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NASA NTRS2021

Formaldehyde evolution in U.S. wildfire plumes during FIREX-AQ

Formaldehyde (HCHO) is one of the most abundant non-methane volatile organic compounds (VOCs) emitted by fires. HCHO also undergoes chemical production and loss as a fire plume ages, and it can be an important oxidant precursor. In this study, we disentangle the processes controlling HCHO by examining its evolution in wildfire plumes sampled by the NASA DC-8 during the FIREX-AQ field campaign. In nine of the twelve analyzed plumes, dilution-normalized HCHO increases with physical age (range 1 – 6 h). The balance of HCHO loss (mainly via photolysis) and production (via OH-initiated VOC oxidation) seems to control the sign and magnitude of this trend. Plume-average OH concentrations, calculated from VOC decays, range from –0.5 (± 0.5) × 10 6 to 5.3 (±0.7) × 10 6 cm -3 . The production and loss rates of dilution-normalized HCHO seem to decrease with plume age. Plume-to-plume variability in dilution-normalized secondary HCHO production correlates with OH abundance rather than normalized OH reactivity, suggesting that OH is the main driver of fire-to-fire variability in HCHO secondary production. Analysis suggests an effective HCHO yield of 0.33 (± 0.05) per VOC molecule oxidized for the 12 wildfire plumes. This finding can help connect space-based HCHO observations to the oxidizing capacity of the atmosphere and to VOC emissions.

Air quality↗

NASA NTRSDate not supplied

Intense Equatorial Electrojet and Counter Electrojet caused by the 15 January 2022 Tonga Volcanic Eruption: Space and Ground-based Observations

Abstract Text: We present space and ground-based multi-instrument observations demonstrating the impact of the 2022 Tonga volcanic eruption on dayside equatorial electrodynamics. A strong counter electrojet (CEJ) was observed by Swarm and ground-based magnetometers on 15 January after the Tonga eruption and during the recovery phase of a moderate geomagnetic storm. Swarm also observed an enhanced equatorial electrojet (EEJ) preceding the CEJ in the previous orbit. The observed EEJ and CEJ exhibited complex spatiotemporal variations. We combine them with the Ionospheric Connection Explorer (ICON) neutral wind measurements to disentangle the potential mechanisms. Our analysis indicates that the geomagnetic storm had minimal impact; instead, a large-scale atmospheric disturbance propagating eastward from the Tonga eruption site was the most likely driver for the observed intensification and directional reversal of the equatorial electrojet. The CEJ was associated with strong eastward zonal winds in the E-region ionosphere, as a direct response to the lower atmosphere forcing. Plain-Language Summary: The Earth's E-region ionosphere (~100-150 km altitude) consists of both ionized and neutral gasses, and the two components are coupled through ion-neutral collisions. The state of this region is closely influenced by neutral atmospheric activities from the lower atmosphere and the variability of the solar drivers. On 15 January 2022, the Tonga volcano had a massive eruption and injected an enormous amount of mass and energy into the atmosphere causing disturbances in the E-region ionosphere or even higher. There was also a moderate geomagnetic storm that started one day before the eruption and ended days after. These conditions offer a unique opportunity to understand the different roles they play in controlling the ionosphere. Coordinated observations including the atmosphere, ionosphere and magnetosphere were made from both space and on the ground during this event. We analyzed the magnetic field and neutral wind data and found that a large-scale atmospheric disturbance generated by the volcano eruption was responsible for the observed directional reversal of the dayside equatorial electric field and electric current.

