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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2006

The EPIC-MOS Particle-Induced Background Spectrum

We have developed a method for constructing a spectrum of the particle-induced instrumental background of the XMM-Newton EPIC MOS detectors that can be used for observations of the diffuse background and extended sources that fill a significant fraction of the instrument field of view. The strength and spectrum of the particle-induced background, that is, the background due to the interaction of particles with the detector and the detector surroundings, is temporally variable as well as spatially variable over individual chips. Our method uses a combination of the filter-wheel-closed data and a database of unexposed-region data to construct a spectrum of the "quiescent" background. We show that, using this method of background subtraction, the differences between independent observations of the same region of "blank sky" are consistent with the statistical uncertainties except when there is clear evidence of solar wind charge exchange emission. We use the blank sky observations to show that contamination by SWCX emission is a strong function of the solar wind proton flux, and that observations through the flanks of the magnetosheath appear to be contaminated only at much higher solar wind fluxes. We have also developed a spectral model of the residual soft proton flares, which allows their effects to be removed to a substantial degree during spectral fitting.

Kuntz, K. D.↗

NASA NTRS2016

Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD Retrievals Against Ground Sunphotometer Observations Over East Asia

Persistent high aerosol loadings together with extremely high population densities have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground-based air quality monitoring is relatively limited in this area. Recently, satellite-retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD observations from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD observations from handheld sunphotometers, 51% of VIIRS Environmental Data Record (EDR) AOD, 37% of GOCI AOD, 33% of VIIRS Intermediate Product (IP) AOD, 26% of Terra MODIS C6 3km AOD, and 16% of Aqua MODIS C6 3km AOD fell within the reference expected error (EE) envelope (+/-0.05/+/- 0.15 AOD). Comparing against AERONET AOD over the JapanSouth Korea region, 64% of EDR, 37% of IP, 61% of GOCI, 39% of Terra MODIS, and 56% of Aqua MODIS C6 3km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3km products had positive biases.

Xiao, Q.↗

DOE OSTI2022

Characterizing soil water content variability across spatial scales from optimized high-resolution distributed temperature sensing technique

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54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

Quantifying Spatial and Seasonal Variability in Atmospheric Ammonia with In Situ and Space-Based Observations

Ammonia plays an important role in many biogeochemical processes, yet atmospheric mixing ratios are not well known. Recently, methods have been developed for retrieving NH3 from space-based observations, but they have not been compared to in situ measurements. We have conducted a field campaign combining co-located surface measurements and satellite special observations from the Tropospheric Emission Spectrometer (TES). Our study includes 25 surface monitoring sites spanning 350 km across eastern North Carolina, a region with large seasonal and spatial variability in NH3. From the TES spectra, we retrieve a NH3 representative volume mixing ratio (RVMR), and we restrict our analysis to times when the region of the atmosphere observed by TES is representative of the surface measurement. We find that the TES NH3 RVMR qualitatively captures the seasonal and spatial variability found in eastern North Carolina. Both surface measurements and TES NH3 show a strong correspondence with the number of livestock facilities within 10 km of the observation. Furthermore, we find that TES H3 RVMR captures the month-to-month variability present in the surface observations. The high correspondence with in situ measurements and vast spatial coverage make TES NH3 RVMR a valuable tool for understanding regional and global NH3 fluxes.

Pinder, Robert W.↗

DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Divergent urban land trajectories under alternative population projections within the Shared Socioeconomic Pathways

