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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Time lags in solar modulation

Von Rosenvinge and Paizis (1981) have discussed the amplitude variations of low energy cosmic rays, taking into account intensity-time variations which occurred starting in late 1973 and continued into mid-1975. The present investigation is concerned with the occurrence of so-called hysteresis effects during the considered period. A graph is presented which shows a comparison of the time-intensity plots for 35.3-82 MeV protons (340 MV) and the Deep River neutron monitor. A time lag can be observed between the neutron monitor intensity and the 35.3-82 MeV proton intensity. The same data is replotted to show a hysteresis loop. A plot for 35.1-82 MeV/n helium is also provided. It is found that all measured values of the time lag for protons and helium are consistent with those reported by Klecker et al. (1980). It is pointed out that the time lag for the anomalous helium is not consistent with the model of O'Gallagher (1975).

Paizis, C.↗
NASA NTRSDate not supplied

Comparing Satellite Measurements of Volcanic SO2 Mass from OMI, OMPS and TROPOMI

Sulfur dioxide (SO2) is a major air pollutant that contributes to acid rain and aerosol formation (e.g., sulfates), adversely affects the environment and human health, and explosive volcanic SO2 emissions can impact climate. The majority of SO2 emissions are related to anthropogenic processes (e.g., fossil fuel burning, metal ore smelting operations), although natural processes such as volcanic eruptions and degassing also play an important role as anthropogenic SO2 emissions continue to decline. Generally, the most interest in volcanoes occurs during major eruptions. We will focus on comparing volcanic SO2 outgassing that occurs on an almost daily basis from lesser known volcanoes using satellite data. At NASA’s Global Sulfur Dioxide Monitoring Home page (https://so2.gsfc.nasa.gov/), we have been posting daily SO2 maps from 40 volcanic and industrial regions around the world using measurements from three satellite instruments; the Ozone Monitoring Instrument (OMI) onboard NASA’s Earth Observing System Aura satellite, the Ozone Monitoring and Profiler Suite (OMPS) onboard the NASA-NOAA Suomi National Polar-orbiting Partnership (NPP) satellite, and the TROPOspheric Monitoring Instrument (TROPOMI) onboard the ESA/Copernicus Sentinel-5 Precursor satellite. These instruments in low Earth sun-synchronous polar orbits with 1:30-2:00 pm equator crossing local time provide daily SO2 maps at different spatial resolutions: 13 x 24 km2, 50 x 50 km2 and 5.5 x 3.5 km2 for OMI, OMPS and TROPOMI respectively. Data from OMI are available since October 2004 (partial coverage since 2008), from OMPS since 2012 and from TROPOMI since 2018. We will present comparative SO2 mass time-series (see Hunga-Tonga plot) and statistical analyses of recent eruptions that have data from all the instruments.

