Aedes aegypti and Ae. albopictus, the primary vectors of dengue, Zika and chikungunya. We asked the following questions: How do environmental response curves derived from vector occurrence data compare to lab-derived responses? Based on these environmental response curves, which environmental drivers best predict the spatial distribution of each vector? Are environmental responses derived from large-scale (continental) occurrence data consistent at fine spatial scales? To answer these questions, we analyzed 6,317 Ae. aegypti occurrence records, 3,629 Ae. albopictus records, 10 satellite-derived environmental covariates, and two independent field surveys cover 134 sites. We found close agreement in the range of lab and environmental temperature responses, though the mean of observed temperatures was higher in the environment (31.0 °C for Ae. aegypti, 29.1 °C for Ae. albopictus) than lab predictions of the thermal optimum for transmission (29.1 °C for Ae. aegypti, 26.4 °C for Ae. albopictus). Using presence-only species distribution modeling approaches, we found that human population density was the best predictor for each vector’s spatial distribution (explaining 68.4% of model performance for Ae. aegypti, 48.7% for Ae. albopictus). These patterns were consistent in the field for presence/absence Ae. aegypti data (0.71 AUC, 0.80 recall), but failed to predict Ae. albopictus distributions in the sites we surveyed (0.53 AUC, 0.20 recall). In this session, we will explore these results and discuss the potential to predict and monitor Aedes habitat using satellite data.

The Kepler mission revealed that sub-Neptunes are about as common as stars, which defied our pre-existing notion of planet demographics. The prevailing view for sub-Neptunes was that they are mostly core by mass and atmosphere by volume (Lopez & Fortney, 2014). However, current formation models do not consider dissolution at the atmosphere-core interface. The temperature and pressure at the magma-atmosphere interface can rise to >3000 K and ~5-30 GPa (Lee et al., 2014; Piso et al., 2015), high enough for dissolution of hydrogen gas into the magma (Chachan & Stevenson, 2018). The dissolution of atmosphere into the magma may explain the drop-off in exoplanet abundance at 3 times Earth radius (Kite et al., 2019), but the puff-up of the magma due to gas dissolution has not previously been included. We propose a simple model to calculate sub-Neptune mass-radius relation, including, for the first time, the puff-up effect. Key assumptions include: (1) nonlinear solubility of gas in magma is constrained by limited laboratory data (Hirschmann et al, 2012); (2) the Fe/core mass fraction is Earth-like, and He/gas mass fraction is Solar-like; (3) ideal mixing between the dissolved gas and magma; (4) the dissolved gas is well mixed within the magma-layer. The EoS used are an Mg2SiO4 for the magma (Stewart et al., 2020); the H/He EoS (Chabrier et al., 2019); and a simple model for Fe (Seager et al., 2007). The model is integrated from the radiative-convective boundary and iterated until atmosphere-magma solubility equilibrium. We have varied the core mass, atmospheric mass and equilibrium temperature in the atmosphere. Our preliminary results are shown in Figures. The critical point for the puff-up of the core due to the dissolved gas corresponds to ~1% solubility at the magma-atmosphere boundary (Fig. 1). The puff-up effect can be important up to 0.3 Earth radius (Fig. 2), much larger than the radius error bars for a single planet in the CKS survey with Gaia DR2 data (Fulton & Petigura, 2018). In future, we will add additional constraints on gas/core mass fraction (Lee, 2019), forward-model the relationship between mass and photospheric radius, and generate predictions for exoplanet masses and radii that can be used to help interpret data from ESA’s PLATO and NASA’s TESS.

Magnetic reconnection is responsible for the major reconfigurations of the magnetosphere that lead to energy transport and deposition into the ionosphere. The fast rate at which magnetic energy is converted to plasma kinetic energy is likely enabled by the polarization Hall electric field that results from the separation of ions and electrons at small scales. Signatures of Hall fields have played a key role in identifying and studying reconnection, but the density of accumulated charge has not been quantified. We use the 4-point measurements of the Magnetospheric Multiscale mission to compute the divergence of the electric field and present the first observations of charge density in the diffusion region of magnetic reconnection. We show how it ties into the Hall system, discuss measurement uncertainties, analyze quality estimates, and make comparisons to 2D simulations. Charge density is briefly presented for other phenomena, and ranges from 2% or less of the background plasma density for magnetic reconnection and electron-scale magnetic holes and peaks to upwards of 4% for electron phase space holes.

