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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An improved representation of the relationship between photosynthesis and stomatal conductance leads to more stable estimation of conductance parameters and improves the goodness-of-fit across diverse data sets

Stomata play a central role in surface-atmosphere exchange by controlling the flux of water and CO 2 between the leaf and the atmosphere. Representation of stomatal conductance (g sw ) is therefore an essential component of models that seek to simulate water and CO 2 exchange in plants and ecosystems. For given environmental conditions at the leaf surface (CO 2 concentration and vapor pressure deficit or relative humidity), models typically assume a linear relationship between g sw and photosynthetic CO 2 assimilation (A). However, measurement of leaf-level g sw response curves to changes in A are rare, particularly in the tropics, resulting in only limited data to evaluate this key assumption. Here, we measured the response of g sw and A to irradiance in six tropical species at different leaf phenological stages. We showed that the relationship between g sw and A was not linear, challenging the key assumption upon which optimality theory is based-that the marginal cost of water gain is constant. Our data showed that increasing A resulted in a small increase in g sw at low irradiance, but a much larger increase at high irradiance. We reformulated the popular Unified Stomatal Optimization (USO) model to account for this phenomenon and to enable consistent estimation of the key conductance parameters g 0 and g 1 . Our modification of the USO model improved the goodness-of-fit and reduced bias, enabling robust estimation of conductance parameters at any irradiance. In addition, our modification revealed previously undetectable relationships between the stomatal slope parameter g 1 and other leaf traits. We also observed nonlinear behavior between A and g sw in independent datasets that included data collected from attached and detached leaves, and from plants grown at elevated CO 2 concentration. We propose that this empirical modification of the USO model can improve the measurement of g sw parameters and the estimation of plant and ecosystem-scale water and CO 2 fluxes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Integrating Large Scale Data Sets to Develop Predictive Hypotheses of Low-Dose Radiation-Induced Health Effects

Over one hundred years of radiation biology research has revealed much about the DNA damages induced by the deposition of energy from exposure to ionizing radiation and the subsequent cellular responses. However, there are still significant gaps in our understanding of how these might lead to detrimental health effects, particularly at low doses (100 mGy (milligray)). Recent advances in high throughput omics technologies enable interrogation of induced radiation effects at the genomic, proteomic and metabolomic levels. These include changes in gene expression, protein modifications, e.g., phosphorylation, acetylation, and methylation, and metabolic changes. We will discuss the integration of data obtained from multiple omics platforms to understand radiation dose, and dose rate effects in a complex human tissue model as a function of time. We will use as an example our results on the low dose responses in a 3D human skin model.

ionizing radiation↗
DOE OSTI2020

MWRRETV2 Value-Added Product Report: The Retrieval of Liquid Water Path and Precipitable Water Vapor from Microwave Radiometer – 3-Channel (MWR3C) Data Sets

This report provides a short description of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Microwave Radiometer – 3-Channel (MWR3C) Retrieval (MWRRETV2) Value-Added Product (VAP) algorithm. MWRRETV2 is an updated version of MWRRET, which extends the physical-iterative retrieval of liquid water path (LWP) and precipitable water vapor PWV) to 3-channel microwave radiometers beyond ARM's original standard 2-channel (23-and 31-GHz) systems. The 89-GHz channel in the 3-channel microwave radiometers has approximately three times the sensitivity to liquid water as the 31-GHz channel, thus improving LWP retrieval accuracy when LWP is less than 100 g/m2. We present a general overview of the technique, input parameters, and output products, and describe data quality checks.

47 OTHER INSTRUMENTATION↗
NASA NTRS2019

The NANOGrav 12.5 yr Data Set: The Frequency Dependence of Pulse Jitter in Precision Millisecond Pulsars

