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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 397 records · Page 22

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NASA NTRS1989

The indexed time table approach for planning and acting

A representation is discussed of symbolic temporal relations, called IxTeT, that is both powerful enough at the reasoning level for tasks such as plan generation, refinement and modification, and efficient enough for dealing with real time constraints in action monitoring and reactive planning. Such representation for dealing with time is needed in a teleoperated space robot. After a brief survey of known approaches, the proposed representation shows its computational efficiency for managing a large data base of temporal relations. Reactive planning with IxTeT is described and exemplified through the problem of mission planning and modification for a simple surveying satellite.

Ghallab, Malik↗

NASA NTRS1992

Radiation dose to critical body organs for October 1989 proton event

The Geostationary Operational Environmental Satellite (GOES-7) provides high-quality environmental data about the temporal development and energy characteristics of the protons emitted during a solar particle event. The GOES-7 time history of the hourly averaged integral proton flux for various particle kinetic energies are analyzed for the solar proton event occurring October 19-29, 1989. This event is similar to the August 1972 event that has been widely studied to estimate free-space and planetary radiation-protection requirements. By analyzing the time-history data, the dose rates, which can vary over many orders of magnitude in the early phases of the flare, can be estimated as well as the cumulative dose as a function of time. When basic transport results are coupled with detailed body organ thickness distributions calculated with the Computerized Anatomical Man and Computerized Anatomical Female models, the dose rates and cumulative doses to specific organs can be predicted. With these results, the risks of cancer incidence and mortality are estimated for astronauts in free space protected by various water shield thicknesses.

Simonsen, Lisa C.↗

NASA NTRS2008

Capabilities and Limitations of Space-Borne Passive Remote Sensing of Dust

Atmospheric dust particles have significant effects on the climate and the environment and despite notable recent advances in modeling and observation, wind-blown dust radiative effects remain poorly quantified in both magnitude and sign [IPCC, 2001]. To address this issue, many scientists are using passive satellite observations to study dust properties and to constrain emission/transport models, because the information provided is both time-resolved and global in coverage. In order to assess the effects of individual dust outbreaks on atmospheric radiation and circulation, relatively high temporal resolution (of the order of hours or days) is required in the observational data. Data should also be available over large geographical areas, as dust clouds may cover hundreds of thousands of square kilometers and will exhibit significant spatial variation in their vertical structure, composition and optical properties, both between and within dust events. Spatial and temporal data continuity is necessary if the large-scale impact of dust loading on climate over periods ranging from hours to months is to be assessed.

Kalashnikova, Olga↗

NASA NTRSDate not supplied

Assessment of High Resolution Commercial Satellite Geolocation Accuracy

Commercial companies such as Maxar, PlanetScope, and BlackSky have launched many satellites with revisit times ranging from two hours to one day and have built large archives of high resolution (1-3 m) Earth observing data. The high temporal resolution and global coverage of these satellites makes commercial satellite images ideal for scientists studying rapidly changing processes such as flooding and fires. A key step in assessing changes in these images is co-registration of various images to each other. High geolocation accuracy (at most 0.5 pixels of offset) allows for easy co-registration of images across different times and sensors. Here, we assess the latest PlanetScope imagery and evaluate it for geolocation accuracy. For our assessment, we compare the target image to a reference image with known geolocation accuracy (WorldView imagery) and determine the offset between these images by shifting them to maximize their Pearson Cross-Correlation (PCC) value. Offsets required to maximize the PCC give the geolocation accuracy of the target image relative to the reference image. Previously, our global assessment of Planet data revealed large variability in geolocation accuracy from one continent to another. The measured root mean squared errors (RMSEs) range from 5.4 m in North America to 14.9 m in Africa. We have updated this previous assessment to include both their newest SuperDove series as well as a more robust assessment of the temporal stability of PlanetScope's geolocation accuracy.