Magmatic structures are well-preserved in a 201.5 Ma diabase sill (PA, USA, equivalent to the Palisades sill) formed as part of the Central Atlantic Magmatic Province during rifting of Pangea. The sill was emplaced at ~6 km depth and tilted ~20° NNW by post-magmatic fault movement. Detailed mush structures are exposed in a dimension stone quarry with walls cut parallel and perpendicular to the strike of the sill. Light gray, plagioclase-rich layers (PLR) a few mm thick contain up to 75% modal plag and are underlain by more pyroxene-rich layers with larger orthopyx antecrysts up to 1 mm length. PLR are sub-parallel to sill margins, have dm-m lateral dimensions, and spaced 0.33 m apart on average. Magma replenishments < 1m thick cross-cut plag-pyx layers at low angles and have basal load-cast-like structures. Since mafic replenishments have PLR at their tops and similar thickness to PLR spacing, we interpret all PLR as having formed by emplacement of small-volume magma pulses bearing ~30% larger pyx and smaller plag antecrysts. This model is similar to Petford and Mirhadizadeh (R Soc Open Sci, 2017) for the Basement sill, Antarctica. Upward migration of mafic melts in pipe-like channels (cm to dm wide) disrupted plag-pyx layers to form dm-scale graben-like and slump-like structures that resemble sediment liquefaction. Channelized flow late in sill development may have been enhanced by seismicity (Davis et al., JVGR, 2007). Diabase micro-structures are similar to published experimental results and numerical simulations of flow and shear-thinning in particle-rich slurries (e.g., Cimarelli et al., G3, 2011; Ishibashi, JVGR, 2009; Deubelbeiss et al., G3, 2011). These include layers such as the plag-pyx couplets and orientations of euhedral plag around pyx phenocrysts. Plag long-axis orientations and tiling indicators in the PLR have strike-parallel and strike-normal components in vertical and plan views consistent with flow alignment in the plane perpendicular to the stress gradient. Plag chemical zoning patterns, limited deformation, and long-axis orientations parallel to inclined layer margins also indicate magmatic flow rather than compaction. Mineral x-ray maps are used to derive initial crystal fraction and aspect ratios for modeling relative viscosity and explore compositional aspects of layer development.

This project introduces Cognitive-Based Emulation (CBE), a new theoretical field that reframes trauma, cognition, and identity through symbolic projection and recursive epistemic feedback. From this field we created a cosmological framework, unique from other models. From this epistemic methodology we create an interdisciplinary baseline equation for the theory of everything based on first principles.  \cite{Newell_2026}   This model is then analyzed through a rigorous connection to Mikhail Medvedev’s (2023) QED-plasma strong field equation for theoretical validation. We then test this efficient, self-consistent, to obtain results parallel to recent findings from data, cosmological simulations, and similar work inspired through Mikhail’s extensive life production. We have explored several applications of the equation, including relativistic Weibel shocks, kinetic instabilities, dark matter dynamics, and potentially QED self-generated fields near bow shock regions. While traditionally studied individually we highlight the physical interwoven complexity of turbulence, magnetic reconnection, and shocks, phenomena traditionally studied in isolation, through a single analytical framework. This enables a consistent interpretation of energy transfer and particle energization without relying on subgrid models or artificial dissipation. In conclusion, we effectively bridge theory, simulation, and observation to enable fast, high-resolution analytical modeling. Therefore, this model naturally provides multiscale temporal and spatial resolution internally, preserving global regularity. Overall, this equation reduces to classical Relativity, magnetohydrodynamic, and quantum electrodynamics, suggesting that these traditional frameworks emerge as special cases of the framework.