SO2↗
NASA NTRS2016

Simulation Development and Analysis of Crew Vehicle Ascent Abort

NASA's Commercial Crew Program is an integral step in its journey to Mars as it would expedite development of space technologies and open up partnership with U.S. commercial companies. NASA reviews and independent assessment of Commercial Crew Program is fundamental to its success, and being able to model a commercial crew vehicle in a simulation rather than conduct a live test would be a safer, faster, and less expensive way to assess and certify the capabilities of the vehicle. To this end, my project was to determine the feasibility of using a simulation tool named SOMBAT version 2.0 to model a multiple parachute system for Commercial Crew Program simulation. The main tasks assigned to me were to debug and test the main parachute system model, (capable of simulating one to four main parachute bodies), and to utilize a graphical program to animate the simulation results. To begin tackling the first task, I learned how to use SOMBAT by familiarizing myself with its mechanics and by understanding the methods used to tweak its various parameters and outputs. I then used this new knowledge to set up, run, and analyze many different situations within SOMBAT in order to explore the limitations of the parachute model. Some examples of parameters that I varied include the initial velocity and orientation of the falling capsule, the number of main parachutes, and the location where the parachutes were attached to the capsule. Each parameter changed would give a different output, and in some cases, would expose a bug or limitation in the model. A major bug that I discovered was the inability of the model to handle any number of parachutes other than three. I spent quite some time trying to debug the code logically, but was unable to figure it out until my mentor taught me that digital simulation limitations can occur when some approximations are mistakenly assumed for certain in a physical system. This led me to the realization that unlike in all of the programming classes I have taken thus far that focus on pure logic, simulation code focuses on mimicking the physical world with some approximation and can have inaccuracies or numerical instabilities. Learning from my mistake, I adopted new methods to analyze these different simulations. One method the student used was to numerically plot various physical parameters using MATLAB to confirm the mechanical behavior of the system in addition to comparing the data to the output from a separate simulation tool called FAST. By having full control over what was being outputted from the simulation, I could choose which parameters to change and to plot as well as how to plot them, allowing for an in depth analysis of the data. Another method of analysis was to convert the output data into a graphical animation. Unlike the numerical plots, where all of the physical components were displayed separately, this graphical display allows for a combined look at the simulation output that makes it much easier for one to see the physical behavior of the model. The process for converting SOMBAT output for EDGE graphical display had to be developed. With some guidance from other EDGE users, I developed a process and created a script that would easily allow one to display simulations graphically. Another limitation with the SOMBAT model was the inability for the capsule to have the main parachutes instantly deployed with a large angle between the air speed vector and the chutes drag vector. To explore this problem, I had to learn about different coordinate frames used in Guidance, Navigation & Control (J2000, ECEF, ENU, etc.) to describe the motion of a vehicle and about Euler angles (e.g. Roll, Pitch, Yaw) to describe the orientation of the vehicle. With a thorough explanation from my mentor about the description of each coordinate frame, as well as how to use a directional cosine matrix to transform one frame to another, I investigated the problem by simulating different capsule orientations. In the end, I was able to show that this limitation could be avoided if the capsule is initially oriented antiparallel to its velocity vector.

Wong, Chi S.↗
NASA NTRSDate not supplied

"Giovanni at 20: Consistency and Persistency in Making Earth Remote Sensing Data Available (and Useful) to the Earth Science Community"

Since its creation in the year 2000, the NASA Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) has been an exemplar of how to provide remotely-sensed satellite data and related Earth science datasets to a broad and globally diverse researcher community. One of the hallmarks of Giovanni system usage is that it is common to see practitioners of many branches of science – particularly those in fields not traditionally associated with remote-sensing data, such as animal behavior and paleooceanography – both accessing and employing datasets which the system provides. This usage pattern is attested to by the diversity of published science citing Giovanni. Giovanni has evolved through several versions, each of which increased analytical and visualization options while also enhancing ease-of-use. Information resources supporting Giovanni from the Goddard Earth Sciences Data and Information Services Center (GES DISC), which hosts the system, start with basic mapping and plotting functions and extend to “How-to” recipes demonstrating interusability with other data analysis systems and software. Giovanni is now being developed for use in a cloud environment, potentially expanding datasets the system can be applied to, and also improving performance for data having high spatial and temporal resolution. This presentation covers the system’s historical success with interesting examples of Giovanni-citing research, and will apply its current capabilities to a multi-dataset examination of derecho events associated with mesoscale convective phenomena in the summer of 2020, indicating how Giovanni is prepared for ongoing support of Earth science in a changing world.

Giovanni Derecho visualization↗
NASA NTRS1984

Computer graphic visualization of orbiter lower surface boundary-layer transition

Computer graphic techniques are applied to the processing of Shuttle Orbiter flight data in order to create a visual presentation of the extent and movement of the boundary-layer transition front over the orbiter lower surface during entry. Flight-measured surface temperature-time histories define the onset and completion of the boundary-layer transition process at any measurement location. The locus of points which define the spatial position of the boundary-layer transition front on the orbiter planform is plotted at each discrete time for which flight data are available. Displaying these images sequentially in real-time results in an animated simulation of the in-flight boundary-layer transition process.

