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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Developing a Customizable Composite Drought Index for Pakistan

Pakistan has experienced intense agricultural droughts in recent years, with the southern provinces experiencing the most severe drought conditions. These prolonged dry conditions often result in failed crop production, impacting families and communities. To reduce the impacts a drought may have on a community by identifying dry conditions, drought indices are used. A customizable composite drought index (CDI) is developed to improve the spatial and temporal understanding of historical agricultural droughts that have affected Pakistan. An in depth analysis using 9 input variables will provide information regarding the most important variables for differing locations. This framework can enhance drought monitoring and forecasting systems. The performance of the CDI will be evaluated by using production data from different crops that have significant economic value in Pakistan. These crops include wheat, rice, maize, cotton and barley. The expected relationship between the CDI and crop production would be to see production decrease when the CDI decreases, an indication that crops failed due to drought. The CDI is made more precise by evaluating only agricultural areas using the months of the growing season for each specific crop. This will allow for a custom drought index to be developed based on crop type and geographic location that only uses the most important variables, while still capturing the full extent of historical drought.

Caily Schwartz↗
NASA NTRS2021

Spatio-Temporal Patterns of Optimal Landsat Data for Burn Severity Index Calculations: Implications for High Northern Latitudes Wildfire Research

Satellite remote sensing has been widely used for the evaluation of wildfire burn severity in various ecosystems. While a variety of remote sensing-based burn severity indices have been developed, the Landsat-based differ-enced Normalized Burn Ratio (dNBR) presents the most widely-used approach to burn severity assessment for fire research and management. Although dNBR-based approaches have been continuously updated, including the development of the relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), one key obstacle for the reliable applications of the burn severity indices in the high northern latitudes has not been adequately addressed. Specifically, optimal Landsat image pairs are very rarely available for the calculation of the burn severity indices in certain regions of the high northern latitudes (HNL), resulting in the burn severity index outputs calculated for a large number of wildfires being considerably affected by non-fire-related factors. The suboptimal selection of image pairs may have partially contributed to the lack of consistency in the performance of the burn severity indices in the HNL regions. In this paper, we systematically evaluated the impacts of sub-optimal image pairs on signal stability through two sets of analyses conducted at different spatial scales. First, at the regional scale, we examined the burn severity indices calculated for a selection of wildfires in Alaska. Here, we demonstrate the inconsistent performance of the same index calculated based on Landsat data that are typically considered as “optimal”. In addition, we show that the calculated indices may be more prone to the negative impacts of these inconsistencies, thus leading to reduced reliability, when there is limited Landsat data availability. Second, at the continental scale, we show that certain areas in the HNL, especially Alaska during the pre-2000 era, are subject to the potentially strong negative impact associated with the limited data availability. Through systematically analyzing this issue, we hope to not only divert more attention to it but also to provide potential solutions, based on which further improvements that may be of particular importance for research on Arctic wildfires could be made.

Dong Chen↗
NASA NTRS2022

The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model

This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test such hypothesis across the Contiguous United States during April 2015 – December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). Results show that the assimilation of leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multi-variate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.

Data assimilation↗
NASA NTRS2023

An Updated Geomagnetic Index-Based Model for Determining the Latitudinal Extent of Energetic Electron Precipitation

Energetic Electron Precipitation (EEP) from the Earth's plasma sheet and the radiation belts is an important feature of atmospheric dynamics through their destruction of ozone in the lower thermosphere and mesosphere. Therefore, understanding the magnitude of the atmospheric impact of the Sun-Earth interaction requires a comprehensive understanding of the intensity and location of EEP. This study improves the accuracy of a previous pressure-corrected Dst model that predicts the equatorward extent of >43, >114, and >292 keV EEP using the measurements from the Medium Energy Proton Electron Detector detector of six National Oceanic and Atmospheric Administration/Polar Orbiting Environmental Satellites and EUMETSAT/METOP satellites. The improvement is achieved through multiple linear regression of pressure-corrected Dst and pressure-corrected Ring Current (RC) indices. The RC index mitigates the baseline variation of the Dst index that created an inherent solar cycle bias in the previous model. The new model is then extended to the Southern Hemisphere (SH) after removing the South Atlantic Anomaly longitudes from the data. More than 80% of the residuals lie within ±1.8° Corrected Geomagnetic Latitude (CGMLat) in the Northern Hemisphere and within ±1.98° CGMLat in the SH.

