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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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At least 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Elevation Change of the Southern Greenland Ice Sheet from Satellite Radar Altimeter Data

Long-term changes in the thickness of the polar ice sheets are important indicators of climate change. Understanding the contributions to the global water mass balance from the accumulation or ablation of grounded ice in Greenland and Antarctica is considered crucial for determining the source of the about 2 mm/yr sea-level rise in the last century. Though the Antarctic ice sheet is much larger than its northern counterpart, the Greenland ice sheet is more likely to undergo dramatic changes in response to a warming trend. This can be attributed to the warmer Greenland climate, as well as a potential for amplification of a global warming trend in the polar regions of the Northern Hemisphere. In collaboration with Drs. Curt Davis and Craig Kluever of the University of Missouri, we are using data from satellite radar altimeters to measure changes in the elevation of the Southern Greenland ice sheet from 1978 to the present. Difficulties with systematic altimeter measurement errors, particularly in intersatellite comparisons, beset earlier studies of the Greenland ice sheet thickness. We use altimeter data collected contemporaneously over the global ocean to establish a reference for correcting ice-sheet data. In addition, the waveform data from the ice-sheet radar returns are reprocessed to better determine the range from the satellite to the ice surface. At JPL, we are focusing our efforts principally on the reduction of orbit errors and range biases in the measurement systems on the various altimeter missions. Our approach emphasizes global characterization and reduction of the long-period orbit errors and range biases using altimeter data from NASA's Ocean Pathfinder program. Along-track sea-height residuals are sequentially filtered and backwards smoothed, and the radial orbit errors are modeled as sinusoids with a wavelength equal to one revolution of the satellite. The amplitudes of the sinusoids are treated as exponentially-correlated noise processes with a time-constant of six days. Measurement errors (e.g., altimeter range bias) are simultaneously recovered as constant parameters. The corrections derived from the global ocean analysis are then applied over the Greenland ice sheet. The orbit error and measurement bias corrections for different missions are developed in a single framework to enable robust linkage of ice-sheet measurements from 1978 to the present. In 1998, we completed our re-evaluation of the 1978 Seasat and 1985-1989 Geosat Exact Repeat Mission data. The estimates of ice thickness over Southern Greenland (south of 72N and above 2000 m) from 1978 to 1988 show large regional variations (+/-18 cm/yr), but yield an overall rate of +1.5 +/- 0.5 cm/yr (one standard error). Accounting for systematic errors, the estimate may not be significantly different from the null growth rate. The average elevation change from 1978 to 1988 is too small to assess whether the Greenland ice sheet is undergoing a long-term change.

Haines, Bruce J.↗
NASA NTRS1985

Integrated analysis of error detection and recovery

An integrated modeling and analysis of error detection and recovery is presented. When fault latency and/or error latency exist, the system may suffer from multiple faults or error propagations which seriously deteriorate the fault-tolerant capability. Several detection models that enable analysis of the effect of detection mechanisms on the subsequent error handling operations and the overall system reliability were developed. Following detection of the faulty unit and reconfiguration of the system, the contaminated processes or tasks have to be recovered. The strategies of error recovery employed depend on the detection mechanisms and the available redundancy. Several recovery methods including the rollback recovery are considered. The recovery overhead is evaluated as an index of the capabilities of the detection and reconfiguration mechanisms.

Shin, K. G.↗
DOE OSTI2012

Temporal Expression-based Analysis of Metabolism

Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1977

Viking satellite orbit determination

During the summer of 1976, the two Viking spacecraft, each consisting of an orbiter-lander combination, were inserted into orbit about Mars. The paper describes the experiences of the Viking Satellite Orbit Determination Team in determining Mars centered ephemerides of the orbiters and positions of the landers from the two-way Doppler and range data, and synthesizes the different phases of the navigation plan which involves pre-flight modeling and error analysis for all Viking navigation functions from launch through landing. The problem of initial orbit convergence is solved by using DPODP's (Double Precision Orbit Determination Program) square-root batch data filter, and gravity models for both Viking I and Viking II were produced from the combination of short arc estimates. The importance of synchronous Viking orbits (with the rotational period of Mars) is stressed and future extended missions of the spacecraft are outlined.

