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

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NASA NTRS2020

Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting

Lack of national data on water-related ecosystems is a major challenge to achieving the Sustainable Development Goal (SDG) 6 targets by 2030. Monitoring surface water extent, wetlands, and water quality from space can be an important asset for many countries in support of SDG 6reporting. We demonstrate the potential for Earth observation (EO) data to support country reporting for SDG Indicator 6.6.1, ‘Change in the extent of water-related ecosystems over time’ and identify important considerations for countries using these data for SDG reporting. The spatial extent of water-related ecosystems, and the partial quality of water within these ecosystems is investigated for seven countries. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5, 7, and 8 with Shuttle Radar Topography Mission (SRTM) are used to measure surface water extent at 250 m and 30 m spatial resolution, respectively, in Cambodia, Jamaica, Peru, the Philippines, Senegal, Uganda, and Zambia. The extent of mangroves is mapped at 30 m spatial resolution using Landsat 8 Operational Land Imager (OLI), Sentinel-1, and SRTM data for Jamaica, Peru, and Senegal. Using Landsat 8 and Sentinel 2A imagery, total suspended solids and chlorophyll-a are mapped overtime for a select number of large surface water bodies in Peru, Senegal, and Zambia. All of the EO datasets used are of global coverage and publicly available at no cost. The temporal consistency and long time-series of many of the datasets enable replicability over time, making reporting of change from baseline values consistent and systematic. We find that statistical comparisons between different surface water data products can help provide some degree of confidence for countries during their validation process and highlight the need for accuracy assessments when using EO-based land change data for SDG reporting. We also raise concern that EO data in the context of SDG Indicator 6.6.1reporting may be more challenging for some countries, such as small island nations, than others to use in assessing the extent of water-related ecosystems due to scale limitations and climate variability. Country-driven validation of the EO data products remains a priority to ensure successful data integration in support of SDG Indicator 6.6.1 reporting. Multi-country studies such as this one can be valuable tools for helping to guide the evolution of SDG monitoring methodologies and provide a useful resource for countries reporting on water-related ecosystems. The EO data analyses and statistical methods used in this study can be easily replicated for country-driven validation of EO data products in the future.

Raha Hakimdavar↗
NASA NTRS2020

Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting

Lack of national data on water-related ecosystems is a major challenge to achieving the Sustainable Development Goal (SDG) 6 targets by 2030. Monitoring surface water extent, wetlands, and water quality from space can be an important asset for many countries in support of SDG 6 reporting. We demonstrate the potential for Earth observation (EO) data to support country reporting for SDG Indicator 6.6.1, ‘Change in the extent of water-related ecosystems over time’ and identify important considerations for countries using these data for SDG reporting. The spatial extent of water-related ecosystems, and the partial quality of water within these ecosystems is investigated for seven countries. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5, 7, and 8 with Shuttle Radar Topography Mission (SRTM) are used to measure surface water extent at 250 m and 30 m spatial resolution, respectively, in Cambodia, Jamaica, Peru, the Philippines, Senegal, Uganda, and Zambia. The extent of mangroves is mapped at 30 m spatial resolution using Landsat 8 Operational Land Imager (OLI), Sentinel-1, and SRTM data for Jamaica, Peru, and Senegal. Using Landsat 8 and Sentinel 2A imagery, total suspended solids and chlorophyll-a are mapped over time for a select number of large surface water bodies in Peru, Senegal, and Zambia. All of the EO datasets used are of global coverage and publicly available at no cost. The temporal consistency and long time-series of many of the datasets enable replicability over time, making reporting of change from baseline values consistent and systematic. We find that statistical comparisons between different surface water data products can help provide some degree of confidence for countries during their validation process and highlight the need for accuracy assessments when using EO-based land change data for SDG reporting. We also raise concern that EO data in the context of SDG Indicator 6.6.1 reporting may be more challenging for some countries, such as small island nations, than others to use in assessing the extent of water-related ecosystems due to scale limitations and climate variability. Country-driven validation of the EO data products remains a priority to ensure successful data integration in support of SDG Indicator 6.6.1 reporting. Multi-country studies such as this one can be valuable tools for helping to guide the evolution of SDG monitoring methodologies and provide a useful resource for countries reporting on water-related ecosystems. The EO data analyses and statistical methods used in this study can be easily replicated for country-driven validation of EO data products in the future.

water-related ecosystems; surface water extent; ma↗
NASA NTRS1984

Flatness of the universe - Reconciling theoretical prejudices with observational data

Theoretical prejudices argue strongly for a flat universe; however, observations do not support this view. It is pointed out that this apparent conflict could be resolved if the mass density of the universe today were dominated by (1) relativistic particles produced by the recent decay of massive, relic particle species, or by (2) a relic cosmological constant. Scenario (1) has several advantages in the context of galaxy formation, but must confront the problem of a young universe.