E. M. Babu↗
NASA NTRSDate not supplied

Seasonal Prediction of an Extreme Temperature Index over the United States

Heat extremes have the potential to cause significant societal harm; advanced warming of the frequency of these events for a given season could help with planning and mitigation measures. While an individual temperature extreme event may not be predictable on seasonal time scales, the number of such events could be. In this study, we use an index representing the number of extreme days per summer to examine the ability of NASA’s Goddard Earth Observing System (GEOS) sub-seasonal to seasonal (S2S) prediction system to predict summertime temperature extremes over the continental United States. The index is defined as the number of days per summer (June-August) where the daily mean temperature is above the calendar-day 90th percentile and is computed for 1991-2020 using the GEOS-S2S seasonal retrospective forecasts as well as the Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2). Temporal correlations are assessed over this period at the grid point scale, indicating significant correlation between the retrospective forecasts and MERRA-2, particularly in the western half of the United States. Spatial correlations between the retrospective forecast and MERRA-2 indices are computed over the United States for each year revealing year-to-year variability in the forecast skill. We investigate local and remote processes associated with increased prediction skill of summertime temperature extremes in a given year.

Natalie Thomas↗
DOE OSTI2021

High optical transparency polymer aerogels using low refractive index monomers

A polymer aerogel has polymerizable monomers and crosslinkers, wherein at least one of the monomers or at least one of the crosslinkers has a refractive index of less than 1.5, and the polymer aerogel has a visible transmittance of at least 20%/3 mm, a haze of 50%/3 mm or lower, and a porosity of at least 10%. A method of producing an aerogel includes dissolving precursors into a solvent, wherein the precursors include monomers, crosslinkers, a controlling agent and an initiator to form a precursor solution, wherein at least one of the monomers or at least one of the crosslinkers has a refractive index of 1.5 or lower, polymerizing the precursor solution to form a gel polymer, and removing the solvent from the gel polymer to produce an aerogel polymer.

Bulger, Eli↗
DOE OSTI2021

Grand unified file indexing

Systems and methods are disclosed for a unified file index for a file system. In one example, a Grand Unified File Index (GUFI) includes a tree replicating the directory hierarchy of one or more primary filesystems, and individual metadata stores for each directory. The GUFI tree permits fast traversal, efficient user space access controls, and simple tree directed operations such as renames, moves, or permission changes. In some examples, the individual metadata stores can be implemented as embedded databases on flash storage for speed. In some examples, use of summary tables at the directory or subtree level can eliminate wasteful executions, prune tree traversal, and further improve performance. In various examples, efficient operation can be achieved from laptop to supercomputer scale, across a wide mix of file distributions and filesystems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High optical transparency polymer aerogels using low refractive index monomers

A method of producing a polymer aerogel includes dissolving precursors into a solvent, wherein the precursors include monomers, crosslinkers, a controlling agent and an initiator to form a precursor solution, wherein at least one of the monomers or at least one of the crosslinkers has a refractive index of 1.5 or lower, polymerizing the precursor solution to form a gel polymer, and removing the solvent from the gel polymer to produce the polymer aerogel. A method of producing a polymer aerogel include dissolving precursors into a solvent, wherein the precursors include monomers, crosslinkers, a controlling agent and an initiator to form a precursor solution, polymerizing the precursor solution to form a gel polymer, removing the solvent from the gel polymer to produce the polymer aerogel, and reducing a refractive index of one of either the gel polymer or the polymer aerogel.

Bulger, Eli↗
DOE OSTI2023

Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation

Whittle index policy is a heuristic to the intractable restless multi-armed bandits (RMAB) problem. Although it is provably asymptotically optimal, finding Whittle indices remains difficult. In this paper, we present Neural-Q-Whittle, a Whittle index based Q-learning algorithm for RMAB with neural network function approximation, which is an example of nonlinear two-timescale stochastic approximation with Q-function values updated on a faster timescale and Whittle indices on a slower timescale. Despite the empirical success of deep Q-learning, the non-asymptotic convergence rate of Neural-Q-Whittle, which couples neural networks with two-timescale Q-learning largely remains unclear. This paper provides a finite-time analysis of Neural-Q-Whittle, where data are generated from a Markov chain, and Q-function is approximated by a ReLU neural network. Our analysis leverages a Lyapunov drift approach to capture the evolution of two coupled parameters, and the nonlinearity in value function approximation further requires us to characterize the approximation error. Combing these provide Neural-Q-Whittle with convergence rate, where is the number of iterations.