Hildebrand, C. E.↗
NASA NTRS1988

Markov chains for testing redundant software

A preliminary design for a validation experiment has been developed that addresses several problems unique to assuring the extremely high quality of multiple-version programs in process-control software. The procedure uses Markov chains to model the error states of the multiple version programs. The programs are observed during simulated process-control testing, and estimates are obtained for the transition probabilities between the states of the Markov chain. The experimental Markov chain model is then expanded into a reliability model that takes into account the inertia of the system being controlled. The reliability of the multiple version software is computed from this reliability model at a given confidence level using confidence intervals obtained for the transition probabilities during the experiment. An example demonstrating the method is provided.

White, Allan L.↗
NASA NTRS2018

Polarization Decomposition and Temperature Bias Resolution for SMAP Passive Soil Moisture Retrieval Using Time Series Brightness Temperature Observations

In passive microwave remote sensing of soil moisture, the tau-omega (τ-ω) model has often been used to provide soil moisture estimates at a spatial scale representative of the satellite footprint dimensions. For modeling simplicity, model parameters such as the single scattering albedo (ω) and vegetation opacity (τ) that go into the geophysical inversion process are often assumed to be independent of polarizations. Although this absence of polarization dependence can often be justified in special cases as in low-frequency remote sensing or under dense vegetation conditions, it is not a robust assumption in general. Additional model parameterization errors arising from this assumption are possible, leading to degradation in soil moisture estimation accuracy. In this paper, we propose a time series approach to try to resolve the polarization dependence of several τ-ω model parameters as well as the temperature bias arising from the ancillary temperature data. The Version 4 of the Soil Moisture Active Passive (SMAP) Level 1B brightness temperature time series observations were used to illustrate the mechanics of this approach, with an emphasis on a comparison between resulting satellite soil moisture retrievals and in situ data collected at several core validation sites. It was found that this time series approach resulted in significant reduction of the dry bias exhibited in the current SMAP passive soil moisture data products, while retaining the same performance in other metrics of the current baseline passive soil moisture retrieval algorithm.

time series↗
DOE OSTI2019

Evaluation of Clouds in Version 1 of E3SM Atmosphere Model with Satellite and Simulators

This study systematically evaluates clouds simulated by the Energy Exascale Earth System Model (E3SM) Atmosphere Model version one (EAMv1) against satellite and ground-based cloud observations. Both low (1°) and high (0.25°) resolution EAMv1 configurations generally underestimate clouds in low and midlatitutes and overestimate clouds in the Arctic although the error is smaller in the high-resolution model. The underestimate of clouds is due to the underestimate of optically thin to intermediate clouds. EAMv1 overestimates the optically intermediate to thick clouds. Other model errors include the largely under-predicted stratocumulus along the coasts and high clouds over the tropical deep convection regions. The underestimate of thin clouds results in too much LW radiation being emitted to space and too little SW radiation being reflected back to space while the overestimate of optically intermediate and thick clouds leads to too little LW radiation being emitted to space and too much SW radiation being reflected back to space. EAMv1 shows better skill in reproducing the observed distribution of clouds and their properties and has smaller radiatively relevant errors in the distribution of clouds than most of the CFMIP2 models. It produces more supercooled liquid cloud fraction than CAM5 and most CMIP5 models owing to a new ice nucleation scheme and a reduction of ice deposition growth rate. It simulates the diurnal variation of clouds during the summer season at SGP qualitatively well, with both the evolution of shallow clouds and the nocturnal peak in high clouds well captured, however, it largely overestimates the observed magnitude.