Multispectral imaging with unmanned aircraft systems (UAS) is a promising high-throughput phenotyping technology that has been shown to help understand the causal mechanisms associated with crop productivity. This imaging technology can accurately predict complex agronomic traits like grain yield within a given generation, creating the potential to fast-track selections in plant breeding and increase genetic gains. The objective of this study was to determine the effectiveness and efficiency of prediction on grain yield in an abnormal drought year across locations within a breeding program. Eleven spectral reflectance indices (SRI) including NDRE, NWI, NDVI, and percent canopy cover were used to evaluate Washington State University winter wheat breeding lines between 2018 and 2021. Data was collected using a DJI Inspire 2 drone, equipped with a Sentera Quad Multispectral Sensor, and collected at the heading date. Lines were observed from single location, single replication preliminary yield trials to multi-location, replicated advanced yield trials. Lines advanced in the breeding program were evaluated across 13 different location-year trials. The calculated SRIs and canopy cover were used individually and in combination as fixed effects in mixed model prediction for grain yield under drought conditions. Models were independently validated with 2021 data. Across locations, SRIs are shown to improve the prediction performance for grain yield under abnormal drought conditions by as much as 40% in the case of NDRE. This research is vital for plant breeders to understand the utility of UAS imaging in variety improvement when dealing with abnormal growing seasons.

We review the mechanisms that shape the spatial distributions of energetic electrons trapped in the magnetospheres of Jupiter, Saturn, Uranus and Neptune. To determine what controls the energy and spatial distributions throughout the different magnetospheres, we compute the time evolution of particle distributions with the help of a diffusion theory particle transport code that solves the governing 3-D Fokker-Planck equation. We discuss the processes already accounted for in our physics-based models of the outer planet electron radiation belts but also those suspected to be missing to improve our model results. Our theoretical modeling is guided by the analysis of particle, field and wave data collected by Pioneer 10&11 and Galileo at Jupiter, Cassini at Saturn, and during Voyager 2’s flyby of Uranus in January 1986 and at Neptune in August 1989.

Groundwater depletion in Central Wisconsin, due in part to agricultural high-capacity wells, has sparked an interest in precision irrigation to reduce groundwater pumping without a significant reduction in yield. A key challenge for bridging precision irrigation research and application is how best to monitor water stress in real-time. Aerial and satellite imagery are potential solutions. Drawbacks of these methods include cost, spatiotemporal resolution, and cloud interference, especially in humid regions. Recent advancements in remotely piloted aircrafts (RPAs) have made frequent, low-flying imagery collection more economical and feasible than ever before. We partnered with the Wisconsin Potato and Vegetable Grower Association to generate high-resolution maps of crop water stress using remotely sensed thermal and multi-spectral RPA imagery. Data were collected at a commercially irrigated potato field in the Central Sands region of Wisconsin from June to August 2019. Missions were flown weekly using a quadcopter RPA system instrumented with a newly released, combined multispectral/thermal camera developed for agricultural applications. Each mission included flights at 30, 60, and 90 m above ground level to assess tradeoffs between resolution, area, and flight time. We used biophysical data from an eddy covariance system installed within the flight domain to validate crop water stress maps generated from the remotely sensed RPA data. Ground measurements of surface temperature and soil moisture were collected throughout the domain within fifteen minutes of each mission. Ongoing results will be used to develop best practices for integrating RPAs into precision irrigation programs.