Turner, M. S.↗
NASA NTRS2019

Using NASA Earth Observation Data in ArcGIS

The NASA Goddard Earth Sciences Data and Information Services Center archives tens of thousands of Earth Observation (EO) parameters for land, atmosphere, and ocean. To facilitate GIS users to easily find, visualize, obtain, and analyze these EO data through, we developed an ArcGIS infrastructure with the Server, image services, Portal, and AOL. We will show how this capability supports broad GIS applications. Use cases including water management and air quality analyses will be demonstrated.

Wei, Jennifer↗
NASA NTRS2015

A Web Service and Android Application for the Distribution of Rainfall Estimates and Earth Observation Data

The full potential of Satellite Rainfall Estimates (SRE) can only be realized if timely access to the datasets is possible. Existing data distribution web portals are often focused on global products and offer limited customization options, especially for the purpose of routine regional monitoring. Furthermore, most online systems are designed to meet the needs of desktop users, limiting the compatibility with mobile devices. In response to the growing demand for SRE and to address the current limitations of available web portals a project was devised to create a set of freely available applications and services, available at a common portal that can: (1) simplify cross-platform access to Tropical Rainfall Measuring Mission Online Visualization and Analysis System (TOVAS) data (including from Android mobile devices), (2) provide customized and continuous monitoring of SRE in response to user demands and (3) combine data from different online data distribution services, including rainfall estimates, river gauge measurements or imagery from Earth Observation missions at a single portal, known as the Tropical Rainfall Measuring Mission (TRMM) Explorer. The TRMM Explorer project suite includes a Python-based web service and Android applications capable of providing SRE and ancillary data in different intuitive formats with the focus on regional and continuous analysis. The outputs include dynamic plots, tables and data files that can also be used to feed downstream applications and services. A case study in Southern Angola is used to describe the potential of the TRMM Explorer for SRE distribution and analysis in the context of ungauged watersheds. The development of a collection of data distribution instances helped to validate the concept and identify the limitations of the program, in a real context and based on user feedback. The TRMM Explorer can successfully supplement existing web portals distributing SRE and provide a cost-efficient resource to small and medium-sized organizations with specific SRE monitoring needs, namely in developing and transition countries.

precipitation↗
NASA NTRS1987

Computation and analyses of averaged monthly zonal albedos at the top of the atmosphere using Nimbus-7 ERB observed data

The Nimbus-7 ERB experiment measures the Earth's albedo from a satellite in a fixed Sun synchronous orbit. The data is obtained at a fixed time of the day for each latitude observed. For Earth Radiation Budget studies it is normally assumed that the observed scene is invariant during the day and that the albedo varies only with the solar zenith angle. This paper presents a technique for computing mean zonal albedos as a function of the albedo (A sub s) of cloud free atmosphere, the albedo (A sub c) of cloudy atmosphere and of the cloud fractions. The values of A sub s and A sub c are obtained from radiation transfer theory and climatological values of the surface and cloud albedos. The albedos are a function of the solar zenith angle, latitude and solar declination. The cloud fractions are measured from the Nimbus-7 ERB albedos. The present study shows the importance of taking into account latitude variations in surface types and in cloud cover.

Dhuria, Harbans↗
NASA NTRS2006

Evaluating Clouds in Long-Term Cloud-Resolving Model Simulations with Observational Data

Two 20-day, continental midlatitude cases are simulated with a three-dimensional (3D) cloud-resolving model (CRM) and compared to Atmospheric Radiation Measurement (ARM) data. This evaluation of long-term cloud-resolving model simulations focuses on the evaluation of clouds and surface fluxes. All numerical experiments, as compared to observations, simulate surface precipitation well but over-predict clouds, especially in the upper troposphere. The sensitivity of cloud properties to dimensionality and other factors is studied to isolate the origins of the over prediction of clouds. Due to the difference in buoyancy damping between 2D and 3D models, surface precipitation fluctuates rapidly with time, and spurious dehumidification occurs near the tropopause in the 2D CRM. Surface fluxes from a land data assimilation system are compared with ARM observations. They are used in place of the ARM surface fluxes to test the sensitivity of simulated clouds to surface fluxes. Summertime simulations show that surface fluxes from the assimilation system bring about a better simulation of diurnal cloud variation in the lower troposphere.