Zhang, Yuying↗
DOE OSTI2024

Evaluating Cloud Feedback Components in Observations and Their Representation in Climate Models

This study quantifies the contribution of individual cloud feedbacks to the total short-term cloud feedback in satellite observations over the period 2002–2014 and evaluates how they are represented in climate models. The observed positive total cloud feedback is primarily due to positive high-cloud altitude, extratropical high- and low-cloud optical depth, and land cloud amount feedbacks partially offset by negative tropical marine low-cloud feedback. Seventeen models from the Atmosphere Model Intercomparison Project of the sixth Coupled Model Intercomparison Project are analyzed. The models generally reproduce the observed moderate positive short-term cloud feedback. However, compared to satellite estimates, the models are systematically high-biased in tropical marine low-cloud and land cloud amount feedbacks and systematically low-biased in high-cloud altitude and extratropical high- and low-cloud optical depth feedbacks. Errors in modeled short-term cloud feedback components identified in this analysis highlight the need for improvements in model simulations of the response of high clouds and tropical marine low clouds. Our results suggest that skill in simulating interannual cloud feedback components may not indicate skill in simulating long-term cloud feedback components.

58 GEOSCIENCES↗
DOE OSTI2011

Bio-energy feedstock yields and their water quality benefits in Mississippi

Cellulosic and agricultural bio-energy crops can, under careful management, be harvested as feedstock for bio-fuels production and provide environmental benefits. However, it is required to quantify their relative advantages in feedstock production and water quality. The primary objective of this research was to evaluate potential feedstock yield and water quality benefit scenarios of bioenergy crops: Miscanthus (Miscanthus-giganteus), Switchgrass (Panicum virgatum), Johnsongrass (Sorghum halepense), Alfalfa (Medicago sativa L.), Soybean {Glycine max (L.) Merr.}, and Corn (Lea mays) in the Upper Pearl River watershed (UPRW), Mississippi using a Soil and Water Assessment Tool (SWAT). The SWAT model was calibrated (January 1981 to December 1994) and validated (January 1995 to September 2008) using monthly measured stream flow data. The calibrated and validated model determined good to very good performance for stream flow prediction (R2 and E from 0.60 to 0.86). The RMSE values (from 14 m3 s-1 to 37 m3 s-1) were estimated at similar levels of errors during model calibration and validation. The long-term average annual potential feedstock yield as an alternative energy source was determined the greatest when growing Miscanthus grass (373,849 Mg) as followed by Alfalfa (206,077 Mg), Switchgrass (132,077 Mg), Johnsongrass (47,576 Mg), Soybean (37,814 Mg), and Corn (22,069 Mg) in the pastureland and cropland of the watershed. Model results determined that average annual sediment yield from the Miscanthus grass scenario determined the least (1.16 Mg/ha) and corn scenario the greatest (12.04 Mg/ha). The SWAT model simulated results suggested that growing Miscanthus grass in the UPRW would have the greatest potential feedstock yield and water quality benefits.

Biofuels↗
DOE OSTI2025

Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the τ-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the τ-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments.

42 ENGINEERING↗
NASA NTRS2014

A Regional CO2 Observing System Simulation Experiment for the ASCENDS Satellite Mission

Top-down estimates of the spatiotemporal variations in emissions and uptake of CO2 will benefit from the increasing measurement density brought by recent and future additions to the suite of in situ and remote CO2 measurement platforms. In particular, the planned NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) satellite mission will provide greater coverage in cloudy regions, at high latitudes, and at night than passive satellite systems, as well as high precision and accuracy. In a novel approach to quantifying the ability of satellite column measurements to constrain CO2 fluxes, we use a portable library of footprints (surface influence functions) generated by the WRF-STILT Lagrangian transport model in a regional Bayesian synthesis inversion. The regional Lagrangian framework is well suited to make use of ASCENDS observations to constrain fluxes at high resolution, in this case at 1 degree latitude x 1 degree longitude and weekly for North America. We consider random measurement errors only, modeled as a function of mission and instrument design specifications along with realistic atmospheric and surface conditions. We find that the ASCENDS observations could potentially reduce flux uncertainties substantially at biome and finer scales. At the 1 degree x 1 degree, weekly scale, the largest uncertainty reductions, on the order of 50 percent, occur where and when there is good coverage by observations with low measurement errors and the a priori uncertainties are large. Uncertainty reductions are smaller for a 1.57 micron candidate wavelength than for a 2.05 micron wavelength, and are smaller for the higher of the two measurement error levels that we consider (1.0 ppm vs. 0.5 ppm clear-sky error at Railroad Valley, Nevada). Uncertainty reductions at the annual, biome scale range from 40 percent to 75 percent across our four instrument design cases, and from 65 percent to 85 percent for the continent as a whole. Our uncertainty reductions at various scales are substantially smaller than those from a global ASCENDS inversion on a coarser grid, demonstrating how quantitative results can depend on inversion methodology. The a posteriori flux uncertainties we obtain, ranging from 0.01 to 0.06 Pg C yr-1 across the biomes, would meet requirements for improved understanding of long-term carbon sinks suggested by a previous study.

