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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS ancillary data) were used to correct for surface temperature effects and to derive microwave emissivity. ECMWF data were also used for precipitation forecasts, presence of snow, and frozen ground. Vegetation options are described below. One year of soil moisture observations from a set of four watersheds in the U.S. were used to evaluate four different retrieval methodologies: (1) SMOS soil moisture estimates (version 400), (2) SeA soil moisture estimates using the SMOS/SMAP data with SMOS estimated vegetation optical depth, which is part of the SMOS level 2 product, (3) SeA soil moisture estimates using the SMOS/SMAP data and the MODIS-based vegetation climatology data, and (4) SeA soil moisture estimates using the SMOS/SMAP data and actual MODIS observations. The use of SMOS real-world global microwave observations and the analyses described here will help in the development and selection of different land surface parameters and ancillary observations needed for the SMAP soil moisture algorithms. These investigations will greatly improve the quality and reliability of this SMAP product at launch.

Bindlish, Rajat↗
NASA NTRS2017

Aerosol Optical Depth Changes in Version 4 CALIPSO Level 2 Product

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) version 4.10 (V4) products were released in November 2016 with substantial enhancements. There have been improvements in the V4 CALIOP level 2 aerosol optical depth (AOD) compared to V3 (version 3) due to various factors. AOD change from V3 to V4 is investigated by separating factors. CALIOP AOD was compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) and Aerosol Robotic Network (AERONET) for both V3 and V4.

Kim, Man-Hae↗
NASA NTRS1999

Level 2 Top-of-Atmosphere Albedo Algorithm Theorectical Basis

This Algorithm Theoretical Basis (ATB) document describes the algorithms used to retrieve the albedo parameters of the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Top-of-Atmosphere (TOA)/Cloud Product.

Top-of-Atmosphere albedo algorithm↗
DOE OSTI2026

ResStock Measure Documentation: Electric Vehicle Adoption With Level 2 Charging

This report is part of a series describing different ResStock (TM) measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Electric Vehicle Adoption With Level 2 Charging" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ResStock Measure Documentation: Efficient Electric Vehicle Adoption With Level 2 Charging

This report is part of a series describing different ResStock (TM) measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Efficient Electric Vehicle Adoption With Level 2 Charging" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ResStock Measure Documentation: Electric Vehicle Adoption With Level 2 Charging and Demand Flexibility

This report is part of a series describing different ResStock measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Electric Vehicle Adoption With Level 2 Charging and Demand Flexibility" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ResStock Measure Documentation: Efficient Electric Vehicle Adoption With Level 2 Charging and Demand Flexibility

This report is part of a series describing different ResStock measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Efficient Electric Vehicle Adoption With Level 2 Charging and Demand Flexibility" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2017

Technical Capability Level 2 Unmanned Aircraft System Traffic Management (UTM) Flight Demonstration: Description and Analysis

NASA's UAS Traffic Management (UTM) project concluded its second flight demonstration activity in late October 2016. This activity demonstrated the capabilities and functionality incorporated into its Technical Capability Level 2 (TCL2) concept, which envisions future operations that are low density, capable of being performed over sparsely populated areas, and allow for a concurrent mix of longer duration, beyond visual-line-of-sight flights and shorter flights within visual-line-of-sight (VLOS). To incorporate these features into a flight demonstration, a scenario-based approach was taken to address different aspects of the TCL2 environment and to meet defined objectives. This paper will describe elements of how the flight activity was conducted and present analyses regarding UTM operations, system messages, and alerting as they pertained to meeting the demonstration objectives and shedding light on research questions and lessons learned.NASA was able to successfully demonstrate complex UTMoperations in a TCL 2 environment in collaboration withindustry partners and the Reno-Stead Airport UAS test range.A scenario-based approach to the demonstration provided arelevant means through which the key elements and objectivesof the test could be captured. Although issues were identifiedthrough the testing and feedback of test participants, theoverall results were in line with the overall UTM objective ofsafely enabling large-scale UAS operations in low altitudeairspace.

Homola, Jeffrey↗
NASA NTRS2011

Evaluation of SMAP Level 2 Soil Moisture Algorithms Using SMOS Data

The objectives of the SMAP (Soil Moisture Active Passive) mission are global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolution, respectively. SMAP will provide soil moisture with a spatial resolution of 10 km with a 3-day revisit time at an accuracy of 0.04 m3/m3 [1]. In this paper we contribute to the development of the Level 2 soil moisture algorithm that is based on passive microwave observations by exploiting Soil Moisture Ocean Salinity (SMOS) satellite observations and products. SMOS brightness temperatures provide a global real-world, rather than simulated, test input for the SMAP radiometer-only soil moisture algorithm. Output of the potential SMAP algorithms will be compared to both in situ measurements and SMOS soil moisture products. The investigation will result in enhanced SMAP pre-launch algorithms for soil moisture.