Guan Le↗

NASA NTRS2022

The Co2 Profile and Analytical Model for the Pioneer Venus Large Probe Neutral Mass Spectrometer

We present a significantly updated CO2 altitude profile for Venus (64.2–0.9 km) and provide support for a potential deep lower atmospheric haze of particles (≤17 km). We extracted this information by developing a new analytical model for mass spectra obtained by the Pioneer Venus Large Probe (PVLP) Neutral Mass Spectrometer (LNMS). Our model accounts for changes in LNMS configuration and output during descent and enables the disentanglement of isobaric species via a data fitting routine that adjusts for mass-dependent changes in peak shape. The model yields CO2 in units of density (kg m−3), isotope ratios for 13C/12C and 18O/16O, and 14 measures of CO2 density across 55.4–0.9 km, which represents the most complete altitude profile for CO2 at ≤60 km to date. The CO2 density profile is also consistent with the pressure, temperature, and volumetric gas measurements from the PVLP and VeNeRa spacecraft. Nominal and low-noise operations for the LNMS mass analyzer are supported by the behaviors (e.g., ionization yields, fragmentation yields, and peak shapes) of several internal standards (e.g., CH3+, CH4+, 40Ar+, 136Xe2+, and 136Xe+), which were tracked across the descent. Lastly, our review of the CO2 profile and LNMS spectra reveals hitherto unreported partial and rapidly clearing clogs of the inlet in the lower atmosphere, along with several ensuing data spikes at multiple masses. Together, these observations suggest that atmospheric intake was impacted by particles at ≤17 km and that rapid particle degradation at the inlet yielded a temporary influx of mass signals into the LNMS.

Venus↗

NASA NTRS2023

Temperature Effect on Silicate Melt-Sulfide-Metal Trace Element Partitioning in the Presence of Sulfur Under Reduced Conditions

The reduced nature of Mercury, enstatite chondrites, and the aubrite parent bodies (APB) have raised many questions regarding the geochemical behavior of typically lithophile, heat-producing, and rare-earth elements (REE) in magmas at low oxygen fugacity (fO2). Due to decreasing O availability at these low fO2, and an abundance of S(sup 2(-)), sulfur (S) acts as an important anion that changes the partitioning behavior of many elements and modifies the physical properties of silicate melts. Preliminary observations suggest that major and minor elements exhibit different geochemical affinities in highly reduced, S-rich systems compared to terrestrial rocks. The speciation and bonding environment of S, dictated by P/T/fO2 conditions, may strongly influence the degree to which S affects partitioning behavior. Here we investigate the partitioning behavior of major, minor, and trace elements between silicate melt, sulfide melt, and metal as well as the coordination chemistry of S in highly reduced silicate melts. Our work is focused on investigating solely the entropy-dependent temperature effect on partitioning of elements for which we currently have MESSENGER data (K, Na, Th, U, Si, Mg, Fe, Ti, Ca, Al, Cr, Mn, S, Cl) as well as a host of geochemically relevant trace elements such as REEs (P, Co, Ni, Mo, Ce, Nd, Sm, Eu, Gd, Dy, Yb). Previous studies in which temperature, pressure, and fO2 were co-varied found that as fO2 decreases, heat-producing elements U and Th become more chalcophile, while K becomes less chalcophile. Concurrently, nominally lithophile elements Mg and Ca become more chalcophile and were observed as minor elements in exsolved sulfides and bonded with S species in silicate melt. These studies, however, could not disentangle entropic effects from changes in the fO2. New temperature-dependent partitioning data from our work will be used to determine which elements are most likely to retain their lithophile character and hence be incorporated into silicates, and which elements are likely contained within the sulfide (chalcophile) and metal core (siderophile), setting the stage for the thermal and magmatic evolution of reduced planetary bodies.