Alana G. Semple↗

NASA NTRS1981

Design and performance of the solar maximum mission Hard X-ray Burst Spectrometer

The Hard X-ray Burst Spectrometer acquires data on the temporal and energy distribution of solar X-rays in the energy region from 25 to 385 keV. The detector system is a CsI(Na) central detector, and an anti-coincidence shield with photomultiplier tubes optically coupled to the central and shield crystals. Additional detectors are included for calibration and South Atlantic Anomaly monitoring. A 15 channel pulse height analysis is performed over the energy range every 128 milliseconds. This instrument is capable of handling event rates up to 500 kHz and provides high rate data up to 100 kHz with low spectral distortion. Nine accumulated rates are telemetered every 8.192 seconds. A unique feature of the instrument is the ability to sample rates from the central detector or shield with a one millisecond minimum time resolution. Such samples are stored in a 32768 sample memory with a 40 percent pretrigger event history and a 60 percent posttrigger history.

Workman, L. G.↗

DOE OSTI2025

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2010

On the Long-Term Variability of Jupiter's Winds and Brightness as Observed from Hubble

Hubble Space Telescope Wide Field Planetary Camera 2 imaging data of Jupiter were combined with wind profiles from Voyager and Cassini data to study long-term variability in Jupiter's winds and cloud brightness. Searches for evidence of wind velocity periodicity yielded a few latitudes with potential variability; the most significant periods were found nearly symmetrically about the equator at 0 deg., 10-12 deg. N, and 14-18 deg. S planetographic latitude. The low to mid-latitude signals have components consistent with the measured stratospheric temperature Quasi-Quadrennial Oscillation (QQO) period of-5 years, while the equatorial signal is approximately seasonal and could be tied to mesoscale wave formation, robustness tests indicate that a constant or continuously varying periodic signal near 4.5 years would appear with high significance in the data periodograms as long as uncertainties or noise in the data are not of greater magnitude. However, the lack of a consistent signal over many latitudes makes it difficult to interpret as a QQO-related change. In addition, further analyses of calibrated 410-nm and 953-nm brightness scans found few corresponding changes in troposphere haze and cloud structure on QQO timescales. However, stratospheric haze reflectance at 255-nm did appear to vary on seasonal timescales, though the data do not have enough temporal coverage or photometric accuracy to be conclusive. Sufficient temporal coverage and spacing, as well as data quality, are critical to this type of search.

Simon-Miller, Amy A.↗

NASA NTRS2014

Prospects for Chlorophyll Fluorescence Remote Sensing from the Orbiting Carbon Observatory-2

The Orbiting Carbon Observatory-2 (OCO-2), scheduled to launch in July 2014, is a NASA mission designed to measure atmospheric CO2. Its main purpose is to allow inversions of net flux estimates of CO2 on regional to continental scales using the total column CO2 retrieved using high-resolution spectra in the 0.76, 1.6, and 2.0 nm ranges. Recently, it was shown that solar-induced chlorophyll fluorescence (SIF), a proxy for gross primary production (GPP, carbon uptake through photosynthesis), can be accurately retrieved from space using high spectral resolution radiances in the 750 nm range from the Japanese GOSAT and European GOME-2 instruments. Here, we use real OCO-2 thermal vacuum test data as well as a full repeat cycle (16 days) of simulated OCO-2 spectra under realistic conditions to evaluate the potential of OCO-2 for retrievals of chlorophyll fluorescence and also its dependence on clouds and aerosols. We find that the single-measurement precision is 0.3-0.5 Wm(exp -2)sr(exp -1) nm(exp -1) (15-25% of typical peak values), better than current measurements from space but still difficult to interpret on a single-sounding basis. The most significant advancement will come from smaller ground-pixel sizes and increased measurement frequency, with a 100-fold increase compared to GOSAT (and about 8 times higher than GOME-2). This will largely decrease the need for coarse spatial and temporal averaging in data analysis and pave the way to accurate local studies.We also find that the lack of full global mapping from the OCO-2 only incurs small representativeness errors on regional averages. Eventually, the combination of net ecosystem exchange (NEE) derived from CO2 source/sink inversions and SIF as proxy for GPP from the same satellite will provide a more process-based understanding of the global carbon cycle.

vegetation agriculture satellite↗

NASA NTRSDate not supplied

Atmospheric Variability and Measurement Uncertainty: Pitfalls in Averaging in situ Data