remote sensing↗
DOE OSTI2026

Scaling open-weight large language models for hydropower regulatory information extraction: A systematic analysis

Information extraction from regulatory and technical documents using large language models (LLMs) involves practical trade-offs between extraction quality and computational cost. We evaluate eight open-weight LLMs spanning 0.6B–70B parameters on hydropower licensing documents and report deployment-oriented evidence under a unified extraction schema and evaluation protocol. Across the model set, we observe clear scale-dependent trends in both baseline extraction quality and the effectiveness of reflective reasoning (self-checking) under our fixed-prompt, no-augmentation setting. Mid-scale models often provide a favorable balance of accuracy and efficiency, whereas the smallest models show limited or inconsistent gains from the reasoning variants tested. Larger models achieve the highest overall F1 scores but incur substantially greater compute and infrastructure requirements. We further find that reliability failure modes can distort conventional metrics in this domain: in particular, high recall can coincide with systematic extraction errors when models fabricate values for fields that are absent from the source text, underscoring the importance of conservative null handling and evidence-grounded evaluation. Overall, our study provides a reproducible resource–performance comparison for open-weight LLM-based extraction in hydropower regulatory documentation and offers practical guidance for model selection under different deployment constraints.

Evaluation protocol↗
NASA NTRS1994

Mean sea surface height of the Antarctic Circumpolar Current from Geosat data: Method and application

The mean sea surface height across the Antarctic Circumpolar Current (ACC) has been reconstructed from height variability measured by the Geosat altimeter without assuming prior knowledge of the geoid. For this study, an automated technique has been developed to estimate mean sea surface height for each satellite ground track using a meandering Gaussian jet model, and errors have been estimated using Monte Carlo simulation. The results are objectively mapped to produce a picture of the mean Subantarctic and Polar Fronts, which together comprise the major components of the Antarctic Circumpolar Current. The meandering jet model explains between 40% and 70% of the height variance along the jet axes. The results show that the fronts are substantially steered by topography and that the jets have an average Gaussian width of about 44 km in the meridional direction and meander about 75 km to either side of their mean locations. The average height difference across the Subantarctic Front (SAF) is 0.7 m and across the Polar Front (PF) 0.6 m. The mean widths of the fronts are correlated with the size of the baroclinic Rossby radius.

Gille, Sarah T.↗
NASA NTRS1992

Precise tracking of the Magellan and Pioneer Venusorbiters by same-beam interferometry. Part 1: Dataaccuracy analysis

Simultaneous tracking of two spacecraft in orbit about a distant planet by two widely separated Earth-based radio antennas provides more-accurate positioning information than can be obtained by tracking each spacecraft separately. A demonstration of this tracking technique, referred to as same-beam interferometry (SBI), is currently being done using the Magellan and Pioneer 12 orbiters at Venus. Signals from both spacecraft fall within the same beamwidth of the Deep Space Station antennas. The plane-of-sky position difference between spacecraft is precisely determined by doubly differenced phase measurements. This radio metric measurement naturally complements line-of-sight Doppler. Data was first collected from Magellan and Pioneer 12 on August 11-12, 1990, shortly after Magellan was inserted into Venus orbit. Data were subsequently acquired in February and April 1991, providing a total of 34 hours of same-beam radio metric observables. Same-beam radio metric residuals have been analyzed and compared with model measurement error predictions. The predicted error is dominated by solar plasma fluctuations. The rms of the residuals is less than predicted by about 25 percent for 5-min averages. The shape of the spectrum computed from residuals is consistent with that derived from a model of solar plasma fluctuations. This data type can greatly aid navigation of a second spacecraft when the first is well-known in its orbit.