Population change is a main driver behind global environmental change, including urban land expansion. In future scenario modeling, assumptions regarding how populations will change locally, despite identical global constraints of Shared Socioeconomic Pathways (SSPs), can have dramatic effects on subsequent regional urbanization. Using a spatial modeling experiment at high resolution (1 km), this study compared how two alternative US population projections, varying in the spatially explicit nature of demographic patterns and migration, affect urban land dynamics simulated by the Spatially Explicit, Long-term, Empirical City development (SELECT) model for SSP2, SSP3, and SSP5. The population projections included: (1) newer downscaled state-specific population (SP) projections inclusive of updated international and domestic migration estimates, and (2) prevailing downscaled national-level projections (NP) agnostic to localized demographic processes. Our work shows that alternative population inputs, even those under the same SSP, can lead to dramatic and complex differences in urban land outcomes. Under the SP projection, urbanization displays more of an extensification pattern compared to the NP projection. This suggests that recent demographic information supports more extreme urban extensification and land pressures on existing rural areas in the US than previously anticipated. Urban land outcomes to population inputs were spatially variable where areas in close spatial proximity showed divergent patterns, reflective of the spatially complex urbanization processes that can be accommodated in SELECT. Although different population projections and assumptions led to divergent outcomes, urban land development is not a linear product of population change but the result of complex relationships between population, dynamic urbanization processes, stages of urban development maturity, and feedback mechanisms. These findings highlight the importance of accounting for spatial variations in the population projections, but also urbanization process to accurately project long-term urban land patterns.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1977

A computer software system for the generation of global ocean tides including self-gravitation and crustal loading effects

A computer software system is described which computes global numerical solutions of the integro-differential Laplace tidal equations, including dissipation terms and ocean loading and self-gravitation effects, for arbitrary diurnal and semidiurnal tidal constituents. The integration algorithm features a successive approximation scheme for the integro-differential system, with time stepping forward differences in the time variable and central differences in spatial variables. Solutions for M2, S2, N2, K2, K1, O1, P1 tidal constituents neglecting the effects of ocean loading and self-gravitation and a converged M2, solution including ocean loading and self-gravitation effects are presented in the form of cotidal and corange maps.

Estes, R. H.↗

NASA NTRS1977

A computer software system for the generation of global ocean tides including self-gravitation and crustal loading effects

Estes, R. H.↗

NASA NTRSDate not supplied

Influence of sediment resuspension on deep-ocean Pa-231 and Th-230 cycling: A PROGRESS REPORT

The naturally-occurring particle-reactive radionuclides protactinium-231 (231Pa) and thorium-230 (230Th) are used as tracers of a variety of oceanic processes, both at present and in the past. Most notably, the sediment 231Pa/230Th ratio has been used to infer changes in the Atlantic Meridional Overturning Circulation over the last (de)glaciation. However, recent measurements along the U.S. GEOTRACES North Atlantic transect (GA03) revealed two features which are at odds with current understanding about 231Pa and 230Th behaviour in the ocean: (i) a sharp decrease in dissolved 231Pa and 230Th activities with depth below 2000-4000 m and (ii) very large particulate 231Pa and 230Th activities near the bottom, at a number of stations between the New England continental shelf and Bermuda. Concomitant measurements of particulate matter concentration and potential temperature showed that both features are associated with the benthic nepheloid layer (BNL) and the bottom mixed layer (BML) that are present at these stations. Here we develop and apply a simplified model of the exchange of particles, 231Pa, and 230Th between the BNL and the upper sediment, to explore the extent to which the radionuclide anomalies observed near the bottom at a number of GA03 stations can be explained by local sediment resuspension. We find that the model can broadly reproduce the observed anomalies at two stations where samples for radionuclide analyses were collected near the seafloor. Sensitivity tests with the model show that the 231Pa/230Th ratio of particles in the BML and the sediment varies by a factor of 3 as the sediment resuspension rate fluctuates within a range consistent with observational estimates. The modelled variability is comparable to the spatial variability of 231Pa/230Th of suspended particles in the modern North Atlantic and to the variability of Atlantic sediment 231Pa/230Th records across the last (de)glacial period. Two factors are found to contribute to the modelled sensitivity of the sediment 231Pa/230Th to sediment resuspension rate: the vertical turbulent mixing in the BML and the differential scavenging intensity of Pa and Th due to variation in particle concentration. Overall, our study indicates that the exchange of material between the BNL and the upper sediment can affect the particulate 231Pa/230Th ratio in the bottom water and the sediment, which may complicate the use of sediment 231Pa/230Th as a palaeoceanographic tracer.

