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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 NTRS2015

NASA'S Earth Science Data Stewardship Activities

NASA has been collecting Earth observation data for over 50 years using instruments on board satellites, aircraft and ground-based systems. With the inception of the Earth Observing System (EOS) Program in 1990, NASA established the Earth Science Data and Information System (ESDIS) Project and initiated development of the Earth Observing System Data and Information System (EOSDIS). A set of Distributed Active Archive Centers (DAACs) was established at locations based on science discipline expertise. Today, EOSDIS consists of 12 DAACs and 12 Science Investigator-led Processing Systems (SIPS), processing data from the EOS missions, as well as the Suomi National Polar Orbiting Partnership mission, and other satellite and airborne missions. The DAACs archive and distribute the vast majority of data from NASA’s Earth science missions, with data holdings exceeding 12 petabytes The data held by EOSDIS are available to all users consistent with NASA’s free and open data policy, which has been in effect since 1990. The EOSDIS archives consist of raw instrument data counts (level 0 data), as well as higher level standard products (e.g., geophysical parameters, products mapped to standard spatio-temporal grids, results of Earth system models using multi-instrument observations, and long time series of Earth System Data Records resulting from multiple satellite observations of a given type of phenomenon). EOSDIS data stewardship responsibilities include ensuring that the data and information content are reliable, of high quality, easily accessible, and usable for as long as they are considered to be of value.

metadata↗
NASA NTRS2014

Status of the NASA GMAO Observing System Simulation Experiment

An Observing System Simulation Experiment (OSSE) is a pure modeling study used when actual observations are too expensive or difficult to obtain. OSSEs are valuable tools for determining the potential impact of new observing systems on numerical weather forecasts and for evaluation of data assimilation systems (DAS). An OSSE has been developed at the NASA Global Modeling and Assimilation Office (GMAO, Errico et al 2013). The GMAO OSSE uses a 13-month integration of the European Centre for Medium- Range Weather Forecasts 2005 operational model at T511/L91 resolution for the Nature Run (NR). Synthetic observations have been updated so that they are based on real observations during the summer of 2013. The emulated observation types include AMSU-A, MHS, IASI, AIRS, and HIRS4 radiance data, GPS-RO, and conventional types including aircraft, rawinsonde, profiler, surface, and satellite winds. The synthetic satellite wind observations are colocated with the NR cloud fields, and the rawinsondes are advected during ascent using the NR wind fields. Data counts for the synthetic observations are matched as closely as possible to real data counts, as shown in Figure 2. Errors are added to the synthetic observations to emulate representativeness and instrument errors. The synthetic errors are calibrated so that the statistics of observation innovation and analysis increments in the OSSE are similar to the same statistics for assimilation of real observations, in an iterative method described by Errico et al (2013). The standard deviations of observation minus forecast (xo-H(xb)) are compared for the OSSE and real data in Figure 3. The synthetic errors include both random, uncorrelated errors, and an additional correlated error component for some observational types. Vertically correlated errors are included for conventional sounding data and GPS-RO, and channel correlated errors are introduced to AIRS and IASI (Figure 4). HIRS, AMSU-A, and MHS have a component of horizontally correlated error. The forecast model used by the GMAO OSSE is the Goddard Earth Observing System Model, Version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) DAS. The model version has been updated to v. 5.13.3, corresponding to the current operational model. Forecasts are run on a cube-sphere grid with 180 points along each edge of the cube (approximately 0.5 degree horizontal resolution) with 72 vertical levels. The DAS is cycled at 6-hour intervals, with 240 hour forecasts launched daily at 0000 UTC. Evaluation of the forecasting skill for July and August is currently underway. Prior versions of the GMAO OSSE have been found to have greater forecasting skill than real world forecasts. It is anticipated that similar forecast skill will be found in the updated OSSE.

OSSE↗
NASA NTRS1987

Development of response models for the Earth Radiation Budget Experiment (ERBE) sensors. Part 4: Preliminary nonscanner models and count conversion algorithms

Two count conversion algorithms and the associated dynamic sensor model for the M/WFOV nonscanner radiometers are defined. The sensor model provides and updates the constants necessary for the conversion algorithms, though the frequency with which these updates were needed was uncertain. This analysis therefore develops mathematical models for the conversion of irradiance at the sensor field of view (FOV) limiter into data counts, derives from this model two algorithms for the conversion of data counts to irradiance at the sensor FOV aperture and develops measurement models which account for a specific target source together with a sensor. The resulting algorithms are of the gain/offset and Kalman filter types. The gain/offset algorithm was chosen since it provided sufficient accuracy using simpler computations.