Low-frequency gravitational-wave experiments require the highest timing precision from an array of the moststable millisecond pulsars. Several known sources of noise on short timescales in single radio pulsar observationsare well described by a simple model of three components: template fitting from a finite signal-to-noise ratio, pulsephase/amplitude jitter from single-pulse stochasticity, and scintillation errors from short-timescale interstellarscattering variations. Currently template-fitting errors dominate, but as radio telescopes push toward higher signalto-noise ratios, jitter becomes the next dominant term for most millisecond pulsars. Understanding the statistics ofjitter becomes crucial for properly characterizing arrival time uncertainties. We characterize the radio frequencydependence of jitter using data on 48 pulsars in the North American Nanohertz Observatory for GravitationalWaves timing program. We detect significant jitter in 43 of the pulsars and test several functional forms for itsfrequency dependence; we find significant frequency dependence for 30 pulsars. We find moderate correlations ofrms jitter with pulse width (R = 0.62) and number of profile components (R = 0.40); the single-pulse rms jitter istypically 1% of pulse phase. The average frequency dependence for all pulsars using a power-law model hasindex 0.42. We investigate the jitter variations for the interpulse of PSR B1937+21 and find no significantdeviations from the main pulse rms jitter. We also test the time variation of jitter in two pulsars and find thatsystematics likely bias the results for high-precision pulsars. Pulsar timing array analyses must properly modeljitter as a significant component of the noise within the detector.

Lam, M. T.↗
NASA NTRSDate not supplied

A Comparative Study of Contrail Frequency Indices and GOES-16 Contrail Data Set

Contrail formations have been shown to contribute to the greenhouse effect: they are practically transparent to incoming solar radiation and do little to reflect heat away from Earth but are highly effective at trapping heat within Earth’s atmosphere. To understand the impact contrails have on climate change, contrail frequency indices (CFIs) can be used as a method to quantify aircraft-induced persistent contrails. These indices are capable of tracking long-term contrail formation and identify regions of airspace with the highest contrail formation rates. In this aper, an algorithm is proposed which is capable of using NASA Sherlock and Global Forecast System (GFS) datasets and computing CFIs over large geographic regions and long temporal intervals using NASA Ames’ High-End Computing Capability (HECC) supercomputing system. CFIs are computed using nowcast weather data and previously flown flight tracks. This paper calculated the CFIs of all twenty Air Route Traffic Control Centers in the National Airspace System on October 28th, 2019 and compared the distribution of non-zero CFIs with observed contrail data collected from GOES-16 Satellite data in order to assess the accuracy of the CFI system as a contrail prediction model. It was ultimately determined that the computed CFIs were broadly distributed in the same way as the GOES-16 contrail data and that the individual CFIs computed at the latitude/longitude points at which GOES-16 contrail masks were available had high precision and recall (at 0.75 and 0.86 respectively). While these validation results bode well for the accuracy of the CFI method, the number of provided GOES-16 masks was quite small. Future work should aim to increase the size of the GOES-16 dataset in order to perform a more comprehensive comparison between these two datasets.

Contrails↗
NASA NTRSDate not supplied

A Comparative Study of Contrail Frequency Indices and GOES-16 Contrail Data Set

Contrail formations have been shown to contribute to the greenhouse effect: they are practically transparent to incoming solar radiation and do little to reflect heat away from Earth but are highly effective at trapping heat within Earth’s atmosphere. To understand the impact contrails have on climate change, contrail frequency indices (CFIs) can be used as a method to quantify aircraft-induced persistent contrails. These indices are capable of tracking long-term contrail formation and identify regions of airspace with the highest contrail formation rates. In this aper, an algorithm is proposed which is capable of using NASA Sherlock and Global Forecast System (GFS) datasets and computing CFIs over large geographic regions and long temporal intervals using NASA Ames’ High-End Computing Capability (HECC) supercomputing system. CFIs are computed using nowcast weather data and previously flown flight tracks. This paper calculated the CFIs of all twenty Air Route Traffic Control Centers in the National Airspace System on October 28th, 2019 and compared the distribution of non-zero CFIs with observed contrail data collected from GOES-16 Satellite data in order to assess the accuracy of the CFI system as a contrail prediction model. It was ultimately determined that the computed CFIs were broadly distributed in the same way as the GOES-16 contrail data and that the individual CFIs computed at the latitude/longitude points at which GOES-16 contrail masks were available had high precision and recall (at 0.75 and 0.86 respectively). While these validation results bode well for the accuracy of the CFI method, the number of provided GOES-16 masks was quite small. Future work should aim to increase the size of the GOES-16 dataset in order to perform a more comprehensive comparison between these two datasets.