particle-reactive radionuclides↗

DOE OSTI2023

Importance of incorporating spatial and temporal variability of biomass yield and quality in bioenergy supply chain

Abstract Biofuels made from biomass and waste residues will largely contribute to United States’ 2050 decarbonization goal in the aviation sector. While cellulosic biofuels have the potential fuel performance equivalent to petroleum-based jet fuel, the biofuel industry needs to overcome the supply chain barrier caused by temporal and spatial variability of biomass yield and quality. This study highlights the importance of incorporating spatial and temporal variability during biomass supply chain planning via optimization modeling that incorporates 10 years of drought index data, a primary factor contributing to yield and quality variability. The results imply that the cost of delivering biomass to biorefinery may be significantly underestimated if the multi-year temporal and spatial variation in biomass yield and quality is not captured. For long term sustainable biorefinery operations, the industry should optimize supply chain strategy by studying the variability of yield and quality of biomass in their supply sheds.

09 BIOMASS FUELS↗

NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗

NASA NTRS1978

Spectral vegetation relationships in a tropical rain forest

Jordan's (1969) spectral method of leaf-area index determination from measurements of the transmitted light at the forest floor is evaluated under a wide variety of illumination conditions in the Luquillo Rain Forest of Puerto Rico, using a hand-held radiometer. Previously suggested 'correction factors' for temporal adjustment were found to degrade the data. Quantitative use of the method was found to be restricted to high sun periods. Data collected under uniformly overcast conditions were of greater stability than under clear skies, however, stability was impaired by changes in cloud thickness. Care must be taken to minimize irradiational variability and to adquately sample the spatial variability.

Holben, B. N.↗

NASA NTRS1990

Analysis and interpretation of variabilities in ozone and temperature fields

The temporal and spatial variabilities were studied of short and long term fluctuations in stratospheric ozone and temperature at various pressure levels using several years of ozone, temperature, and solar flux data from Nimbus 4, Nimbus 7, and SME satellites. Some results are as follows: (1) the solar UV flux and various indices of solar activity indicate a strong period at about 5 months; (2) satellite total ozone observations were analyzed using 17 years of data from the Nimbus 4 BUV and the Nimbus 7 SBUV experiments, which show very similar seasonal variations and quasibiennial oscillation (QBO) with some indication of a 4 year component; and (3) the zonal characteristics of both the ozone and temperature trends were derived from ten years of total ozone and 50 mb temperature based on the Nimbus 7 TOMS measurements and the NMC analyses respectively.

Chandra, S.↗

DOE OSTI2025

Soil biogeochemical properties and metrics of tree-mycorrhizal dominance for a 25-Ha forest in South Central Indiana, USA.

This data package contains a dataset used in the papers “Seeing the forest for all the trees: Mycorrhizal-associated nutrient economies are modulated by stem density and the synchrony between overstory and understory communities” and “Mycorrhizal associations of tree species influence soil nitrogen dynamics via effects on soil acid–base chemistry”. Four csv files are included along with a dataset. The dataset features chemical soil properties for a single sampling campaign within the 25 Ha Lilly-Dickey Woods Smithsonian Forest Global Earth Observatory (ForestGEO) plot in South Central Indiana, USA (ldw_dat_raw.csv). Also included are separate files focused on pH (pH_data.csv), carbon and nitrogen (CN_data.csv), and nitrification rates (Nitrification_data.csv). These variables are commonly associated with the tree-mycorrhizal dominance of forest stands. In these data subsets, each soil variable was matched to a 10 meter radius neighborhood wherein metrics of tree-mycorrhizal dominance (basal area, stem count, importance value, etc.) were calculated. Models between these soil variables and dominance metrics were used to investigate how different assessments of mycorrhizal associated nutrient economies (MANE) capture these relationships. This research was performed as a part of the Smithsonian ForestGEO project. This data package can be used to explore spatial variability in soil chemistry within a mature hardwood forest, or it can be combined with the included tree data, other fine-scale spatial information, or other tree inventory data for the site to evaluate how soil chemistry varies with tree community composition or edaphic or topographic properties.