Throckmorton, D. A.↗
NASA NTRS1992

ICE/ISEE plasma wave data analysis

The interval reported on, from Jan. 1990 to Dec. 1991, has been one of continued processing and archiving of ICE plasma wave (pw) data and transition from analysis of ISEE 3 and ICE cometary data to ICE data taken along its cruise trajectory, where coronal mass ejections are the focus of attention. We have continued to examine with great interest the last year of ISEE 3's precomet phase, when it spent considerable time far downwind from Earth, recording conditions upstream, downstream, and across the very weak, distant flank bow shock. Among other motivations was the apparent similarity of some shock and post shock structures to the signatures of the bow wave surrounding comet Giacobini-Zinner, whose ICE-phase data was revisited. While pursuing detailed, second-order scientific inquiries still pending from the late ISEE 3 recordings, we have also sought to position ourselves for study of CME's by instituting a data processing format new to the ISEE 3/ICE pw detector. Processed detector output has always been summarized and archived in 24-hour segments, with all pw channels individually plotted and stacked one above the next down in frequency, with each channel calibrated separately to keep all data patterns equally visible in the plots, regardless of gross differences in energy content at the various frequencies. Since CME's, with their preceding and following solar wind plasmas, can take more than one day to pass by the spacecraft, a more condensed synoptic view of the pw data is required to identify, let alone assess, CME characteristics than has been afforded by the traditional routines. This requirement is addressed in a major new processing initiative in the past two years. Besides our own ongoing and fresh investigations, we have cooperated, within our resources, with studies conducted extramurally by distant colleagues irrespective of the phase of the ISEE 3/ICE mission under scrutiny. The remainder of this report summarizes our processing activities, our investigations, both internal and cooperative, our scientific results, and our publication activity.

Greenstadt, E. W.↗
NASA NTRSDate not supplied

Black Hills Wildfires: Mapping Post-fire Conifer Regeneration using Snow-on Imagery

The 2000 Jasper Fire in the Black Hills of South Dakota was the largest wildfire to date in the region, burning over 83,000 acres of ponderosa pine forest. In collaboration with partners from the United States Forest Service (USFS) Black Hills Experimental Forest, USFS Rocky Mountain Research Station, and United States Geological Survey Geosciences and Environmental Change Science Center, we characterized post-fire forest regeneration within high-severity burn patches. We accomplished this by implementing novel conifer detection techniques using a snow index mask to create a winter, snow-on image composite from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data. We utilized 2015 USFS stem maps of field-observed regeneration plots and ocularly sampled additional reforestation sites planted in 2001–2013. In Google Earth Engine (GEE), the field data and imagery were used to train a Random Forest (RF) model. The RF model classified 2021 conifer regeneration density as low, medium, or high across the high-severity burn area with an overall accuracy of 81.3%. Approximately 45.9% of the high-severity burn had low or no regeneration (0-40 trees per acre) 20 years post-fire. Given our partners' desire to find easily accessible low conifer regeneration zones, we identified 4,079 acres of priority planting sites that were within 1,500 feet of roads, had not been planted previously, and were larger than 50 acres. This method supports the use of snow-on imagery as a successful technique to identify conifer regeneration.

Casey Menick​↗
NASA NTRS2022

Black Hills Wildfires: Mapping Post-Fire Conifer Regeneration using Snow-On Imagery

The 2000 Jasper Fire in the Black Hills of South Dakota was the largest wildfire to date in the region, burning over 83,000 acres of ponderosa pine forest. In collaboration with partners from the United States Forest Service (USFS) Black Hills Experimental Forest, USFS Rocky Mountain Research Station, and United States Geological Survey Geosciences and Environmental Change Science Center, we characterized post-fire forest regeneration within high-severity burn patches. We accomplished this by implementing novel conifer detection techniques using a snow index mask to create a winter, snow-on image composite from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data. We utilized 2015 USFS stem maps of field-observed regeneration plots and ocularly sampled additional reforestation sites planted in 2001–2013. In Google Earth Engine (GEE), the field data and imagery were used to train a Random Forest (RF) model. The RF model classified 2021 conifer regeneration density as low, medium, or high across the high-severity burn area with an overall accuracy of 81.3%. Approximately 45.9% of the high-severity burn had low or no regeneration (0-40 trees per acre) 20 years post-fire. Given our partners' desire to find easily accessible low conifer regeneration zones, we identified 4,079 acres of priority planting sites that were within 1,500 feet of roads, had not been planted previously, and were larger than 50 acres. This method supports the use of snow-on imagery as a successful technique to identify conifer regeneration.