Bindlish, Rajat↗
NASA NTRS2011

A Proposed Extension to the Soil Moisture and Ocean Salinity Level 2 Algorithm for Mixed Forest and Moderate Vegetation Pixels

The Soil Moisture and Ocean Salinity (SMOS)mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4 GHz) emission of the Earth's surface with a spatial resolution of 40-50 km.Uncertainty in the retrieval of soilmoisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soilmoisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops),with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40-60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE,-3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, -0.3%v/v bias) in pixels characterised by a critical amount of forest (40-60%), at the limited cost of only a crude estimate of the optical depth of the forested area (better than 35% uncertainty). This study makes use of an unprecedented data set of airborne L-band observations and ground supporting data from the National Airborne Field Experiment 2005 (NAFE'05), which allowed accurate characterisation of the land surface heterogeneity over an area equivalent in size to a SMOS pixel.

Panciera, Rocco↗
DOE OSTI2020

Absence of Low-Energy Shape Coexistence in 80 Ge: The Nonobservation of a Proposed Excited 0 2 + Level at 639 keV

The 80 Ge structure was investigated in a high-statistics β-decay experiment of 80 Ga using the GRIFFIN spectrometer at TRIUMF-ISAC through γ, β-e, e-γ and γ-γ spectroscopy. No evidence was found for the recently reported 0 2 + 639-keV level suggested as evidence for low-energy shape coexistence in 80Ge. Large-scale shell model calculations performed in 78,80,82 Ge place the 0 2 + level in 80 Ge at 2 MeV. The new experimental evidence combined with shell model predictions indicate that low-energy shape coexistence is not present in 80 Ge.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2018

CALIPSO IIR Version 2 Level 1b Calibrated Radiances: Analysis and Reduction of Residual Biases in the Northern Hemisphere

Version 2 of the Level 1b calibrated radiances of the Imaging Infrared Radiometer (IIR) on board the Cloud-Aerosol Lidar and Infrared Satellite Observation (CALIPSO) satellite has been released recently. This new version incorporates corrections of small but systematic seasonal calibration biases previously revealed in Version 1 data products mostly north of 30° N. These biases – of different amplitudes in the three IIR channels 8.65 µm (IIR1), 10.6 µm (IIR2), and 12.05 µm (IIR3) – were made apparent by a striping effect in images of IIR inter-channel brightness temperature differences (BTDs) and through seasonal warm biases of nighttime IIR brightness temperatures in the 30–60° N latitude range. The latter were highlighted through observed and simulated comparisons with similar channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua spacecraft. To characterize the calibration biases affecting Version 1 data, a semi-empirical approach is developed, which is based on the in-depth analysis of the IIR internal calibration procedure in conjunction with observations such as statistical comparisons with similar MODIS/Aqua channels. Two types of calibration biases are revealed: an equalization bias affecting part of the individual IIR images and a global bias affecting the radiometric level of each image. These biases are observed only when the temperature of the instrument increases, and they are found to be functions of elapsed time since night-to-day transition, regardless of the season. Correction coefficients of Version 1 radiances could thus be defined and implemented in the Version 2 code. As a result, the striping effect seen in Version 1 is significantly attenuated in Version 2. Systematic discrepancies between nighttime and daytime IIR–MODIS BTDs in the 30–60° N latitude range in summer are reduced from 0.2 K in Version 1 to 0.1 K in Version 2 for IIR1–MODIS29. For IIR2–MODIS31 and IIR3–MODIS32, they are reduced from 0.4 K to close to zero, except for IIR3–MODIS32 in June, where the night-minus-day difference is around −0.1 K.

Garnier, Anne↗
NASA NTRS2014

Exploring NASA OMI Level 2 Data With Visualization

Satellite data products are important for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if the satellite data are well utilized and interpreted, such as model inputs from satellite, or extreme events (such as volcano eruptions, dust storms, etc.).Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite data products provided by NASA and other organizations. Such obstacles may be avoided by allowing users to visualize satellite data as images, with accurate pixel-level (Level-2) information, including pixel coverage area delineation and science team recommended quality screening for individual geophysical parameters. We present a prototype service from the Goddard Earth Sciences Data and Information Services Center (GES DISC) supporting Aura OMI Level-2 Data with GIS-like capabilities. Functionality includes selecting data sources (e.g., multiple parameters under the same scene, like NO2 and SO2, or the same parameter with different aggregation methods, like NO2 in OMNO2G and OMNO2D products), user-defined area-of-interest and temporal extents, zooming, panning, overlaying, sliding, and data subsetting, reformatting, and reprojection. The system will allow any user-defined portal interface (front-end) to connect to our backend server with OGC standard-compliant Web Mapping Service (WMS) and Web Coverage Service (WCS) calls. This back-end service should greatly enhance its expandability to integrate additional outside data-map sources.