B A Anzures↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗

NASA NTRSDate not supplied

Silicon Partitioning Between Iron-Rich Metal and Silicate Melt: New Constraints From Aerodynamic Laser Levitation Furnace Experiments

The partitioning of light elements (e.g., Si, C, H, etc.) between silicate melts and iron metal alloys as a function of pressure and temperature is a critical constraint in defining the compositional evolution of planetary interiors. While there are a significant number of experimental studies in the literature that examine the various influences of oxygen fugacity, temperature, pressure, and composition on the metal-melt partitioning of silicon, it is difficult to disentangle the effects of each variable due to the nominally independent parameters (e.g., pressure, temperature, composition) being collinear. To better test for the independent effects of pressure, temperature, and melt composition on the partitioning of silicon between iron alloy and silicate melt, new high temperature (1800°–2500°C), rapid quench (~700°C/s) aerodynamic levitation laser-heating experiments in the Fe-Si-Mg-O (En65-Fa5-Si3-Fe27) system were conducted. This high temperature, low pressure method is ideal for these experiments because it eliminates the metal container as a sink for elements of interest to partition into, and it has a rapid quench rate that minimizes potential exsolution of silicon from the metal phase during quench cooling. Given the short run times of the levitation experiments (30–60 seconds), a time series at a lower temperature (1700°C) in a vertical gas-mixing muffle tube furnace was also conducted to assess equilibrium times of the bulk composition. All successful runs were imaged using back-scatter electron imaging and analyzed for phase composition using a JEOL 8530F field emission microprobe. These new data were added to a comprehensive and internally consistent data set created from published experiments, and subsequently modeled using parameters designed to minimize collinearity (e.g., optical basicity is used as a proxy for melt composition). High collinearity is found in the data set however between temperature and oxygen fugacity, precluding any model that contains both as nominally independent variables. The resulting model demonstrates a statistically significant effect of pressure, temperature, and melt composition on the partitioning of silicon in Fe-rich metal, which has implications for the formation of planetary cores in rocky planets and provides important constraints on potential bulk core compositions.

Metal↗

NASA NTRS2022

Line Emission Mapper Probing Physics of Cosmic Ecosystems

The Line Emission Mapper (LEM) is an X-ray Probe for the 2030s that will answer the outstanding questions of the Universe’s structure formation. It will also provide transformative new observing capabilities for every area of astrophysics, and to heliophysics and planetary physics as well. LEM’s main goal is a comprehensive look at the physics of galaxy formation, including stellar and black-hole feedback and flows of baryonic matter into and out of galaxies. These processes are best studied in X-rays; as emphasized by the 2020 Decadal Survey, emission-line mapping is the pressing need in this area. LEM will use a large microcalorimeter array/IFU (that builds on Athena XIFU technology developments), covering a 30 × 300 field with 1000 angular resolution, to map the soft Xray line emission from objects that constitute galactic ecosystems. These include supernova remnants, star-forming regions, superbubbles, galactic outflows (such as the Fermi/eROSITA bubbles in the Milky Way and their analogs in other galaxies), the Circumgalactic Medium in the Milky Way and other galaxies, and the Intergalactic Medium at the outskirts and beyond the confines of galaxies and clusters. LEM’s 1–2 eV spectral resolution in the 0.2–2 keV band will make it possible to disentangle the faintest emission lines in those objects from the bright Milky Way foreground, providing groundbreaking measurements of the physics of these plasmas, from temperatures, densities, chemical composition to gas dynamics. While the mission is optimized to provide critical observations that will push our understanding of galaxy formation, LEM will provide transformative capability for all classes of astrophysical objects, from the Earth’s magnetosphere, planets and comets to the interstellar medium and X-ray binaries in nearby galaxies, AGN, and cooling gas in galaxy clusters. In addition to pointed observations, LEM will perform a shallow all-sky survey that will dramatically expand the discovery space.