reinforcement learning, structured learning, conve↗
DOE OSTI2022

Refractive Index and Abbe Number Tuning via 3D Printable Optical Quality Silica–Titania–Germania Glasses

The development of optical quality GeO 2 –TiO 2 –SiO 2 glasses compatible with direct ink writing (DIW) 3D printing is reported in this study. Colloidal GeO 2 and TiO 2 –SiO 2 core–shell feedstocks are prepared by a sol–gel method and converted to printable inks for DIW. Printed inks are subsequently densified to glass using heat treatment at temperatures up to 1100 °C in air. A series of print‐compatible glass compositions are prepared, yielding transparent glass with the highest refractive index n = 1.576. Herein, it is shown that more TiO 2 can be incorporated into the glass without haziness or scattering through the addition of GeO 2 . The mechanisms for this are discussed, and it is shown that crystallization persists in these glasses despite the lack of visible light scattering. Finally, the combination of both TiO 2 and GeO 2 into a ternary glass also is used to independently tune the dispersion properties of the glass, as demonstrated by measured refractive indices and Abbe numbers, which has potential implications for 3D‐printed optics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mutual Diffusion Coefficients and Refractive Index Increments of K 2 SO 4 (aq) at 298.15 K from Rayleigh Interferometry

Here, isothermal mutual diffusion coefficients (interdiffusion coefficients) were measured for K 2 SO 4 (aq) at 298.15 ± 0.005 K, at numerous concentrations ranging from dilute solutions to near saturation (0.59648 mol∙dm -3 ; 0.61349 mol∙kg -1 ) under free diffusion boundary conditions, using high precision Rayleigh interferometry. Under the experimental conditions these diffusion coefficients are on the volume-fixed reference frame D v . Two series of experiments were performed, the first using the traditional experimental approach with a mercury lamp source and with the interference patterns being recorded on glass photographic plates, and the second with a He-Ne laser light source and computerized data acquisition using a photodiode array. The results from both series of experiments are in excellent agreement, and generally yield diffusion coefficients precise to at least 0.002 x 10 -9 m 2 ∙s 1 (0.15% to 0.19%) and in most cases to 0.001 x 10 -9 m 2 ∙s -1 (0.07% to 0.1%). These experiments also yield accurate values of the refractive index differences for the solution pairs used in the diffusion experiments. The new diffusion coefficients are compared to two sets of published values of diffusion coefficients for K 2 SO 4 (aq) which are somewhat discrepant from each other. This study extends and complements our earlier work on the diffusion coefficients of the most common brine salts: NaCl(aq), KCl(aq), MgCl 2 (aq), CaCl 2 (aq), Na 2 SO 4 (aq), ] MgSO 4 (aq), NaHCO 3 (aq), and KHCO 3 (aq) at 298.15 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index

Abstract The second UN Sustainable Development Goal establishes food security as a priority for governments, multilateral organizations, and NGOs. These institutions track national-level food security performance with an array of metrics and weigh intervention options considering the leverage of many possible drivers. We studied the relationships between several candidate drivers and two response variables based on prominent measures of national food security: the 2019 Global Food Security Index (GFSI) and the Food Insecurity Experience Scale’s (FIES) estimate of the percentage of a nation’s population experiencing food security or mild food insecurity (FI ). We compared the contributions of explanatory variables in regressions predicting both response variables, and we further tested the stability of our results to changes in explanatory variable selection and in the countries included in regression model training and testing. At the cross-national level, the quantity and quality of a nation’s agricultural land were not predictive of either food security metric. We found mixed evidence that per-capita cereal production, per-hectare cereal yield, an aggregate governance metric, logistics performance, and extent of paid employment work were predictive of national food security. Household spending as measured by per-capita final consumption expenditure (HFCE) was consistently the strongest driver among those studied, alone explaining a median of 92% and 70% of variation (based on out-of-sample R 2 ) in GFSI and FI , respectively. The relative strength of HFCE as a predictor was observed for both response variables and was independent of the countries used for model training, the transformations applied to the explanatory variables prior to model training, and the variable selection technique used to specify multivariate regressions. The results of this cross-national analysis reinforce previous research supportive of a causal mechanism where, in the absence of exceptional local factors, an increase in income drives increase in food security. However, the strength of this effect varies depending on the countries included in regression model fitting. We demonstrate that using multiple response metrics, repeated random sampling of input data, and iterative variable selection facilitates a convergence of evidence approach to analyzing food security drivers.