Satellite measurements and atmospheric models, two essential components of the integrated global observing system, provide crucial tools for monitoring and predicting regional and global foci, spanning numerous Earth Science fields. Unfortunately, models can lack the spatial and temporal resolution needed to resolve finer scale structure. Satellite measurements also have similar tempo-spatial restriction issues, but additionally can only measure certain species and can have biases that must be evaluated. Ground measurement networks are critical components of this system, by providing both independent inputs of species needed by models (including those that satellites do not provide) and assisting with investigation of biases in satellite products. Similarly, aircraft measurements play a vital role in closing the gaps between satellite measurements, model products, and ground monitoring networks by providing high accuracy, high-resolution data on local to regional spatial scales. Therefore, both ground-based and airborne observations are widely used to assess model predictions and satellite observations. One of the great challenges in using both ground and aircraft data in this fashion is matching the data temporally and spatially to the model/satellite data. A meaningful comparison with model or satellite requires a solid assessment of the variability of the in-situ measurements, which include both the instrument uncertainty and the statistical uncertainty due to atmospheric variability. While instrument uncertainty is generally more straightforwardly characterized, it can be challenging to accurately capture this variability uncertainty as it often presents in a non-Gaussian manner (e.g. emission plumes, frontal passages). We will present results examining spatial and temporal variability over a selection of scales relevant to satellite measurements and models of several in situ measurement species spanning both airborne and ground measurements. The extent of non-Gaussian variability will be quantified, and we will discuss additional statistical parameters that help assess the fitness of gaussian variability assumption when temporally or spatially averaging.

satellite validation↗

DOE OSTI2026

Short‐Term Hourly Weather Forecasting Using PredRNN With Image Preprocessing

Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed. In this study, we developed a short-term hourly weather forecast framework with a wavelet transform function for data preprocessing and a spatiotemporal DL model, PredRNN, for predicting five surface atmospheric variables, including wind speed and direction, mean sea level pressure (MSLP), temperature, and precipitation. The framework demonstrated promising results. It produces global forecasts at 0.25° (∼25 km) with a 1-day lead time RMSE of 1.8 m/s for wind components, 180 Pa for MSLP, and 1.8 K for temperature. Although our model does not surpass state-of-the-art AI weather forecast models across all metrics, it outperforms these models in precipitation forecasting and wind prediction at short lead times and achieves comparable accuracy for MSLP. Its native hourly forecasting capability, together with training on widely accessible GPU hardware, contributes meaningfully to the advancement of accessible DL weather forecasting methods. Our work highlights the importance of integrating temporal components and data transformation techniques to improve the predictability and accuracy of weather forecasts.

Tran, Hoang [Pacific Northwest National Laboratory↗

DOE OSTI2024

Aboveground Biomass Estimation Using NISAR Simulated ALOS-2 Time Series Data

Aboveground biomass (AGB) is a critical parameter to better understand the global carbon cycle and to develop sustainable forest management. However, a large uncertainty prevails. L-band SAR data have demonstrated strong potential to accurately retrieve AGB over low-biomass regions (<100 Mg ha-1). The upcoming NASA-ISRO Synthetic Aperture Radar mission will collect data at L- and S-band over earth’s landmass with a repeat period of 12 days, allowing us to have ample data for monitoring biomass and its dynamics. One of the key science requirements of the mission is to produce annual AGB maps at 1-ha resolution with RMS accuracy of 20 Mg/ha for 80 percentage of area over low-biomass regions in Calibration/Validation sites. The NISAR biomass algorithm will generate AGB maps based on the parameterization of semi-empirical model along with NISAR time-series dual pol data (HH and HV). To calibrate and validate the model for mission requirements, the mission will use reference estimates of AGB produced from ground inventory plots and airborne LiDAR data collected over selected sites distributed across different global ecoregions. This paper presents the initial results of the calibration/validation of the NISAR AGB retrieval algorithm over the Lenoir Landing (LENO), Alabama, USA site using NISAR simulated ALOS-2 time series data. Five multi-temporal dual-pol HH and HV NISAR Simulated ALOS 2 data collections were used as input to assess the performance of the model. The model AGB retrieval results shows that the NISAR model was able to achieve RMS accuracy within 20 Mg/ha.