Ralph Kraft↗

NASA NTRSDate not supplied

Understanding Plant Nitrogen Form Preference Responses to Elevated CO2 Earth and Space Environments

Future long-duration missions to the International Space Station (ISS) and beyond will require a sustainable supply of food to support human crews. The spaceflight cabin environment often contains very high concentrations of CO2, and it is therefore crucial to understand plant responses to elevated CO2 (eCO2) environments. Much focus has been given to plant photosynthetic and performance parameters in response to eCO2, but studies on the effects of eCO2 on nitrogen uptake are poorly understood. Our previous work at The University of Sheffield, UK using novel stable isotope approaches has shown enhanced ammonium uptake and preference compared to nitrate at eCO2 in varieties of spring barley, hypothesized to be an indirect consequence of changes in photosynthesis and photorespiration. However, several varieties did not display altered preference under eCO2 and the universality of this response remains to be understood. In this NASA NPP project, several candidate crop species such as lettuce, radish and tomato will be screened using stable isotopes to assess whether N preference changes in favor of ammonium in response to eCO2 and whether this remains true at super-elevated CO2 (seCO2), which is often experienced on the ISS. Photosynthetic and related measurements will help to disentangle the relationship between these responses and photosynthesis. This work will enable the development of optimized nutrient regimes for candidate crops in space and in future Lunar and Martian habitats and will pave the way for selection of crop varieties adapted to an eCO2 and/or seCO2 environment. Moreover, this research will further our understanding of plant responses to the eCO2 environment brought about by climate change, allowing the development of future-proof crops that will help to maintain food security. This NPP Fellowship is funded by NASA Space Biology.

Luke Leslie Fountain↗

NASA NTRS2022

Deciphering the Birth Region, Formation, and Evolution of Ambient and Transient Solar Wind Using Heavy Ion Observations

This paper outlines key scientific topics that are important for the development of solar system physics and how observations of heavy ion composition can address them. The key objectives include, 1) understanding the Sun’s chemical composition by identifying specific mechanisms driving elemental variation in the corona. 2) Disentangling the solar wind birthplace and drivers of release by determining the relative contributions of active regions (ARs), quiet Sun, and coronal hole plasma to the solar wind. 3) Determining the principal mechanisms driving solar wind evolution from the Sun by identifying the importance and interplay of reconnection, waves, and/or turbulence in driving the extended acceleration and heating of solar wind and transient plasma. The paper recommends complementary heavy ion measurements that can be traced from the Sun to the heliosphere to properly connect and study these regions to address these topics. The careful determination of heavy ion and elemental composition of several particle populations, matched at the Sun and in the heliosphere, will permit for a comprehensive examination of fractionation processes, wave-particle interactions, coronal heating, and solar wind release and energization that are key to understanding how the Sun forms and influences the heliosphere.

chemical composition↗

NASA NTRSDate not supplied

Understanding Plant Nitrogen Form Preference Responses to Elevated CO2 Earth and Spaceflight Cabin Environments

Future long-duration missions to the International Space Station (ISS) and beyond will require a sustainable supply of food to support human crews. The spaceflight cabin environment often contains very high concentrations of CO2, and it is therefore crucial to understand plant responses to elevated CO2 (eCO2) environments. Much focus has been given to plant photosynthetic and performance parameters in response to eCO2, but studies on the effects of eCO2 on nitrogen uptake are poorly understood. Our previous work at The University of Sheffield, UK using novel stable isotope approaches has shown enhanced ammonium uptake and preference compared to nitrate at eCO2 in varieties of spring barley, hypothesised to be an indirect consequence of changes in photosynthesis and photorespiration. However, several varieties did not display altered preference under eCO2 and the universality of this response remains to be understood. In this NASA NPP project, several candidate crop species such as lettuce, radish and tomato will be screened using stable isotopes to assess whether N preference changes in favor of ammonium in response to eCO2 and whether this remains true at super-elevated CO2 (seCO2), which is often experienced on the ISS. Photosynthetic and related measurements will help to disentangle the relationship between these responses and photosynthesis. This work will enable the development of optimized nutrient regimes for candidate crops in space and in future Lunar and Martian habitats and will pave the way for selection of crop varieties adapted to an eCO2 and/or seCO2 environment. Moreover, this research will further our understanding of plant responses to the eCO2 environment brought about by climate change, allowing the development of future-proof crops that will help to maintain food security. This NPP Fellowship is funded by NASA Space Biology.