42 ENGINEERING↗
DOE OSTI2020

Ability of Particulate Matter Index to describe sooting tendency of various gasoline formulations in a stratified-charge spark-ignition engine

This study investigates the ability of Particulate Matter Index (PMI) to describe the sooting behavior of various gasoline formulations in a stratified-charge (SC) spark-ignition engine. Specifically, the engine was operated at 2000 rpm with an intake pressure of 130 kPa where soot formation is known to primarily occur in the bulk gases. Exhaust soot emissions were measured for nine test fuels at various exhaust gas recirculation levels. A comparison between measured soot levels and PMI shows that PMI is a relatively poor predictor of the sooting tendency of the tested fuels under lean SC combustion. Among the fuels, three fuels, namely the diisobutylene blend, High Olefin, and E30 fuels exhibit measured soot behavior opposite of that predicted by PMI. Optical diagnostics were utilized to further investigate the in-cylinder phenomena for these three fuels. Analysis of natural luminosity and diffused back-illumination extinction imaging suggests that fuel-induced differences in the amount of soot formed are responsible for a majority of the discrepancy in measured versus predicted sooting tendency. Fuel-induced differences in soot oxidation and spray development seem to play minor roles. Because the combustion and air-fuel mixing processes for lean SC combustion are different from conventional stoichiometric operation it is hypothesized that the PMI correlation needs to be modified to account for differences in stoichiometric air-fuel ratio and level of oxygenation between fuels. Furthermore, the role of fuel volatility in PMI possibly needs to be de-emphasized for SC operation with fuel injection into compression-heated gases.

42 ENGINEERING↗
DOE OSTI2020

A comparison of computational models for predicting yield sooting index

Sooting propensity, a measurement of how much particulate matter is produced when a fuel is burned, is a property of significant interest among researchers who are striving to discover the next generation of cleaner, more efficient fuels and fuel additives. Many compounds are not viable as fuels and/or fuel additives, and as a result, designing cleaner-burning biofuels using only experimental techniques is inefficient. Predictive models have been instrumental in reducing this inherent difficulty, providing researchers with a tool to preemptively screen compounds before production and testing. The present work compares the accuracies and interpretabilities of existing models used to predict a particular measure of sooting propensity, Yield Sooting Index (YSI). These models include artificial neural networks, graph neural networks, and multivariate equations. A novel equation for predicting YSI based on atom path count and bond order is proposed, which can highlight key structural components that contribute to YSI. It was found that artificial neural networks slightly outperform graph neural networks and greatly outperform multivariate equations in blind (test set) prediction accuracy; however, graph neural networks and multivariate equations provide significantly more interpretability as to how compound structure relates to YSI. Predictions of YSI are compared to experimental measurements for previously un-tested compounds with cetane numbers comparable to diesel fuel (50-60) (butyl decanoate, ethyl decanoate, 1,4-bis(ethenoxymethyl)cyclohexane, and 5-heptyloxolan-2-one), and it was found that these compounds produce significantly less soot compared to diesel fuel.

09 BIOMASS FUELS↗
DOE OSTI2023

Evaluating Retention Index Score Assumptions to Refine GC–MS Metabolite Identification

As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in gas chromatography–mass spectrometry (GC–MS) still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, in this study, we examined whether uninvestigated assumptions of the RI score are valid and propose ways to improve them. Query RIs were matched to library RI with a generous window of ±35 to avoid unintentional removal of valid compound identifications. Each match was manually verified as a true positive (TP), true negative, or unknown. Metabolites with at least 30 TP identifications were included in downstream analyses, resulting in a total of 87 metabolites from samples of varying complexity and type (e.g., amino acid mixtures, human urine, fungal species, and so on.). Our results showed that the RI score assumptions of normality, consistent variance across metabolites, and a mean error centered at 0 are often violated. We demonstrated through a cross-validation analysis that modifying these underlying assumptions according to empirical metabolite-specific distributions improved the TP and negative rankings. Further, we statistically determined the minimum number of samples required to estimate distributional parameters for scoring metrics. Overall, this work proposes a robust statistical pipeline to reduce the time bottleneck of metabolite identification by improving RI scores and thus minimize the effort to complete manual verification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Intra-canopy leaf trait variation facilitates high leaf area index and compensatory growth in a clonal woody encroaching shrub