Contrails↗
DOE OSTI2025

Exploring Data Set Bias and Decision Support with Predictive Uncertainty Through Bayesian Approximations and Convolutional Neural Networks

Individual seismic catalogs can contain multiscale observations from fault level to global scales and associated waveforms from discrete events reflect crustal structure across many different scales and locations. Seismic network aperture, geographic location, and observation distance may not provide informative guidance or intuition on how different catalogs will behave across models trained under different conditions. We rely on uncertainty to provide guardrails for when to trust model decisions, but understanding when our uncertainty is trustworthy is an open challenge. Here, in this work, we explore Bayesian approximation methods for assigning predictive uncertainty in seismic event classification problems. We find that computationally expensive Bayesian approximations do not outperform simple ensemble methods. We also find that when exploiting multiple seismic event catalogs, joint training with data from all the catalogs combined with Bayesian approximations and supervised training for classification can obscure bias and result in less robust uncertainty while also not providing substantial performance benefits compared to training individual models for each catalog.

58 GEOSCIENCES↗
NASA NTRS1993

The Visible Human Project of the National Library of Medicine: Remote access and distribution of a multi-gigabyte data set

As part of the 1986 Long-Range Plan for the National Library of Medicine (NLM), the Planning Panel on Medical Education wrote that NLM should '...thoroughly and systematically investigate the technical requirements for and feasibility of instituting a biomedical images library.' The panel noted the increasing use of images in clinical practice and biomedical research. An image library would complement NLM's existing bibliographic and factual database services and would ideally be available through the same computer networks as are these current NLM services. Early in 1989, NLM's Board of Regents convened an ad hoc planning panel to explore possible roles for the NLM in the area of electronic image libraries. In its report to the Board of Regents, the NLM Planning Panel on Electronic Image Libraries recommended that 'NLM should undertake a first project building a digital image library of volumetric data representing a complete, normal adult male and female. This Visible Human Project will include digitized photographic images for cryosectioning, digital images derived from computerized tomography, and digital magnetic resonance images of cadavers.' The technologies needed to support digital high resolution image libraries, including rapid development; and that NLM encourage investigator-initiated research into methods for representing and linking spatial and textual information, structural informatics. The first part of the Visible Human Project is the acquisition of cross-sectional CT and MRI digital images and cross-sectional cryosectional photographic images of a representative male and female cadaver at an average of one millimeter intervals. The corresponding cross-sections in each of the three modalities are to be registerable with one another.

Ackerman, Michael J.↗
DOE OSTI2025

A Cloud-Tracking Data Set for the CSAPR2 Adaptive Scanning during TRACER

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility (Mather and Voyles 2013) deployed the first ARM Mobile Facility (AMF1; Miller et al. 2016) near LaPorte, Texas to support the Tracking Aerosol Convection Interactions Experiment (TRACER) (Jensen et al. 2025) near Houston, Texas. From October 2021 to September 2022, AMF1 was deployed to 29.67° N, 95.06° W near LaPorte, Texas and the 2nd Generation C-band Scanning ARM Precipitation Radar (CSAPR2) was deployed to a supplementary site at 29.53° N, 95.28° W (Figure 1). During an intensive operational period (IOP) from 1 June to 30 September 2022, the CSAPR2 sampled precipitation echoes in an adaptive scanning mode following the Multisensor Agile Adaptive Scanning (MAAS) framework (Kollias et al. 2020). MAAS helped optimize the CSAPR2 scan strategy to perform frequent plan position indicator (PPI) and range height indicator (RHI) scans (Lamer et al. 2023). Details of the CSAPR2 scanning, data processing, and calibration procedures used by the principal investigator (PI), and the PI data files are described by Oue et al. (2023). Details of the CSAPR2 operational performance, ARM data processing and correction procedures, and data quality masks are described by Feng et al. (2024a).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Complete collision data set for electrons scattering on molecular hydrogen and its isotopologues: IV. Vibrationally-resolved ionization of the ground and excited electronic states

Here, we present a comprehensive set of vibrationally-resolved cross sections for electron-impact ionization of molecular hydrogen and its isotopologues (H 2 , D 2 , T 2 , HD, HT, and DT) in both the ground and excited electronic states. We apply the adiabatic-nuclei molecular convergent close-coupling (MCCC) method to calculate cross sections from threshold to 1000 eV for ionization of the ground and excited vibrational levels of the X 1 Σ$^{+}_{g}$, B 1 Σ$^{+}_{u}$, C 1 π u , EF 1 Σ$^{+}_{g}$, a 3 Σ$^{+}_{g}$, and c 3 π u electronic states, representing all states with united-atoms-limit principle quantum number n=1–2. The cross sections are presented in graphical form and provided as both numerical values and analytic fit functions in supplementary data files. The data can also be downloaded from the MCCC database at mccc-db.org.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS2012