Luke Fountain↗

NASA NTRSDate not supplied

Understanding Plant Nitrogen Form Preference Responses to Elevated CO2 Earth and Spaceflight Cabin Environments.

Future long-duration missions to the International Space Station (ISS) and beyond will require a sustainable supply of food to support human crews. The spaceflight cabin environment often contains very high concentrations of CO2, and it is therefore crucial to understand plant responses to elevated CO2 (eCO2) environments. Much focus has been given to plant photosynthetic and performance parameters in response to eCO2, but studies on the effects of eCO2 on nitrogen uptake are poorly understood. Our previous work at The University of Sheffield, UK using novel stable isotope approaches has shown enhanced ammonium uptake and preference compared to nitrate at eCO2 in varieties of spring barley, hypothesised to be an indirect consequence of changes in photosynthesis and photorespiration. However, several varieties did not display altered preference under eCO2 and the universality of this response remains to be understood. In this NASA NPP project, several candidate crop species such as lettuce, radish and tomato will be screened using stable isotopes to assess whether N preference changes in favor of ammonium in response to eCO2 and whether this remains true at super-elevated CO2 (seCO2), which is often experienced on the ISS. Photosynthetic and related measurements will help to disentangle the relationship between these responses and photosynthesis. This work will enable the development of optimized nutrient regimes for candidate crops in space and in future Lunar and Martian habitats and will pave the way for selection of crop varieties adapted to an eCO2 and/or seCO2 environment. Moreover, this research will further our understanding of plant responses to the eCO2 environment brought about by climate change, allowing the development of future-proof crops that will help to maintain food security. This NPP Fellowship is funded by NASA Space Biology.

space plant biology↗

NASA NTRS2021

Tunable Pseudocapacitive Intercalation of Chloroaluminate Anions into Graphite Electrodes for Rechargeable Aluminum Batteries

Rechargeable aluminum-graphite batteries using chloroaluminate-containing electrolytes have been the focus of significant research, particularly due to their high-rate capabilities. Engineered graphite electrodes have been shown to exhibit supercapacitor-like rate performance, despite the fact they store charge via the electrochemical intercalation of polyatomic AlCl4− anions. However, the origins of such rate capabilities are not well understood. Here, using electrochemical techniques, we disentangle quantitatively the diffusion-limited Faradaic, pseudocapacitive, and capacitive contributions to charge storage, revealing that AlCl4− anions intercalate into graphite with significant pseudocapacitive characteristics due to low ion diffusion limitations. Pristine and mildly exfoliated graphites are compared, where exfoliation resulted in significantly higher pseudocapacitive AlCl4− intercalation at the highest potential redox pair as well as higher galvanostatic capacity retention at faster discharge rates. The relationships between graphite structure, ion mass transport, and the overall rate of electrochemical AlCl4− intercalation are discussed. Ion diffusion within the electrolyte phase of the porous electrode is shown to play a key role in controlling the rate of intercalation at higher potentials and faster rates, which can be enhanced by reducing electrode tortuosity. The results establish that chloroaluminate anion intercalation into graphite exhibits non-diffusion-limited pseudocapacitive contributions that are tunable by modifying the graphite structure.

Jeffrey H. Xu↗

NASA NTRSDate not supplied

Interpretable Machine Learning Models for Autonomous Characterization of Analogue Ocean World Seawater Chemistry and Biosignature Potential Using Isotope Ratio Data

Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.