The IRIS Data Management Center (DMC) maintains a large assemblage of pre-computed and dynamically generated quality assurance metrics for seismic data by means of its MUSTANG system. Freely accessible through web services at http://service.iris.edu/mustang, this collection of measurements includes basic statistics, data latency, data availability, miniSEED flag counts, Power Spectral Densities (PSD), Probability Density Functions (PDF), and more. The metrics produced are suitable for a wide range of uses such as data selection for research projects, identification of data and metadata problems, assessment of station health and upgrades, and characterization of environmental noise, among others. Currently, MUSTANG measurements for seismic channels span our entire primary data repository from the year 1972 to the present. We are expanding MUSTANG’s utility to the broader geosciences community by calculating measurements on our active source, PH5 data repository and by working towards including other types of data such as infrasound. We will also present improvements to metric visualization and quality assessment tools like the MUSTANG Databrowser (basic metric plots and boxplots), MUSTANGular (map-based metric plots), and ISPAQ (a stand-alone Python utility for calculating metrics for miniSEED data stored locally or at any FDSN data center).

The Amazon River discharges more than 200,000 m3 s-1 into the Western Tropical Atlantic Ocean from May to June. The low salinity surface plume extends more than 1800 km from the mouth and covers an area greater than 1 million square kilometers. We hypothesize that the plume exhibits distinct microbial community assemblages driven by plume age, nutrient supply, and light availability. We collected samples for nutrients and flow-cytometry measurements to investigate the spatial variability of the cyanobacteria Prochlorococcus spp. and Synechococcus spp., picoeukaryotes, and heterotrophic bacteria. Overall the surface salinity of the water we sampled ranged from 15.5 ppt at the southernmost station to 36.3 ppt in the open ocean station. The surface nitrate and soluble reactive phosphorus concentrations ranged from below detection limit to 3.3 µM and 2 µM, respectively. Generally, in the freshest surface plume waters (15-28 ppt) we found the highest abundances of Synechococcus spp., picoeukaryotes, and hetrotrophic bacteria with little or no Prochlorococcus spp. In the transition of surface salinities from 28 ppt to 32 ppt, a population of Prochlorococcus spp. began to form below the surface plume while Synechococcus spp. abundances at the surface remained unchanged and picoeukaryotes, and heterotrophic bacteria abundances decreased. As the surface salinity climbed over 32 ppt, the Prochlorococcus spp. abundance was uniformly high throughout the euphotic zone. On the other hand, as surface salinities increased over 32 ppt Synechococcus spp. abundances at the surface gradually decreased, while picoeukaryote and hetrotrophic bacterial abundances remained constant. We will discuss changes in the microbial community composition as a function of nutrient and light availability, as well as plume age in the Amazon Plume-Ocean continuum in both surface and deep chlorophyll maximum assemblages.

The Ross Ice Shelf (RIS) has long obscured the structure of the extended crust of the southern Ross Embayment. Here, we use airborne magnetics data from the ROSETTA-Ice project to estimate the depth to crystalline basement and sediment thickness beneath the RIS. We estimate the depth to the top of the magnetic crust, using a Werner deconvolution in a 2-D moving window applied to aeromagnetic data at 10 km line spacing. The result is then filtered, clustered, and gridded solutions to achieve a continuous basement surface. This method was tuned with seismic acoustic basement in the Ross Sea and magnetic basement determined from Operation Ice Bridge flight line data over both the Ross Sea and the RIS. Sub-RIS sediment thickness was then calculated relative to bathymetry. Shallow basement and thin sediments beneath the RIS define two major basement highs. A throughgoing, wide basement high is located midway across the RIS, and appears to form a southward continuation of the Ross Sea’s Central High. The other prominent basement feature underlies Roosevelt Island, with a continuation SE to the grounding zone. An elongate basin between the two highs deepens southward, reaching its greatest depth beneath Siple Dome. A deep oval basin flanks Crary Ice Rise. Both basins contain >2 km sediment. Bordering the Transantarctic Mountains (TAM) there is a deeper and broader basement basin that contains a narrow, linear, NW trending basement ridge. The magnetic basement is shallower toward Marie Byrd Land and deeper near the TAM/East Antarctica, consistent with contrasts in magnetic and gravity signatures of the crust on either side of the mid-RIS high. The trend of basement highs and basins parallel to the central TAM front suggests that the basement relief ≥2 km is structurally controlled, and is a product of regional extension. Basin sediments may be a source of deformable subglacial bed conditions for grounded ice, and basin flanking faults may control geothermal flux and subglacial water transport. These considerations have a bearing on sub-RIS boundary conditions that are important for cryosphere-ocean numerical modelling frameworks. Our work extends the current knowledge of sub-RIS sediment distribution, continental rifting orientations and the likely locations of basin flanking faults.