Level 2↗
NASA NTRS2014

Exploring NASA OMI Level 2 Data With Visualization

Satellite data products are important for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if the satellite data are well utilized and interpreted, such as model inputs from satellite, or extreme events (such as volcano eruptions, dust storms,... etc.). Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite data products provided by NASA and other organizations. Such obstacles may be avoided by allowing users to visualize satellite data as "images", with accurate pixel-level (Level-2) information, including pixel coverage area delineation and science team recommended quality screening for individual geophysical parameters. We present a prototype service from the Goddard Earth Sciences Data and Information Services Center (GES DISC) supporting Aura OMI Level-2 Data with GIS-like capabilities. Functionality includes selecting data sources (e.g., multiple parameters under the same scene, like NO2 and SO2, or the same parameter with different aggregation methods, like NO2 in OMNO2G and OMNO2D products), user-defined area-of-interest and temporal extents, zooming, panning, overlaying, sliding, and data subsetting, reformatting, and reprojection. The system will allow any user-defined portal interface (front-end) to connect to our backend server with OGC standard-compliant Web Mapping Service (WMS) and Web Coverage Service (WCS) calls. This back-end service should greatly enhance its expandability to integrate additional outside data/map sources.

Aura↗
NASA NTRS2023

Comparison Between Ozone Column Depths and Methane Lifetimes Computed By One- and Three-Dimensional Models at Different Atmospheric O(2) Levels

The calculation of ozone column depth as a function of atmospheric O2concentration has been performed multiple times over the past 40 years using one-dimensional (1-D) photochemical models. Several of these calculations [1–3] have been done by the Kasting group. We refer to the current version of that model as the‘standard’1-D model (see §2). Recently, Cooke et al., henceforth C22 ,performed a similar calculation using a three-dimensional (3-D) coupled chemistry-climate model (WACCM6). They found substantially lower ozone column depths at lower pO2levels. For example, at0.01 times the present atmospheric level (PAL) of O(2), Segura et al. calculated 124 Dobson units(DU), whereas C22 calculated a mean column depth of just 66 DU—almost a factor of two lower. This comparison is shown in figure 1. C22 argued, correctly, that 3-D models should be able to do a better job on this problem than 1-D models because ozone column depths are affected by atmospheric transport, e.g. the Brewer–Dobson circulation in the stratosphere that blows ozone from the tropics towards the poles, as well as vertical transport that carries ozone downward from the stratosphere to the troposphere. Meridional transport is absent in 1-D models, and vertical transport is fixed, so model geometry almost certainly contributes to the observed discrepancies in ozone column depth. The 3-D model also has a variable tropopause height (higher at the equator, lower towards the poles), which is difficult to simulate in one dimension.

Atmospheric chemistry↗
NASA NTRS2007

Processing AIRS Scientific Data Through Level 2

The Atmospheric Infrared Spectrometer (AIRS) Science Processing System (SPS) is a collection of computer programs, denoted product generation executives (PGEs), for processing the readings of the AIRS suite of infrared and microwave instruments orbiting the Earth aboard NASA s Aqua spacecraft. AIRS SPS at an earlier stage of development was described in "Initial Processing of Infrared Spectral Data' (NPO-35243), NASA Tech Briefs, Vol. 28, No. 11 (November 2004), page 39. To recapitulate: Starting from level 0 (representing raw AIRS data), the PGEs and their data products are denoted by alphanumeric labels (1A, 1B, and 2) that signify the successive stages of processing. The cited prior article described processing through level 1B (the level-2 PGEs were not yet operational). The level-2 PGEs, which are now operational, receive packages of level-1B geolocated radiance data products and produce such geolocated geophysical atmospheric data products such as temperature and humidity profiles. The process of computing these geophysical data products is denoted "retrieval" and is quite complex. The main steps of the process are denoted microwave-only retrieval, cloud detection and cloud clearing, regression, full retrieval, and rapid transmittance algorithm.

Oliphant, Robert↗
DOE OSTI2022

Building-Integrated Microscale Sensors for CO 2 -Level Monitoring

This research effort enabled the development of new classes of small-scale sensing systems that use mass and/or electrochemical sensors to detect the presence of carbon dioxide (CO 2 ). The concentration of this gas within the indoor environment is an important metric, as it can help enable the use of variable speed ventilation fans in commercial and governmental buildings, thus saving a significant amount of energy. Furthermore, there is a pressing need for enhanced CO 2 sensing to improve the comfort and productivity of people in commercial and government buildings, including academic spaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