geochemistry↗

NASA NTRSDate not supplied

Boundary-Layer Cloud Modeling Challenges on the North Slope of Alaska

The accurate modeling and prediction of cloud base heights is critical for energy balance calculations and aviation operations, alike. Low-level (i.e., boundary-layer) Arctic clouds can be difficult to model, making prediction of formation and dissipation challenging. Primarily mixed-phase, these clouds typically contain low quantities of supercooled liquid water and often slowly precipitate relatively small amounts of moderately and heavily rimed snow particles. While this appears to be the predominant cloudy state on the North Slope of Alaska (NSA), the delicate balance of microphysical, dynamical, radiative, surface coupling, and advective processes can rapidly shift to heavy snow (with various degrees of riming) or to a complete dissipation of the cloud layer without any precipitation, depending on the dominant processes. Here we strive to disentangle these various processes. First, we compare the predictive performances of four different numerical weather models in forecasting the presence and base-heights of low-level clouds: the High-Resolution Rapid Refresh - Alaska (HRRR-AK) model, the Polar Weather Research and Forecasting (Polar WRF) model, the Unified Model (UM), and the European Centre for Medium-range Weather Forecasting (ECMWF) model. Initial results comparing model output at two U.S. Department of Energy Atmospheric Radiation Measurement (AMT) NSA sites, during the fall season in 2019 and 2022, show that the UM slightly outperforms the HRRR-AK in terms of accurately forecasting the presence of a low-level cloud layer (89% of the time). All models have a significant bias of 300 to 800 meters in forecasting cloud base height (lower than is observed); however, the UM and ECMWF models have the lowest biases. Finally, a case study for a particularly challenging April 2017 thin-cloud event is presented, wherein we compare the performance of four different bulk microphysical parameterization schemes using a higher-resolution large eddy simulation (LES) model, the WRF-LES. Initial results show that the Thompson scheme was the only one able to reproduce and sustain a substantial supercooled liquid layer, but it was unable to reproduce the transition from a deep, liquid-rich cloud to a thin layer with moderately and heavily rimed precipitation. This is the first step in linking simulated LES-scale riming processes with those parameterized at a coarser mesoscale model scale. This has important implications for forecasting low-level clouds in an operational environment, given the efficiency of the riming process.

cloud base heights↗

NASA NTRSDate not supplied

Parameterization of Vertical Cloud Distribution from C3M and MERRA Data Using ML Method

Clouds play a key role in regulating the hydrological cycle and the Earth's radiative energy budget. However, global climate models (GCMs) with a horizontal grid spacing on the order of 100 km have limitations in representing sub-grid cloud dynamics with spatial scales on the order of 1 km, leading to potential uncertainties in cloud radiative feedback on the global scale. In our research, we will leverage the capabilities of Deep Machine Learning (DML) methods to construct parameterizations of sub-grid volumetric cloud fraction (VCF), which is the frequency of occurrence on a grid volume accumulated in the horizontal and vertical directions. Our investigation delves into the intricate relationship between VCF obtained from the NASA CALIPSO-CloudSat-CERES-MODIS (CCCM) satellite observation data and 3-D MERRA-2 reanalysis meteorological profiling data (e.g., wind, relative humidity, temperature). Through a comprehensive one-year data training utilizing the Sequence to Sequence DML method, we have successfully disentangled the complicated cloud formation dynamics across diverse meteorological conditions through a day-to-day analysis framework. Preliminary findings reveal promising statistical agreements in geographical and vertical distributions and seasonal variations of volumetric cloud fraction between ML prediction and satellite measurements. These results underscore the aptitude of our DML model to discern underlying cloud physical processes and accurately represent sub-grid cloud formation dynamics. Additionally, we have also employed trained neural network to analyze uncertainties arising from errors in meteorological data, further enhancing the robustness of our VCF parameterization.

Shan Zeng↗

NASA NTRS2024

Global Sensitivity Analysis of Simulated Remote Sensing Polarimetric Observations Over Snow