Ramachandran, Naveen [Jet Propulsion Laboratory, C↗

NASA NTRS2000

A Coordinated RXTE/ASCA Study of Absorption Dips in Circinus X-1 at Phase Zero

This proposal was for an Rossi X Ray Timing Explorer (RXTE) study of Circinus X-1 with the ASCA satellite for the purpose of studying the iron emission feature during intensity dips. These dips had been discovered previously with ASCA and they appeared to vary in equivalent width and in the energy of the iron K edge. Proper analyses requires good continuum measurements which Rossi X Ray Timing Explorer/Proportional Counter Array (RXTE/PCA) could provide. Also, the PCA data would show any temporal evolution correlated with spectral evolution. RXTE provided 90 ks of data during March 3-5, 1998 which included phase zero which is where the dips usually take place. The ASCA observations were taken by Dr. Neil Brandt of Penn State University. Unfortunately, the source intensity showed far less dipping activity than is typical near phase zero. The light curve exhibited only very narrow sporadic dips rather than some long deep dips necessary for sufficient statistics in the ASCA and RXTE instruments for spectral studies. This prevented us from carrying out the primary goal of the proposal.

Bradt, Hale↗

NASA NTRS2000

Time-Resolved Spectroscopy of Active Binary Stars

This NASA grant covered EUVE observing and data analysis programs during EUVE Cycle 5 GO observing. The research involved a single Guest Observer project 97-EUVE-061 "Time-Resolved Spectroscopy of Active Binary Stars". The grant provided funding that covered 1.25 months of the PI's salary. The activities undertaken included observation planning and data analysis (both temporal and spectral). This project was awarded 910 ksec of observing time to study seven active binary stars, all but one of which were actually observed. Lambda-And was observed on 1997 Jul 30 - Aug 3 and Aug 7-14 for a total of 297 ksec; these observations showed two large complex flares that were analyzed by Osten & Brown (1999). AR Psc, observed for 350 ksec on 1997 Aug 27 - Sep 13, showed only relatively small flares that were also discussed by Osten & Brown (1999). EUVE observations of El Eri were obtained on 1994 August 24-28, simultaneous with ASCA X-ray spectra. Four flares were detected by EUVE with one of these also observed simultaneously, by ASCA. The other three EUVE observations were of the stars BY Dra (1997 Sep 22-28), V478 Lyr (1998 May 18-27), and sigma Gem (1998 Dec 10-22). The first two stars showed a few small flares. The sigma Gem data shows a beautiful complete flare with a factor of ten peak brightness compared to quiescence. The flare rise and almost all the decay phase are observed. Unfortunately no observations in other spectral regions were obtained for these stars. Analysis of the lambda-And and AR Psc observations is complete and the results were published in Osten & Brown (1999). Analysis of the BY Dra, V478 Lyr and sigma Gem EUVE data is complete and will be published in Osten (2000, in prep.). The El Eri EUV analysis is also completed and the simultaneous EUV/X-ray study will be published in Osten et al. (2000, in prep.). Both these latter papers will be submitted in summer 2000. All these results will form part of Rachel Osten's PhD thesis.

Brown, Alexander↗

NASA NTRS2011

Remote Sensing of Urban Land Cover/Land Use Change, Surface Thermal Responses, and Potential Meteorological and Climate Change Impacts