Halyo, Nesim↗
DOE OSTI2025

Detecting outbreaks using a spatial latent field

In this paper, we present a method for estimating the infection-rate of a disease as a spatial-temporal field. Our data comprises time-series case-counts of symptomatic patients in various areal units of a region. We extend an epidemiological model, originally designed for a single areal unit, to accommodate multiple units. The field estimation is framed within a Bayesian context, utilizing a parameterized Gaussian random field as a spatial prior. We apply an adaptive Markov chain Monte Carlo method to sample the posterior distribution of the model parameters condition on COVID-19 case-count data from three adjacent counties in New Mexico, USA. Our results suggest that the correlation between epidemiological dynamics in neighboring regions helps regularize estimations in areas with high variance (i.e., poor quality) data. Using the calibrated epidemic model, we forecast the infection-rate over each areal unit and develop a simple anomaly detector to signal new epidemic waves. Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.

Safta, Cosmin [Sandia National Laboratories (SNL-C↗
NASA NTRS1995

Technical report series on global modeling and data assimilation. Volume 6: A multiyear assimilation with the GEOS-1 system: Overview and results

The Data Assimilation Office (DAO) at Goddard Space Flight Center has produced a multiyear global assimilated data set with version 1 of the Goddard Earth Observing System Data Assimilation System (GEOS-1 DAS). One of the main goals of this project, in addition to benchmarking the GEOS-1 system, was to produce a research quality data set suitable for the study of short-term climate variability. The output, which is global and gridded, includes all prognostic fields and a large number of diagnostic quantities such as precipitation, latent heating, and surface fluxes. Output is provided four times daily with selected quantities available eight times per day. Information about the observations input to the GEOS-1 DAS is provided in terms of maps of spatial coverage, bar graphs of data counts, and tables of all time periods with significant data gaps. The purpose of this document is to serve as a users' guide to NASA's first multiyear assimilated data set and to provide an early look at the quality of the output. Documentation is provided on all the data archives, including sample read programs and methods of data access. Extensive comparisons are made with the corresponding operational European Center for Medium-Range Weather Forecasts analyses, as well as various in situ and satellite observations. This document is also intended to alert users of the data about potential limitations of assimilated data, in general, and the GEOS-1 data, in particular. Results are presented for the period March 1985-February 1990.

Suarez, Max J.↗
NASA NTRS2017

Mapping Application for Penguin Populations and Projected Dynamics (MAPPPD): Data and Tools for Dynamic Management and Decision Support

The Mapping Application for Penguin Populations and Projected Dynamics (MAPPPD) is a web-based, open access, decision-support tool designed to assist scientists, non-governmental organizations and policy-makers working to meet the management objectives as set forth by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) and other components of the Antarctic Treaty System (ATS) (that is, Consultative Meetings and the ATS Committee on Environmental Protection). MAPPPD was designed specifically to complement existing efforts such as the CCAMLR Ecosystem Monitoring Program (CEMP) and the ATS site guidelines for visitors. The database underlying MAPPPD includes all publicly available (published and unpublished) count data on emperor, gentoo, Adelie) and chinstrap penguins in Antarctica. Penguin population models are used to assimilate available data into estimates of abundance for each site and year.Results are easily aggregated across multiple sites to obtain abundance estimates over any user-defined area of interest. A front end web interface located at www.penguinmap.com provides free and ready access to the most recent count and modelled data, and can act as a facilitator for data transfer between scientists and Antarctic stakeholders to help inform management decisions for the continent.

Humphries, G. R. W.↗
NASA NTRS1993

The history of mare volcanism in the Orientale Basin: Mare deposit ages, compositions and morphologies

The eruptive history of mare basalts in the Orientale Basin has been studied, using Lunar Orbiter 4 high-resolution photographs, Zond 8 photographs, and recently acquired Galileo EM-1 multispectral images. This work represents a refined set of compositional data incorporating the use of a linear mixing model for mare compositions, crater count data, and a comprehensive morphologic analysis of Orientale Basin mare deposits. Evidence for multiple eruptive episodes has been found, with compositions ranging from medium- to high-Ti basalt (less than 4 to greater than 6 wt. percent TiO2). Eruptive styles included flood, rille-forming, and shield-forming eruptions. Impact crater densities of mare units in the Orientale Basin enable determination of the ages of these deposits, using the method of Neukum et al. Earliest eruptions of mare basalt in the basin occurred at greater than or equal to 3.80 Ga and the latest eruptions occurred at about 2.3-2.5 Ga. Hence, mare volcanism occurred over a period of nearly 1.5 Ga.