Zeng, Xiping↗
NASA NTRSDate not supplied

The Surface Longwave Downward Fluxes of the NASA GEWEX SRB Release 4.0 IP Products: Validation Against the Surface-Based BSRN and PMEL Observed Data

Since the NASA Global Energy Water Exchanges (GEWEX) Surface Radiation Budget (SRB) project released its 3rd version of products in 2010, the GEWEX Data Assessments Panel (GDAP) has been working on integrating various data products to address issues in the closing of the global energy and water cycles. The 4th version of the SRB products, Rel. 4.0-IP, has integrated data products from the cloud, aerosol, atmosphere, ocean surface, and land surface projects, coordinating within GDAP, to produce a long-term time series of TOA and surface radiative estimates. The Rel. 4-IP shortwave products span 34 years continuously from July 1983 to June 2017 on a quasi-equal-area 1degree longitude by 1degree latitude grid system. The longwave products are for land only from 1983-07 to 1987-12; for both land and ocean from 1988-01 to 2009-12; and ocean only from 2010-01 to 2017-06. The data are provided at 3 hourly, 3-hourly-monthly, daily and monthly means. The ISCCP HXS clouds and radiances are the key cloud input of the current GEWEX SRB algorithms. In addition, the longwave algorithm has also made changes in cloud microphysical property, surface skin temperature input, surface emissivity, atmospheric profile, adding longwave aerosol optical properties, revising cloud overlap procedure, and so on. Details of changes in both inputs and algorithms are documented in a NASA Algorithm Theoretical Basis Document (ATBD). We have validated the surface longwave downward fluxes against the surface-based Baseline Surface Radiation Network (BSRN) and the Pacific Marine Environmental Laboratory (PMEL) buoy data. As of 2020, the BSRN archive has 12,116 site-months of observed records from 73 stations on all seven continents, and as of 2017, PMEL archive has 4389 buoy months of observed records from 64 buoys deployed in the tropics of Pacific, Atlantic and Indian Oceans. This paper presents how the SRB Rel. 4.0-IP surface longwave downward fluxes compare with these surface-based measurements and how the comparison statistics differ from that of Rel. 3.0.

GEWEX SRB↗
NASA NTRS2008

Five-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Data Processing, Sky Maps, and Basic Results

The Wilkinson Microwave Anisotropy Probe (WMAP) is a Medium-Class Explorer (MIDEX) satellite aimed at elucidating cosmology through full-sky observations of the cosmic microwave background (CMB). The WMAP full-sky maps of the temperature and polarization anisotropy in five frequency bands provide our most accurate view to date of conditions in the early universe. The multi-frequency data facilitate the separation of the CMB signal from foreground emission arising both from our Galaxy and from extragalactic sources. The CMB angular power spectrum derived from these maps exhibits a highly coherent acoustic peak structure which makes it possible to extract a wealth of information about the composition and history of the universe. as well as the processes that seeded the fluctuations. WMAP data have played a key role in establishing ACDM as the new standard model of cosmology (Bennett et al. 2003: Spergel et al. 2003; Hinshaw et al. 2007: Spergel et al. 2007): a flat universe dominated by dark energy, supplemented by dark matter and atoms with density fluctuations seeded by a Gaussian, adiabatic, nearly scale invariant process. The basic properties of this universe are determined by five numbers: the density of matter, the density of atoms. the age of the universe (or equivalently, the Hubble constant today), the amplitude of the initial fluctuations, and their scale dependence. By accurately measuring the first few peaks in the angular power spectrum, WMAP data have enabled the following accomplishments: Showing the dark matter must be non-baryonic and interact only weakly with atoms and radiation. The WMAP measurement of the dark matter density puts important constraints on supersymmetric dark matter models and on the properties of other dark matter candidates. With five years of data and a better determination of our beam response, this measurement has been significantly improved. Precise determination of the density of atoms in the universe. The agreement between the atomic density derived from WMAP and the density inferred from the deuterium abundance is an important test of the standard big bang model. Determination of the acoustic scale at redshift z = 1090. Similarly, the recent measurement of baryon acoustic oscillations (BAO) in the galaxy power spectrum (Eisenstein et al. 2005) has determined the acoustic scale at redshift z approx. 0.35. When combined, these standard rulers accurately measure the geometry of the universe and the properties of the dark energy. These data require a nearly flat universe dominated by dark energy consistent with a cosmological constant. Precise determination of the Hubble Constant, in conjunction with BAO observations. Even when allowing curvature (Omega(sub 0) does not equal 1) and a free dark energy equation of state (w does not equal -1), the acoustic data determine the Hubble constant to within 3%. The measured value is in excellent agreement with independent results from the Hubble Key Project (Freedman et al. 2001), providing yet another important consistency test for the standard model. Significant constraint of the basic properties of the primordial fluctuations. The anti-correlation seen in the temperature/polarization (TE) correlation spectrum on 4deg scales implies that the fluctuations are primarily adiabatic and rule out defect models and isocurvature models as the primary source of fluctuations (Peiris et al. 2003).