Evaluating the Contribution of NASA Remotely-Sensed Data Sets on a Convection-Allowing Forecast Model

The Short-term Prediction Research and Transition (SPoRT) Center is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service forecast offices. SPoRT provides real-time NASA products and capabilities to help its partners address specific operational forecast challenges. One challenge that forecasters face is using guidance from local and regional deterministic numerical models configured at convection-allowing resolution to help assess a variety of mesoscale/convective-scale phenomena such as sea-breezes, local wind circulations, and mesoscale convective weather potential on a given day. While guidance from convection-allowing models has proven valuable in many circumstances, the potential exists for model improvements by incorporating more representative land-water surface datasets, and by assimilating retrieved temperature and moisture profiles from hyper-spectral sounders. In order to help increase the accuracy of deterministic convection-allowing models, SPoRT produces real-time, 4-km CONUS forecasts using a configuration of the Weather Research and Forecasting (WRF) model (hereafter SPoRT-WRF) that includes unique NASA products and capabilities including 4-km resolution soil initialization data from the Land Information System (LIS), 2-km resolution SPoRT SST composites over oceans and large water bodies, high-resolution real-time Green Vegetation Fraction (GVF) composites derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument, and retrieved temperature and moisture profiles from the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). NCAR's Model Evaluation Tools (MET) verification package is used to generate statistics of model performance compared to in situ observations and rainfall analyses for three months during the summer of 2012 (June-August). Detailed analyses of specific severe weather outbreaks during the summer will be presented to assess the potential added-value of the SPoRT datasets and data assimilation methodology compared to a WRF configuration without the unique datasets and data assimilation.

Zavodsky, Bradley T.↗
NASA NTRS1998

The Use of a Satellite Climatological Data Set to Infer Large Scale Three Dimensional Flow Characteristics

Ever since the first satellite image loops from the 6.3 micron water vapor channel on the METEOSAT-1 in 1978, there have been numerous efforts (many to a great degree of success) to relate the water vapor radiance patterns to familiar atmospheric dynamic quantities. The realization of these efforts is becoming evident with the merging of satellite derived winds into predictive models (Velden et al., 1997; Swadley and Goerss, 1989). Another parameter that has been quantified from satellite water vapor channel measurements is upper tropospheric relative humidity (UTH) (e.g., Soden and Bretherton, 1996; Schmetz and Turpeinen, 1988). These humidity measurements, in turn, can be used to quantify upper tropospheric water vapor and its transport to more accurately diagnose climate changes (Lerner et al., 1998; Schmetz et al. 1995a) and quantify radiative processes in the upper troposphere. Also apparent in water vapor imagery animations are regions of subsiding and ascending air flow. Indeed, a component of the translated motions we observe are due to vertical velocities. The few attempts at exploiting this information have been met with a fair degree of success. Picon and Desbois (1990) statistically related Meteosat monthly mean water vapor radiances to six standard pressure levels of the European Centre for Medium Range Weather Forecast (ECMWF) model vertical velocities and found correlation coefficients of about 0.50 or less. This paper presents some preliminary results of viewing climatological satellite water vapor data in a different fashion. Specifically, we attempt to infer the three dimensional flow characteristics of the mid- to upper troposphere as portrayed by GOES VAS during the warm ENSO event (1987) and a subsequent cold period in 1998.

Lerner, Jeffrey A.↗
NASA NTRS2011

Utilization of Ancillary Data Sets for Conceptual SMAP Mission Algorithm Development and Product Generation