Leaf trait variation enables plants to utilize large gradients of light availability that exist across canopies of high leaf area index (LAI), allowing for greater net carbon gain while reducing light availability for understory competitors. While these canopy dynamics are well understood in forest ecosystems, studies of canopy structure of woody shrubs in grasslands are lacking. To evaluate the investment strategy used by these shrubs, we investigated the vertical distribution of leaf traits and physiology across canopies of Cornus drummondii, the predominant woody encroaching shrub in the Kansas tallgrass prairie. We also examined the impact of disturbance by browsing and grazing on these factors. Our results reveal that leaf mass per area (LMA) and leaf nitrogen per area (Na) varied approximately threefold across canopies of C. drummondii, resulting in major differences in the physiological functioning of leaves. High LMA leaves had high photosynthetic capacity, while low LMA leaves had a novel strategy for maintaining light compensation points below ambient light levels. The vertical allocation of leaf traits in C. drummondii canopies was also modified in response to browsing, which increased light availability at deeper canopy depths. As a result, LMA and Na increased at lower canopy depths, leading to a greater photosynthetic capacity deeper in browsed canopies compared to control canopies. This response, along with increased light availability, facilitated greater photosynthesis and resource-use efficiency deeper in browsed canopies compared to control canopies. Furthermore, our results illustrate how C. drummondii facilitates high LAI canopies and a compensatory growth response to browsing—both of which are key factors contributing to the success of C. drummondii and other species responsible for grassland woody encroachment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Performance Index Assessment for the PV Fleet Performance Data Initiative

We report on 250 PV systems throughout the United States, comprising 157 MWdc of system capacity and more than 10,000 monthly performance index (PI) values. Loss factors were isolated including first-year start-up issues, snowfall, soiling and inverter downtime. Inverter availability was found to contribute significant system energy loss during the first six months of operation, with an average of 8% loss occurring during this period, and 2.3% on average thereafter. Other start-up issues beyond inverter downtime, such as partial string outage, contributed additional underperformance in the first year of operation across the fleet. Winter performance was also found to be below summer performance on average, likely due to snowfall. A relationship was found between monthly snowfall accumulation in centimeters and monthly under-performance, indicating a 6%-40% loss in months with measured snowfall, depending on climate. After correcting for availability, snow and startup loss, over 90% of systems were performing within 10% of monthly expectation based on satellite resource data and PVWatts production estimates.

loss factors↗
DOE OSTI2025

Vertical canopy gradients of respiration drive plant carbon budgets and leaf area index

Despite its importance for determining global carbon fluxes, leaf respiration remains poorly constrained in land surface models (LSMs). We tested the sensitivity of the Energy Exascale Earth System Model Land Model – Functionally Assembled Terrestrial Ecosystem Simulator (ELM-FATES) to variation in the canopy gradients of leaf maintenance respiration (R dark ). We ran global and point simulations varying the canopy gradient of R dark to explore the impacts on forest structure, composition, and carbon cycling. In global simulations, steeper canopy gradients of R dark lead to increased understory survival and leaf biomass. Leaf area index (LAI) increased up to 77% in tropical regions compared with the default parameterization, improving alignment with remotely sensed benchmarks. Global vegetation carbon varied from 308 Pg C to 449 Pg C across the ensemble. In tropical forest simulations, steeper gradients of R dark had a large impact on successional dynamics. Results show the importance of canopy gradients in leaf traits and fluxes for determining plant carbon budgets and emergent ecosystem properties such as competitive dynamics, LAI, and vegetation carbon. The high-model sensitivity to canopy gradients in R dark highlights the need for more observations of how leaf traits and fluxes vary along light micro-environments to inform critical dynamics in LSMs.

59 BASIC BIOLOGICAL SCIENCES↗