Kadel, S. D.↗
NASA NTRS1988

Lightning Detection

Lightning causes an estimated $50 million annually in damages to power lines, transformers and other electric utility equipment. Lightning strikes are not yet predictable, but U.S. East Coast Lightning Detection Network (LDN) is providing utilities and other clients data on lightning characteristics, flash frequency and location, and the general direction in which lightning associated storms are heading. Monitoring stations are equipped with direction finding antennas that detect lightning strikes reaching the ground by measuring fluctuations in the magnetic field. Stations relay strike information to SUNY-Albany-LDN operations center which is manned around the clock. Computers process data, count strikes, spot their locations, and note other characteristics of lightning, LDN's data is beamed to a satellite for broadcast to client's receiving stations. By utilizing real-time lightning strike information, managers are now more able to effectively manage their resources. This reduces outage time for utility customers.

Source record↗
NASA NTRS2016

Soil Moisture Active Passive (SMAP) Project Algorithm Theoretical Basis Document SMAP L1B Radiometer Data Product: L1B_TB

The purpose of the Soil Moisture Active Passive (SMAP) radiometer calibration algorithm is to convert Level 0 (L0) radiometer digital counts data into calibrated estimates of brightness temperatures referenced to the Earth's surface within the main beam. The algorithm theory in most respects is similar to what has been developed and implemented for decades for other satellite radiometers; however, SMAP includes two key features heretofore absent from most satellite borne radiometers: radio frequency interference (RFI) detection and mitigation, and measurement of the third and fourth Stokes parameters using digital correlation. The purpose of this document is to describe the SMAP radiometer and forward model, explain the SMAP calibration algorithm, including approximations, errors, and biases, provide all necessary equations for implementing the calibration algorithm and detail the RFI detection and mitigation process. Section 2 provides a summary of algorithm objectives and driving requirements. Section 3 is a description of the instrument and Section 4 covers the forward models, upon which the algorithm is based. Section 5 gives the retrieval algorithm and theory. Section 6 describes the orbit simulator, which implements the forward model and is the key for deriving antenna pattern correction coefficients and testing the overall algorithm.

microwave radiometers↗
NASA NTRS1986

Visibility related to backscatter at 1.54 micron

The lidar process was shown to have the necessary potential to fulfill the need for a remote measurement of visibility. Visibility can be inferred from a lidar return optical extinction. The wavelength 1.54 micron was chosen, being near the visible wavelength region and having a high eye safety threshol, 200,000 times higher than 1.06 micron; 1.54 is the erbium laser wavelength. This research utilized 105 measured height profiles of natural droplet size distributions data, taken in clouds, fog, and haze. These profiles were examined to determine the completeness of the droplet counting data. It was found that the particle spectrometer data were incomplete in the very light ford and haze so this portion of the data was eliminated. Utilizing the Mie theory, these droplet size distribution profiles were converted to backscatter at 1.54 micron and extinction in the visible region, 0.55 micron. Using Koschmeider's relationship, the extinction profiles were converted to visibility. The visibility and backscatter profiles were compared to develop a relationship between visibility and backscatter at 1.54 micron.

Barber, T. L.↗
NASA NTRS2021

JPSS-1 ATMS Post-launch Active Geolocation Analysis

A NOAA-20 (N20) ATMS active geolocation test was performed around Jan. 2018 with 24 pre-selected coastline crossing scenes. After comprehensive analysis of ATMS stare data and the corresponding VIIRS data, the ATMS pitch, roll and yaw pointing angle errors are found from the nadir perpendicular, the nadir oblique shallow angle, and the off-nadir perpendicular coastline crossing data, respectively. In this study, we first determine the ATMS radiometric coastline crossing time by using the ATMS radiometric count data. Since the coastline can be located anywhere within one ATMS FOV from the passive (regular scanning) geolocation data, depending on the scan starting time, it is not a valid assumption for the inflection point being the same as the coastline location. Consequently, using the passive geolocation data to validate the sensor’s on-orbit pointing angle performance is limited. After finding the ATMS radiometric coastline crossing time from the ATMS data, we compare it with the VIIRS effective time stamp (see main text). Specifically the VIIRS M3, M4 & M5 (true color) & M15 and M16 bands (thermal) data have a much smaller footprint size. Using the differences of the ATMS and VIIRS (effective) coastlines crossing times, the N20 ATMS pitch, roll and yaw pointing angle errors are found to be -0.09o, -0.24o and 0.28o, respectively. To determine ATMS geolocation properly, these on-orbit pointing errors need to be corrected, adding to the ATMS SDR Processing Coefficient Table, and passed on to the operational geolocation processing code.