Numerical Weather Prediction models (NWP) have been used extensively since the ’40-’50s. Despite the advances in the field, the representation and forecast of the magnitude and variability of tropical processes in models is still a challenge. One of the steps to improve the precipitation forecasts using limited-area models is to evaluate which set of physical schemes and model domain configurations represent in a better way the actual behavior observed in the tropics. We implemented, as a part of a regional risk management strategy, two different operational weather forecast strategies for a complex terrain region in the Andes mountain range in northern South America. Both strategies, together, generate a total of eleven different forecasts every day, using the Weather Research and Forecasting model (WRF) with initial and boundary conditions from the Global Forecast System (GFS). The first configuration, implemented over five years ago and referred to as SYNAPSIS, includes three nested domains (18, 6 and 2 km) and is carried out every day using the 12 UTC GFS run and three different microphysics parametrizations: Eta Ferrier scheme, Purdue Lin Scheme and Thompson Scheme. The forecast lead-time of the latter strategy is 120 hours, and it does not use data assimilation. Since December of 2017, we implemented a second configuration termed RDFS, with two nested domains (12 and 2.4 Km), which carried out four times a day using the 00, 06, 12 and 18 UTC GFS runs. This configuration has a 30-hours lead time with the Thompson microphysics scheme. In RDFS, two WRF forecast runs are performed for each start hour, one assimilating weather radar reflectivity and the other without assimilation as control run, for a total of eight forecast runs daily. In this study, we assess the rainfall and temperature forecasts for all the different configurations using precipitation derived from reflectivity from weather radar, and air temperature at 2m from a network of automatic weather stations. We use 6 hourly and monthly skill scores (RMSE, BIAS, and Correlation coefficient) to quantify the precipitation differences between the SYNAPSIS and the RDFS configurations. To evaluate the impact of data assimilation in the precipitation forecast, we aggregate the results in a region within the inner domain, and then we calculate the average precipitation forecast between 0 and 36 predicted hours for RDFS with and without data assimilation. The results suggest a strong relationship between the forecast start time and the improve of precipitation forecast accuracy using data assimilation. The diurnal cycle of precipitation in the study region has a minimum in the morning (12 UTC) and a maximum in the afternoon (00 UTC) and during the night (09 UTC). The correspondence between the forecast improvement using data assimilation and the diurnal cycle of precipitation is likely due to the amount of assimilated data. In order to quantify the precipitation differences between the diffe

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

We have developed a new experimental system to study the pyrolysis of the refractory organic constituents in cosmic dust. Pyrolysis is observed by mass spectrometric detection of CO2 and SO2, and starts from around 850 K. The time-resolved kinetic behaviour is consistent with two organic components – one significantly more refractory than the other, which probably correspond to the insoluble and soluble organic fractions, respectively. The laboratory results are then incorporated into the Leeds Chemical Ablation Model (CABMOD), which is used to predict the conditions under which organic pyrolysis should be detectable using a high performance/large aperture radar. It has been proposed that loss of the organics leads to fragmentation of cometary dust particles into micron-sized fragments. If fragmentation of dust particles from Jupiter Family and Halley Type Comets does occur to a significant extent, there are several important implications: 1) slow-moving particles, particularly from Jupiter Family Comets, will be undetectable by radar, so that the total dust input to the atmosphere may be considerably larger than current estimates of 20 – 50 tonnes per day; 2) experiments at Leeds show that meteoritic fragments are excellent ice nuclei for freezing stratospheric droplets in the polar lower stratosphere, producing polar stratospheric clouds which activate chlorine and cause ozone depletion; and 3) the measured accumulation rates of meteoric smoke particles, micrometeorites and cosmic spherules in the polar regions can now be explained self-consistently.