This study presents a detailed theoretical assessment of the information content of passive polarimetric observations over snow scenes, using a global sensitivity analysis (GSA) method. Conventional sensitivity studies focus on varying a single parameter while keeping all other parameters fixed. In contrast, the GSA correctly addresses the covariance of state parameters across their entire parameter space, hence favoring a more correct interpretation of inversion algorithms and the optimal design of their state vectors. The forward simulations exploit a vector radiative transfer model to obtain the Stokes vector emerging at the top of the atmosphere for different solar zenith angles, when the bottom boundary consists of a vertically resolved snowpack of non-spherical grains. The presence of light-absorbing particulates (LAPs), either embedded in the snow or aloft in the atmosphere above in the form of aerosols, is also considered. The results are presented for a set of wavelengths spanning the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) region of the spectrum. The GSA correctly captures the expected, high sensitivity of the reflectance to LAPs in the VIS–NIR and to grain size at different depths in the snowpack in the NIR–SWIR. With adequate viewing geometries, mono-angle measurements of total reflectance in the VIS–SWIR (akin to those of the Moderate Resolution Imaging Spectroradiometer, MODIS) resolve grain size in the top layer of the snowpack sufficiently well. The addition of multi-angle polarimetric observations in the VIS–NIR provides information on grain shape and microscale roughness. The simultaneous sensitivity in the VIS–NIR to both aerosols and snow-embedded impurities can be disentangled by extending the spectral range to the SWIR, which contains information on aerosol optical depth while remaining essentially unaffected when the same particulates are mixed with the snow. Multi-angle polarimetric observations can therefore (i) effectively partition LAPs between the atmosphere and the surface, which represents a notorious challenge for snow remote sensing based on measurements of total reflectance only and (ii) lead to better estimates of grain shape and roughness and, in turn, the asymmetry parameter, which is critical for the determination of albedo. The retrieval uncertainties are minimized when the degree of linear polarization is used in place of the polarized reflectance. The Sobol indices, which are the main metric for the GSA, were used to select the state parameters in retrievals performed on data simulated for multiple instrument configurations. Improvements in retrieval quality with the addition of measurements of polarization, multi-angle views, and different spectral channels reflect the information content, identified by the Sobol indices, relative to each configuration. The results encourage the development of new remote sensing algorithms that fully leverage multi-angle and polarimetric capabilities of modern remote sensors. They can also aid flight planning activities, since the optimal exploitation of the information content of multi-angle measurements depends on the viewing geometry. The better characterization of surface and atmospheric parameters in snow-covered regions advances research opportunities for scientists of the cryosphere and ultimately benefits albedo estimates in climate models.

remote sensing↗

NASA NTRS2026

Tracing the Source of Carbon Oxides on the Large Moons of Uranus

The Uranian moons Ariel, Umbriel, Titania, and Oberon are enriched in CO 2 mixed with CO, but the origin(s) of these carbon oxides, be they primarily native or radiolytic, remain(s) uncertain. Using data collected by NIRSpec on the James Webb Space Telescope (JWST), we measured the spectral signature of CO 2 and other carbon oxides to help disentangle these hypotheses. Through comparison to laboratory data, we find that many of the detected spectral features are consistent with CO 2 ice, including 12 CO2 scattering peaks (4.15–4.26 μm), multilobe 13 CO 2 bands (4.35–4.43 μm), and CO 2 biphonon and triphonon modes (4.80–5.25 μm). Our measurements show that CO 2 and CO are concentrated on the trailing hemispheres of the inner moons Ariel and Umbriel, potentially supporting a radiolytic production hypothesis, consistent with prior ground-based results. However, many of the identified spectral features are only observed in thick crystalline ice deposits measured in the laboratory, which may be difficult to form via radiolysis of carbon-bearing material mixed in icy regoliths. Similarly, the data exhibit weak 4.02 and 4.40 μm bands, hinting at the presence of carbonate minerals and 13 CO 2 clathrates, respectively, possibly formed in the interiors of these moons. Furthermore, JWST has revealed that CO 2 is widespread at Uranus, present in its system of rings, ring moons, and irregular satellites, consistent with its largest moons accreting CO 2 and other carbon oxides from the Uranian subnebula. We conclude that exposed carbon oxides are potentially native, with their surface distributions shaped by charged particle irradiation and seasonal sublimation–condensation cycles.

Ice spectroscopy↗