The planned Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond [1]. Scheduled to launch late in 2014, the proposed SMAP mission would provide high resolution and frequent revisit global mapping of soil moisture and freeze/thaw state, utilizing enhanced Radio Frequency Interference (RFI) mitigation approaches to collect new measurements of the hydrological condition of the Earth's surface. The SMAP instrument design incorporates an L-band radar (3 km) and an L band radiometer (40 km) sharing a single 6-meter rotating mesh antenna to provide measurements of soil moisture and landscape freeze/thaw state [2]. These observations would (1) improve our understanding of linkages between the Earth's water, energy, and carbon cycles, (2) benefit many application areas including numerical weather and climate prediction, flood and drought monitoring, agricultural productivity, human health, and national security, (3) help to address priority questions on climate change, and (4) potentially provide continuity with brightness temperature and soil moisture measurements from ESA's SMOS (Soil Moisture Ocean Salinity) and NASA's Aquarius missions. In the planned SMAP mission prelaunch time frame, baseline algorithms are being developed for generating (1) soil moisture products both from radiometer measurements on a 36 km grid and from combined radar/radiometer measurements on a 9 km grid, and (2) freeze/thaw products from radar measurements on a 3 km grid. These retrieval algorithms need a variety of global ancillary data, both static and dynamic, to run the retrieval models, constrain the retrievals, and provide flags for indicating retrieval quality. The choice of which ancillary dataset to use for a particular SMAP product would be based on a number of factors, including its availability and ease of use, its inherent error and resulting impact on the overall soil moisture or freeze/thaw retrieval accuracy, and its compatibility with similar choices made by the SMOS mission. All decisions regarding SMAP ancillary data sources would be fully documented by the SMAP Project and made available to the user community.

freeze/thaw↗
DOE OSTI2020

CheKiPEUQ Intro 2: Harnessing Uncertainties from Data Sets, Bayesian Design of Experiments in Chemical Kinetics**

When choosing experimental conditions, Bayesian statistical tools can predict the experimental choices which will yield the highest information gain. Experimental choices could be temperature, pressure, reaction time, number of measurements, reactor volume, etc.. Three example analyses are presented here, each using the software Chemical Kinetics Parameter Estimation and Uncertainty Quantification (CheKiPEUQ). Information gain is a measure of reduction of uncertainty in a model's parameters. The three chemical system examples presented each illustrate Bayesian Design of Experiments using information gain. In the first chemical example, temperature selection impacts the information gain for the free energy of reaction in a two-component equilibrium reaction. In the second example, temperature and pressure are explored for a competitive adsorption Langmuir replacement reaction system. Finally, the third example is a catalytic membrane reactor which is a culmination of the previous examples. The catalytic membrane reactor has a complex and nonlinear response in the observables which is solved by numerical evaluation. In the three examples, the experimental conditions are treated as design variables for maximizing information gain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Network Modeling of Complex Data Sets

We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.

Jones, Piet C.↗
DOE OSTI2022

Estimation of the Surface Fluxes for Heat and Momentum in Unstable Conditions with Machine Learning and Similarity Approaches for the LAFE Data Set

Abstract Measurements of three flux towers operated during the land atmosphere feedback experiment (LAFE) are used to investigate relationships between surface fluxes and variables of the land–atmosphere system. We study these relations by means of two machine learning (ML) techniques: multilayer perceptrons (MLP) and extreme gradient boosting (XGB). We compare their flux derivation performance with Monin–Obukhov similarity theory (MOST) and a similarity relationship using the bulk Richardson number (BRN). The ML approaches outperform MOST and BRN. Best agreement with the observations is achieved for the friction velocity. For the sensible heat flux and even more so for the latent heat flux, MOST and BRN deviate from the observations while MLP and XGB yield more accurate predictions. Using MOST and BRN for latent heat flux, the root mean square errors (RMSE) are 107 Wm $$^{-2}$$ - 2 and 121 Wm $$^{-2}$$ - 2 , respectively, as well as the intercepts of the regression lines are $$\approx 110$$ ≈ 110 Wm $$^{-2}$$ - 2 . For the ML methods, the RMSEs reduce to 31 Wm $$^{-2}$$ - 2 for MLP and 33 Wm $$^{-2}$$ - 2 for XGB as well as the intercepts to just 4 Wm $$^{-2}$$ - 2 for MLP and $$-1$$ - 1 Wm $$^{-2}$$ - 2 for XGB with slopes of the regression lines close to 1, respectively. These results indicate significant deficiencies of MOST and BRN, particularly for the derivation of the latent heat flux. In fact, in contrast to the established theories, feature importance weighting demonstrates that the ML methods base their improved derivations on net radiation, the incoming and outgoing shortwave radiations, the air temperature gradient, and the available water contents, but not on the water vapor gradient. The results imply that further studies of surface fluxes and other turbulent variables with ML techniques provide great promise for deriving advanced flux parameterizations and their implementation in land–atmosphere system models.

54 ENVIRONMENTAL SCIENCES↗