Active Geolocation↗
NASA NTRS1994

Block distributions on the lunar surface: A comparison between measurements obtained from surface and orbital photography

Enlargements of Lunar-Orbiter photography were used in conjunction with a digitizing tablet to collect the locations and dimensions of blocks surrounding the Surveyor 1, 3, 6, and 7 landing sites. Data were reduced to the location and the major axis of the visible portion of each block. Shadows sometimes made it difficult to assess whether the visible major axis corresponded with the actual principal dimension. These data were then correlated with the locations of major craters in the study areas, thus subdividing the data set into blocks obviously associated with craters and those in intercrater areas. A block was arbitrarily defined to be associated with a crater when its location was within 1.1 crater radii of the crater's center. Since this study was commissioned for the ultimate purpose of determining hazards to landing spacecraft, such a definition was deemed appropriate in defining block-related hazards associated with craters. Size distributions of smaller fragments as determined from Surveyor photography were obtained as measurements from graphical data. Basic comparisons were performed through use of cumulative frequency distributions identical to those applied to studies of crater-count data.

Cintala, Mark J.↗
NASA NTRS1976

Discovery of X-ray bursts from two sources in Aquila

Two X-ray burst sources have been discovered in Aquila with the aid of the SAS 3 X-ray Observatory; MXB 1906 + 00 and Aql MXB. The present paper reports on source positions, spectra, time structure, and burst energy. Five bursts were detected from MXB 1906 + 00, and the data are shown to be consistent with the assumption that there was a burst about every 8.9 hr. A comparison of raw counting data for three bursts indicates that the bursts were detected in the energy range from 1.3 to 19 keV and that the spectrum softened during burst decay. The spectrum of the one burst observed from Aql MXB is found not to soften during burst decay. It is suggested that MXB 1906 + 00 and the previously known 'steady' source A 1905 + 00 are very likely to be the same source and that the ratio of time-averaged 'steady' luminosity to time-averaged burst luminosity would be approximately 80.

Lewin, W. H. G.↗
NASA NTRS2021

ICESat-2 Atmospheric Channel Description, Data Processing and First Results

The Advanced Topographic Laser Altimeter System (ATLAS) was launched aboard the Ice Cloud and land-Elevation Satellite-2 (ICESat-2) satellite in September 2018. ATLAS is a single wavelength (532 nm) lidar system designed to acquire high resolution measurements of the earth's surface while also obtaining atmospheric backscatter from molecules, clouds, and aerosols. Because ATLAS is optimized for altimetry, the atmospheric data acquired is unique in many respects and requires non-standard analysis techniques. For example, the high repetition rate laser limits the vertical extent of the profiles to just 14 km and causes atmospheric scattering from above 15 km to be added to the scattering in the lower 0–14 km profile. In addition, the limited vertical range of the acquired profiles renders it difficult to compute the magnitude of the solar background and hinders the application of standard calibration techniques. Despite these limitations, methods have been developed to successfully produce data products that have value to the atmospheric community for cloud and aerosol research and are currently available at the National Snow and Ice Data Center (NSIDC). In this paper we describe the ICESat-2 atmospheric channel and the methods used to process the ATLAS raw photon count data to obtain calibrated backscatter and higher level products such as layer heights and type, blowing snow, column optical depth and apparent surface reflectance.

Stephen P Palm↗
NASA NTRS1979

Radiometric resolution for monitoring vegetation: How many bits are needed?

The significance of the various number of radiometric quantizing levels required for satellite monitoring of vegetation resources was evaluated by using in situ collected spectral reflectance data, an atmospheric radiative transfer simulation model, and a satellite sensor simulation model. Reflectance data were converted to radiance data; passed through a model atmosphere to an altitude of 706 km; and subsequently quantized at 16, 32, 64, 128, 256, and 512 digital count levels for Thematic Mapper bands TM3(0.63 - 0.69 microns) and TM4(0.76 - 0.90 microns). The simulated digital count data were regressed against the in situ biological data to quantify the relationship between quantizing levels.

Tucker, C. J.↗
NASA NTRS1980

Radiometric resolution for monitoring vegetation - how many bits are needed

The number of radiometric quantizing levels required for satellite monitoring of vegetation resources was evaluated by using in situ collected spectral reflectance data, an atmospheric radiative transfer simulation model, and a satellite sensor simulation model. Reflectance data were converted to radiance data, passed through a model atmosphere to an altitude of 706 km, and subsequently quantized at 16, 32, 64, 128, 256 and 512 digital count levels for Thematic Mapper bands TM3 (0.63-0.69 microns) and TM4 (0.76-0.90 microns). The simulated digital count data were regressed against in situ biological data to quantify the relationship between quantizing levels. Results of the analysis demonstrated that solar zenith angle has an effect on the quantization equivalent change in reflectance, that 256 quantizing levels gave a 1-3% improvement per channel over 64 quantizing levels, and that 256 quantizing levels gave a 1% improvement per channel over 128 quantizing levels. No improvements were found for 256 versus 512 quantizing levels.

Tucker, C. J.↗