Craig, Matthew [ORNL] (ORCID:0000000288907920)↗

DOE OSTI2024

Geographical Insights into Suicide Mortality Through Spatial Machine Learning

Suicide mortality is a leading cause of death in the United States, with an upward trend that emphasizes its significance as a public health issue. Previous research has employed global models like ordinary least squares (OLS) regression and local models such as geographically weighted regression (GWR). While local models are useful for analyzing spatial variations in suicide mortality, they share limitations with traditional global models, particularly about their inability to handle multi-collinearity and non-linear relationships. Machine learning approaches, like random forests (RF), can address some of these limitations but often fail to account for spatial variability. This gap highlights the need for spatial ML models specifically designed to tackle suicide mortality. This research seeks to fill this void by using a geographically weighted random forest model (GWRF) to examine the associations between county-level suicide mortality in the U.S. from 2010 to 2020 and various social and environmental determinants of health. A key aspect of our methodology is disciplined feature selection, which reduces the pool of explanatory variables by about 90%. This refinement enhances the explanatory power of both global (R2 improved from 0.59 to 0.67) and local (R2 improved from 0.64 to 0.67) RF models while reducing their run times. An analysis of the importance scores for these selected features reveals that the drivers of suicide mortality vary by context. Thus, to effectively address regional disparities and inform targeted public health interventions, a holistic approach that incorporates multiple county-level characteristics is essential.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗

NASA NTRS2023

Spatial distributions of Tribrachidium, Rugoconites, and Obamus from the Ediacara Member (Rawnsley Quartzite), South Australia

The spatial distribution of in situ sessile organisms, including those from the fossil record, provides information about life histories, such as possible dispersal and/or settlement mechanisms, and how taxa interact with one another and their local environments. At Nilpena Ediacara National Park (NENP), South Australia, the exquisite preservation and excavation of 33 fossiliferous bedding planes from the Ediacara Member of the Rawnsley Quartzite reveals in situ communities of the Ediacara Biota. Here, the spatial distributions of three relatively common taxa, Tribrachidium, Rugoconites, and Obamus, occurring on excavated surfaces were analyzed using spatial point pattern analysis. Tribrachidium have a variable spatial distribution, implying that settlement or post-settlement conditions/preferences had an effect on populations. Rugoconites display aggregation, possibly related to their reproductive methods in combination with settlement location availability at the time of dispersal and/or settlement. Additionally, post-settlement environmental controls could have affected Rugoconites on other surfaces, resulting in lower populations and densities. Both Tribrachidium and Rugoconites also commonly occur as individuals or in low numbers on a number of beds, thus constraining possible reproductive strategies and environmental/substrate preferences. The distribution of Obamus is consistent with selective settlement, aggregating near conspecifics and on substrates of mature microbial mat. This dispersal process is the first example of substrate-selective dispersal among the Ediacara Biota, thus making Obamus similar to numerous modern sessile invertebrates with similar dispersal and settlement strategies. Resumen La distribución espacial de los organismos sésiles in situ, incluyendo los del registro fósil, brinda información sobre las historias de vida, tal como los posibles mecanismos de dispersión y/o asentamiento, y sobre cómo los taxones interactúan entre sí y entre sus entornos locales. En el Parque Nacional Nilpena Ediacara (NENP), Australia Meridional, la excelente preservación y excavación de 33 planos de lecho fosilífero del Miembro Ediacara de la Cuarcita Rawnsley revela comunidades in situ de la biota ediacárica. En este estudio analizamos las distribuciones espaciales de tres taxones relativamente comunes, Tribrachidium, Rugoconites y Obamus, que se encuentran en superficies excavadas mediante el análisis de patrones de puntos espaciales. Tribrachidium tiene una distribución espacial variable, lo que implica que las condiciones/preferencias durante o después del asentamiento tuvieron un efecto en las poblaciones. Rugoconites muestran agregación, posiblemente relacionado con sus métodos reproductivos en combinación con la disponibilidad de lugares de asentamiento en el momento de la dispersión y/o asentamiento. Además, los controles ambientales posteriormente al asentamiento podrían estar afectando a Rugoconites en otras superficies, lo que resultaría en poblaciones y densidades más bajas. Tanto Tribrachidium como Rugoconites ocurren como individuos en varios lechos, restringiendo las posibles estrategias reproductivas y las preferencias ambientales/de sustrato. La distribución de Obamus es consistente con un asentamiento selectivo, agregando cerca de sus congéneres y sobre sustratos de tapete microbiano maduro. Este proceso de dispersión es el primer ejemplo de dispersión selectiva de sustrato entre la biota ediacárica, lo que hace que Obamus sea similar a numerosos invertebrados sésiles modernos con estrategias similares de dispersión y asentamiento.