City growth influences the development of the urban heat island (UHI), but the effect that local meteorology has on the UHI is less well known. This paper presents some preliminary findings from a study that uses multitemporal Landsat TM and ASTER data to evaluate land cover/land use change (LULCC) over the NASA Marshall Space Flight Center (MFSC) and its Huntsville, AL metropolitan area. Landsat NLCD data for 1992 and 2001 have been used to evaluate LULCC for MSFC and the surrounding urban area. Land surface temperature (LST) and emissivity derived from NLCD data have also been analyzed to assess changes in these parameters in relation to LULCC. Additionally, LULCC, LST, and emissivity have been identified from ASTER data from 2001 and 2011 to provide a comparison with the 2001 NLCD and as a measure of current conditions within the study area. As anticipated, the multi-temporal NLCD and ASTER data show that significant changes have occurred in land covers, LST, and emissivity within and around MSFC. The patterns and arrangement of these changes, however, is significant because the juxtaposition of urban land covers within and outside of MSFC provides insight on what impacts at a local to regional scale, the inter-linkage of these changes potentially have on meteorology. To further analyze these interactions between LULCC, LST, and emissivity with the lower atmosphere, a network of eleven weather stations has been established across the MSFC property. These weather stations provide data at a 10 minute interval, and these data are uplinked for use by MSFC facilities operations and the National Weather Service. The weather data are also integrated within a larger network of meteorological stations across north Alabama. Given that the MSFC weather stations will operate for an extended period of time, they can be used to evaluate how the building of new structures, and changes in roadways, and green spaces as identified in the MSFC master plan for the future, will potentially affect land cover LSTs across the Center. Moreover, the weather stations will also provide baseline data for developing a better understanding of how localized weather factors, such as extreme rainfall and heat events, affect micrometeorology. These data can also be used to model the interrelationships between LSTs and meteorology on a longer term basis to help evaluate how changes in these parameters can be quantified from satellite data collected in the future. In turn, the overall integration of multi-temporal meteorological information with LULCC, and LST data for MSFC proper and the surrounding Huntsville urbanized area can provide a perspective on how urban land surface types affect the meteorology in the boundary layer and ultimately, the UHI. Additionally, data such as this can be used as a foundation for modeling how climate change will potentially impact local and regional meteorology and conversely, how urban LULCC can or will influence changes on climate over the north Alabama area.

Quattrochi, Dale A.↗

NASA NTRS2011

Remote Sensing of Urban Land Cover/Land Use Change, Surface Thermal Responses, and Potential Meteorological and Climate Change Impacts

Quattrochi, Dale A.↗

NASA NTRS2016

A New Tropical Cyclone Dynamic Initialization Technique Using High Temporal and Spatial Density Atmospheric Motion Vectors and Airborne Field Campaign Data

Background: Initialization of tropical cyclones in numerical weather prediction (NWP) systems is a great challenge: Mass-wind ﬁeld balance; Secondary circulation and heating; Asymmetries. There can be large adjustments in structure and intensity in the ﬁrst 24 hours if the initial vortex is not in balance: Spurious gravity waves; Spin-up (model and physics). Existing mesoscale NWP model TC (Tropical Cyclone) initialization strategies: Bogus vortex, cold start from global analyses; 3DVAR or 4DVAR, possibly with synthetic observations; EnKF (Ensemble Kalman Filter); Dynamic initialization. Dynamic initialization allows vortex to have improved balance and physics spin-up at the initial time (e.g., Hendricks et al. 2013, 2011; Nguyen and Chen 2011; Fiorino and Warner 1981; Hoke and Anthes 1976). Himawari-8 geostationary satellite has capability of continuous imagery (10-minutes) over the full disk: New GOES-R satellites will have same capability. This will allow for unprecedented observations of tropical cyclones. However, current data assimila1on systems are not capable of ingesting such high temporal observations (Atmospheric Mo1on Vectors - AMVs). Hourly AMVs are produced, and thinned to 100-kilometer spacing in the horizontal. An entirely new data assimilation concept is required to utilize these observations.

remote sensing↗

NASA NTRSDate not supplied

TPSAS-NF1676L-17943-DND

SAGE II used the solar occultation technique to measure atmospheric ozone for 21 years between 1984 and 2005 and has continued to play a key role in numerous international assessments. Traditionally, the determination of trends in ozone using SAGE II data has been done via time series analysis of monthly mean data within specific latitude bands. However, given the sparse sampling of SAGE II data, this will introduce temporal and/or spatial biases, particularly in the period after late 2000 when the instrument operated at half-duty cycle. Herein we present a new method for performing time series analysis of unevenly sampled data (in particular SAGE II); namely, we utilize the daily mean data and perform a simultaneous temporal and spatial analysis, making use of a Fourier series to constrain latitudinal variation. This methodology is applied to both the previous (v6.2) and current (v7.0) versions of the SAGE II data. Results are discussed that show the improvements to the data between versions 6.2 and 7.0 that make time series analysis produce reduced residuals as well as the preliminary results of the time series itself, which reveal some of the limitations in currently used proxies for the regression.

Robert P Damadeo↗

NASA NTRS2017

Joint Assimilation of SMOS Brightness Temperature and GRACE Terrestrial Water Storage Observations for Improved Soil Moisture Estimation

Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0 - 5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle.

Soil Moisture↗