Border, J. S.↗
NASA NTRS2003

Impact of Flow-Dependent Error Correlations and Tropospheric Chemistry on Assimilated Ozone

The presentation compares different versions of a global three-dimensional ozone data assimilation system developed at NASA's Data Assimilation Office. The Solar Backscatter Ultraviolet/2 (SBUV/2) total and partial ozone column retrievals are the sole data assimilated in all of the experiments presented. We study the impact of changing the forecast error covariance model from a version assuming static correlations with a one that captures a short-term Lagrangian evolution of those correlations. This is further combined with a study of the impact of neglecting the tropospheric ozone production, loss and dry deposition rates, which are obtained from the Harvard GEOS-CHEM model. We compare statistical characteristics of the assimilated data and the results of validation against independent observations, obtained from WMO balloon-borne sondes and the Polar Ozone and Aerosol Measurement (POAM) III instrument. Experiments show that allowing forecast error correlations to evolve with the flow results in positive impact on assimilated ozone within the regions where data were not assimilated, particularly at high latitudes in both hemispheres. On the other hand, the main sensitivity to tropospheric chemistry is in the Tropics and sub-Tropics. The best agreement between the assimilated ozone and the in-situ sonde data is in the experiment using both flow-dependent error covariances and tropospheric chemistry.

Wargan, K.↗
NASA NTRS1994

Application of artificial neural networks in hydrological modeling: A case study of runoff simulation of a Himalayan glacier basin

The simulation of runoff from a Himalayan Glacier basin using an Artificial Neural Network (ANN) is presented. The performance of the ANN model is found to be superior to the Energy Balance Model and the Multiple Regression model. The RMS Error is used as the figure of merit for judging the performance of the three models, and the RMS Error for the ANN model is the latest of the three models. The ANN is faster in learning and exhibits excellent system generalization characteristics.

Buch, A. M.↗
NASA NTRS2001

DAO's Next Generation Physical-Space/Finite-Volume Data Assimilation System: Formulation and Initial Evaluation

The Physical-space/Finite-volume Data Assimilation System (fvDAS) is the next generation global atmospheric data assimilation system in development at the Data Assimilation Office at NASA's Goddard Space Flight Center. It is based on a new finite-volume general circulation model jointly developed by NASA and NCAR, and on the Physical-Space Statistical Analysis System (PSAS) developed at the DAO. In this talk we will describe the general system formulation, the adaptive quality control and general aspects of the error covariance modeling. The NASA-NCAR GCM is a completely new model which replaces the CEOs GCM used in the previous GEOS-1/2/3 Data Assimilation systems. A particular configuration of adaptive Statistical Quality Control and the Physical-space Statistical Analysis System (PSAS) are currently implemented in DAO's operational Data Assimilation System. However, the unique finite-volume formulation of the NASA-NCAR GCM, combined with the generality of the observation-space formulation of PSAS, provides for a very simple and accurate model-analysis interface. The system assimilates a variety of conventional and satellite observations. In particular, TOVS Level 1B radiances are assimilated using a 1-D variational scheme, both in clear sky and cloudy conditions. Computationally, the fvDAS runs approximately 10 times faster than the operational GEOS-Terra system. We will show that the next-generation fvDAS has much improved observation-minus-6hr forecast (O-F) statistics, as well as 5-day forecast skills. Top of the atmosphere radiation fields are in closer agreement with CERES measurements, with realistic precipitation and moisture fields. We will also show that the finite-volume formulation of the fvDAS produce assimilated fields which are more suitable for driving constituent transport models.

daSilva, A.↗