Casey Menick↗
NASA NTRS1986

A novel method of calculating far-field patterns of large aperture antennas

A method is described for calculation of the radiation pattern of large aperture antennas. A piece-wise linear approximation of the aperture field using overlapping pyramidal basis functions allows the radiation pattern of an aperture antenna to be calculated as though it were a two-dimensional array. The calculation of radiation pattern data versus theta and phi, suitable for 3-D or contour plot algorithms, is achieved by locating the array in the yz-plane and performing a summation over the aperture field data sampled on a square grid. A FORTRAN subroutine is provided for performing radiation pattern calculations. Numerical results are included to demonstrate the accuracy and convergence of the method. These numerical results indicate that typical accuracies of + or - 0.1 dB for Directivity, + or - dB for the 1st Sidelobe Level, and + - 2dB for the 2nd Sidelobe Level can be obtained with an aperture grid of 45x45 points and requires approximately 0.02 seconds CPU time per far-field data point on a VAX 11/750 with a floating point accelerator.

Bailey, M. C.↗
NASA NTRS2020

AMPR CAMP2Ex Calibrated and Quality-Controlled Dataset Level 2B, Revision B

Data were acquired by the Advanced Microwave Precipitation Radiometer (AMPR) during the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) field campaign in August-October of 2019. These files include the Level 2B calibrated, corrected, and geo-referenced brightness temperature for the four AMPR-observed frequencies (10, 19, 37, 85 GHz). These data are archived in a self-describing, Climate and Forecasting (CF) 1.6-compliant Version 4 Network Common Data Format (netCDF4) format.Python software has been developed for reading, plotting, and providing some additional analysis capabilities. This software is available from: https://github.com/nasa/pyampr.The AMPR instrument is explained in more detail here: https://weather.msfc.nasa.gov/ampr/.These data have been determined to be viable for publishable scientific research, and alsoshould be useful for generating quicklooks or understanding what happened during a flight. Note: AMPRis not expected to provide useful data during significant aircraft maneuvers

Timothy Lang↗
NASA NTRS1983

The utility of calibration - Landsat 4 TM simulator data, Patrick Draw, Wyoming

A novel calibration approach was used for Landsat 4 TM simulator data acquired at the Patrick Draw test site, Wyoming, where the 'true' reflectance spectrum for any pixel area cannot be practically determined because of mixing of spectral components. An empirically derived function was first applied to the scanner data to remove the brightness gradient due to directional scattering and surface geometry. Based on the observation that the distribution of scanner brightness values (DN) were Gaussian, the minimum, mean, and maximum DN for each band were extracted and plotted versus the similar percent reflectance values determined from a large representative collection of field reflectance data for the area. These plots provided linear transformations relating DN value to reflectance in each band.

Lang, H. R.↗
NASA NTRS1972

Lunar ephemeris and selenographic coordinates of the earth and sun for 1979 and 1980

Ephemeris data are presented in sections for each month for 1979 and 1980 to provide a time history of lunar coordinates and related geometric information. A NASA Manned Spacecraft Center modification of an ephemeris tape was used to calculate and plot coordinates of the earth, moon, and sun. The ephemeris is referenced to the mean vernal equinox at the nearest beginning of a Besselian year. Therefore, the reference equinox changes from one year to the next between 30 June and 1 July. The apparent discontinuity in the data is not noticeable in the graphical presentation, but can be observed in the digital output. The mean equator of epoch is used in all cases. The computer program used to compute and plot the ephemeris data is described in the appendix.

Hartung, A. D.↗
NASA NTRS1972

Lunar ephemeris and selenographic coordinates of the earth and sun for 1977 and 1978

Ephemeris data are presented in sections for each month for 1977 and 1978 to provide a time history of lunar coordinates and related geometric information. A NASA Manned Spacecraft Center modification of an ephmeris tape was used to calculate and plot coordinates of the earth, moon, and sun. The ephemeris is referenced to the mean vernal equinox at the nearest beginning of a Besselian year. Therefore, the reference equinox changes from one year to the next between June 30 and July 1. The apparent discontinuity in the data is not noticeable in the graphical presentation, but can be observed in the digital output. The mean equator of epoch is used in all cases. The computer program used to compute and plot the ephemeris data is described in the appendix.