Phillip C. Boan↗

DOE OSTI2020

A spatial emergent constraint on the sensitivity of soil carbon turnover to global warming

Carbon cycle feedbacks represent large uncertainties in climate change projections, and the response of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil carbon depend on changes in litter and root inputs from plants and especially on reductions in the turnover time of soil carbon (τ s ) with warming. An approximation to the latter term for the top one metre of soil (ΔC s,τ ) can be diagnosed from projections made with the CMIP6 and CMIP5 Earth System Models (ESMs), and is found to span a large range even at 2 °C of global warming (-196 ± 117 PgC). Here, we present a constraint on ΔC s,τ , which makes use of current heterotrophic respiration and the spatial variability of τs inferred from observations. This spatial emergent constraint allows us to halve the uncertainty in ΔC s,τ at 2 °C to -232 ± 52 PgC.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Airborne Lidar Measurements of Ozone and Aerosol Profiles Over Major US Metropolitan Areas

During field missions in 2021 and 2023, the airborne NASA Langley Research Center High Spectral Resolution Lidar 2 (HSRL 2) measured the temporal and spatial evolution of ozone and aerosol distributions impacting urban air quality over the four most populated cities in the United States. HSRL-2 measurements in 2021 were acquired over the Houston metropolitan region, including Galveston Bay and the Houston Ship Channel, as part of the NASA Tracking Aerosol Convection Experiment – Air Quality (TRACER-AQ) mission conducted in collaboration with the Department of Energy. HSRL-2 measurements were acquired over Los Angeles, Chicago, and New York City in 2023 as part of the NASA Synergistic TEMPO Air Quality Science (STAQS) mission conducted in collaboration with the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) mission. HSRL 2 provided nadir vertical profiles of ozone, aerosol backscatter, extinction, and depolarization as the aircraft flew lawnmower type patterns at 9 km for several hours over these urban areas. HSRL-2 measured profiles of aerosol extinction and aerosol optical depth (AOD) via the HSRL technique at 355 and 532 nm and profiles of aerosol backscatter and depolarization at 355, 532, and 1064 nm. Mixed Layer Heights (MLH) were derived by locating sharp vertical gradients in the profiles of aerosol backscatter. The flights were comprised of up to three repeating lawnmower patterns over each city showing the evolution of the ozone and aerosol distributions from the morning through the afternoon. The HSRL-2 measurements reveal ozone enhancements near the surface as well as in the free troposphere above the mixed layer. Some lidar measurements over Chicago and New York City show the daytime boundary layer growing into elevated layers of biomass burning aerosol. These layers complicate efforts to use column-integrated satellite measurements to infer surface air quality. As expected, mixed layer height (MLH) typically increased significantly during the day; however, during some flights, particularly over the Houston area, MLH also showed large spatial variability associated with changes in surface cover and/or small scale circulations. Often HSRL-2 measurements of AOD also showed large spatial and temporal variability throughout the day over these cities. We discuss how the ozone and aerosol profiles are averaged over different vertical and horizontal scales near the surface for use in assessments of regional air quality models and near-surface ozone retrievals from NASA’s recently launched Tropospheric Emissions: Monitoring Pollution (TEMPO) satellite.

Lidar↗