Weiland, J.L.↗
DOE OSTI2021

AI-Based Upgrades to Observational Data Centers to Facilitate Data Interoperability

Focal Areas: (1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Focal areas 2 and 3 have critical dependencies to the modernization described. Key benefits to the focal areas: (1) Modernized observatory framework capable of agile adaptive observation, (2) Advanced instrument and data tagging supporting AI data acquisition for assimilation or validation, and (3) Widespread data interoperability bridging Earth system prediction scales

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine Learning for a-posteriori model-observed data fusion to enhance predictive value of ESM output

The proposed research is consistent with focal areas 2 and 3: when successful, this effort will enhance the predictive value of Earth System Model (ESM) output by identifying and quantifying its systematic deviations from available observations and by correcting and recalibrating to existing data. The product is a hybrid process & data-driven modeling tool whose estimation can also bring insights through pattern discovery recognition on model deficiencies on the one hand and data gaps on the other.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
NASA NTRS2003

First Year Wilkinson Microwave Anisotropy Probe(WMAP) Observations: Data Processing Methods and Systematic Errors Limits

We describe the calibration and data processing methods used to generate full-sky maps of the cosmic microwave background (CMB) from the first year of Wilkinson Microwave Anisotropy Probe (WMAP) observations. Detailed limits on residual systematic errors are assigned based largely on analyses of the flight data supplemented, where necessary, with results from ground tests. The data are calibrated in flight using the dipole modulation of the CMB due to the observatory's motion around the Sun. This constitutes a full-beam calibration source. An iterative algorithm simultaneously fits the time-ordered data to obtain calibration parameters and pixelized sky map temperatures. The noise properties are determined by analyzing the time-ordered data with this sky signal estimate subtracted. Based on this, we apply a pre-whitening filter to the time-ordered data to remove a low level of l/f noise. We infer and correct for a small (approx. 1 %) transmission imbalance between the two sky inputs to each differential radiometer, and we subtract a small sidelobe correction from the 23 GHz (K band) map prior to further analysis. No other systematic error corrections are applied to the data. Calibration and baseline artifacts, including the response to environmental perturbations, are negligible. Systematic uncertainties are comparable to statistical uncertainties in the characterization of the beam response. Both are accounted for in the covariance matrix of the window function and are propagated to uncertainties in the final power spectrum. We characterize the combined upper limits to residual systematic uncertainties through the pixel covariance matrix.

Hinshaw, G.↗
NASA NTRS2003

Automating the Processing of Earth Observation Data

NASA s vision for Earth science is to build a "sensor web": an adaptive array of heterogeneous satellites and other sensors that will track important events, such as storms, and provide real-time information about the state of the Earth to a wide variety of customers. Achieving this vision will require automation not only in the scheduling of the observations but also in the processing of the resulting data. To address this need, we are developing a planner-based agent to automatically generate and execute data-flow programs to produce the requested data products.

Golden, Keith↗
NASA NTRS1981

Observational data needs for plasma phenomena

Bright comets display a rich variety of interesting plasma phenomena which occur over an enormous range of spatial scales, and which require different observational techniques to be studied effectively. Wide-angle photography of high time resolution is probably the best method of studying the phenomenon of largest known scale: the plasma tail disconnection event (DE), which has been attributed to magnetic reconnection at interplanetary sector boundary crossings. These structures usually accelerate as they recede from the head region and observed velocities are typically in the range 50 V km/s. They are often visible for several days following the time of disconnection, and are sometimes seen out past 0.2 AU from the cometary head. The following areas pertaining to plasma phenomena in the ionoshere are addressed: the existence, size, and heliocentric distance variations of the contact surface, and the observational signatures of magnetic reconnection at sector boundary crossings.

Niedner, M. B., Jr.↗
NASA NTRS1981

Multistage classification of multispectral Earth observational data: The design approach

An algorithm is proposed which predicts the optimal features at every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. Results comparing probabilities of error predicted by the proposed algorithm as a function of dimensionality as compared to experimental observations are shown for aircraft and LANDSAT data. Results are obtained for both real and simulated data. Finally, two binary tree examples which use the algorithm are presented to illustrate the usefulness of the procedure.

Bauer, M. E.↗
NASA NTRS1982

Experimental land observing data system feasibility study

An end-to-end data system to support a Shuttle-based Multispectral Linear Array (MLA) mission in the mid-1980's was defined. The experimental Land Observing System (ELOS) is discussed. A ground system that exploits extensive assets from the LANDSAT-D Program to effectively meet the objectives of the ELOS Mission was defined. The goal of 10 meter pixel precision, the variety of data acquisition capabilities, and the use of Shuttle are key to the mission requirements, Ground mission management functions are met through the use of GSFC's Multi-Satellite Operations Control Center (MSOCC). The MLA Image Generation Facility (MIGF) combines major hardware elements from the Applications Development Data System (ADDS) facility and LANDSAT Assessment System (LAS) with a special purpose MLA interface unit. LANDSAT-D image processing techniques, adapted to MLA characteristics, form the basis for the use of existing software and the definition of new software required.

Buckley, J. L.↗