Hartung, A. D.↗
NASA NTRS1972

Lunar ephemeris and selenographic coordinates of the earth and sun for 1983 and 1984

Ephemeris data are presented in sections for each month for 1983 and 1984 to provide a time history of lunar coordinates and related geometric information. A NASA Manned Spacecraft Center modification of an ephemeris tape was used to calculate and plot coordinates of the earth, moon, and sun. The ephemeris is referenced to the mean vernal equinox at the nearest beginning of a Besselian year. Therefore, the reference equinox changes from one year to the next between 30 June and 1 July. The apparent discontinuity in the data is not noticeable in the graphical presentation, but can be observed in the digital output. The mean equator of epoch is used in all cases. The computer program used to compute and plot the ephemeris data is described in the appendix.

Hartung, A. D.↗
NASA NTRS2010

Orbit Determination Toolbox

The Orbit Determination Toolbox is an orbit determination (OD) analysis tool based on MATLAB and Java that provides a flexible way to do early mission analysis. The toolbox is primarily intended for advanced mission analysis such as might be performed in concept exploration, proposal, early design phase, or rapid design center environments. The emphasis is on flexibility, but it has enough fidelity to produce credible results. Insight into all flight dynamics source code is provided. MATLAB is the primary user interface and is used for piecing together measurement and dynamic models. The Java Astrodynamics Toolbox is used as an engine for things that might be slow or inefficient in MATLAB, such as high-fidelity trajectory propagation, lunar and planetary ephemeris look-ups, precession, nutation, polar motion calculations, ephemeris file parsing, and the like. The primary analysis functions are sequential filter/smoother and batch least-squares commands that incorporate Monte-Carlo data simulation, linear covariance analysis, measurement processing, and plotting capabilities at the generic level. These functions have a user interface that is based on that of the MATLAB ODE suite. To perform a specific analysis, users write MATLAB functions that implement truth and design system models. The user provides his or her models as inputs to the filter commands. The software provides a capability to publish and subscribe to a software bus that is compliant with the NASA Goddard Mission Services Evolution Center (GMSEC) standards, to exchange data with other flight dynamics tools to simplify the flight dynamics design cycle. Using the publish and subscribe approach allows for analysts in a rapid design center environment to seamlessly incorporate changes in spacecraft and mission design into navigation analysis and vice versa.

Carpenter, James R.↗
NASA NTRS1972

Lunar ephemeris and selenographic coordinates of the earth and sun for 1971 and 1972

Ephemeris data are presented for each month of 1971 and 1972 to provide a time history of lunar coordinates and related geometric information. A NASA Manned Spacecraft Center modification of the Jet Propulsion Laboratory ephemeris tape was used to calculate and plot coordinates of the earth, moon, and sun. The ephemeris is referenced to the mean vernal equinox at the nearest beginning of a Besselian year. Therefore, the reference equinox changes from one year to the next between 30 June and 1 July. The apparent discontinuity in the data is not noticeable in the graphical presentation, but can be observed in the digital output. The mean equator of epoch is used in all cases. The computer program used to compute and plot the ephemeris data is described.

Hartung, A. D.↗
NASA NTRS1972

Lunar ephemeris and selenographic coordinates of the earth and sun for 1973 and 1974

Ephemeris data are presented for each month of 1973 and 1974 to provide a time history of lunar coordinates and related geometric information. A NASA Manned Spacecraft Center modification of the Jet Propulsion Laboratory ephemeris tape was used to calculate and plot coordinates of the earth, moon, and sun. The ephemeris is referenced to the mean vernal equinox at the nearest beginning of a Besselian year. Therefore, the reference equinox changes from one year to the next between 30 June and 1 July. The apparent discontinuity in the data is not noticeable in the graphical presentation, but can be observed in the digital output. The mean equator of epoch is used in all cases. The computer program used to compute and plot the ephemeris data is described.

Hartung, A. D.↗