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NASA NTRS1979

Planning Space Shuttle's maiden voyage

NASA's first Space Shuttle, Columbia, whose technological advances include a space laboratory, navigational and communication satellites, and planetary explorers, is examined, and the first few flights, scheduled for 1980, are described. The Shuttle employs an all-digital, all-electronic, computer-operated avionics system. The onboard data processing and software subsystem, encompassing five computers (four online and one backup), a data-bus network, bus terminals, and software, is analyzed in detail. Attention is given to the basic structure of the Orbiter (37.19 m in length and 23.77 m wingspan), its main engines, and the payload and cargo capacities (29,500 kg). A two-step program that could increase the power and duration of spaceflights is presented. The first step is the creation of a power extension package, using solar arrays, generating electricity to extend the basic five-day flight to 20 days, while the second step uses the same design to create a 25-kW power model capable of providing energy for a 50-day flight. Plans for construction of a manned space construction base and a larger power platform of 250 kW are also presented.

Malkin, M. S.↗

NASA NTRS1986

Spatial characteristics of observed precipitation fields: A catalog of summer storms in Arizona, Volume 1

Eight years of summer raingage observations are analyzed for a dense, 93 gage, network operated by the U. S. Department of Agriculture, Agricultural Research Service, in their 150 sq km Walnut Gulch catchment near Tucson, Arizona. Storms are defined by the total depths collected at each raingage during the noon to noon period for which there was depth recorded at any of the gages. For each of the resulting 428 storms, the 93 gage depths are interpolated onto a dense grid and the resulting random field is anlyzed. Presented are: storm depth isohyets at 2 mm contour intervals, first three moments of point storm depth, spatial correlation function, spatial variance function, and the spatial distribution of total rainstorm depth.

Fennessey, N. M.↗

NASA NTRS1986

Data communications and monitor for the Penn State University profiler network

The profiler network installed by the Department of Meteorology at Penn State University utilizes a microcomputer for network monitoring and control. The network consists of two VHF and one UHF wind profiling Doppler radars. Additional measurement systems added to the network include temperature and humidity profiling radiometers, sodar for boundary layer wind profiling and selected surface based baseline systems. Remote diagnostic capabilities were also implemented in the Penn State network. It is possible to remotely analyze many specific malfunctions of the transmitter or signal processor.

Peters, R. M.↗

NASA NTRS1987

FLUINT - General fluid system analysis with SINDA '85

This paper introduces FLUINT (FLUid INTegrator), a general fluid system simulation program that works in conjunction with SINDA '85 (Systems Improved Numerical Differencing Analyzer, 1985 version). FLUINT solves arbitrary networks representing internal fluid systems while SINDA '85 simultaneously solves traditional thermal networks. This transportable program is intended to provide spacecraft thermal management engineers with the analytic tools needed to simulate single- and two-phase coolant loops. The fundamental concepts for fluid system modeling are described, and methods of modeling complex, hardware-specific components are outlined. Underlying correlations and solution methods are briefly discussed. Example analyses and comparisons to test data are provided.

Cullimore, B. A.↗

NASA NTRS1988

SINDA - Systems Improved Numerical Differencing Analyzer

Capability to model fluid flow added. SINDA is software system developed to solve physical problems governed by diffusion-type equations mathematically modeled by lumped-parameter representations. Used as general thermal analyzer with resistor-and-capacitor-network representations. Adapted to wide range of problems represented by Fourier, Possion, Laplace, or other differential equations. Written in FORTRAN and Assembler.

Damico, Steve J.↗

NASA NTRS1989

Systems analysis for DSN microwave antenna holography

Proposed systems for Deep Space Network (DSN) microwave antenna holography are analyzed. Microwave holography, as applied to antennas, is a technique which utilizes the Fourier Transform relation between the complex far-field radiation pattern of an antenna and the complex aperture field distribution to provide a methodology for the analysis and evaluation of antenna performance. Resulting aperture phase and amplitude distribution data are used to precisely characterize various crucial performance parameters, including panel alignment, subreflector position, antenna aperture illumination, directivity at various frequencies, and gravity deformation. Microwave holographic analysis provides diagnostic capacity as well as being a powerful tool for evaluating antenna design specifications and their corresponding theoretical models.

Rochblatt, D. J.↗

NASA NTRS1990

SINDA '85/FLUINT, Version 2.2

SINDA, Systems Improved Numerical Differencing Analyzer, software system developed for solving physical problems governed by diffusion-type equations modeled by lumped-parameter representations. Used as general thermal analyzer with resistor-and-capacitor-network representations, adapted to wide range of problems represented by such differential equations as Fourier, Poisson, or LaPlace. Software system updated. FLUINT, FLUid INTegrator, is advanced, one-dimensional fluid-analysis program solving equations of arbitrary fluid-flow networks. Written in FORTRAN and ASSEMBLER.

Cullimore, Brent↗

NASA NTRS1991

VHF discharges in storm cells producing microbursts

An experiment was carried out in which 3-D mapping of VHF sources was compared to a 3-D description of the reflectivity and dynamics of associated cloud cells observed by a radar network. Data from 61 microbursts were analyzed and it was found that, in 93 pct. of the cases, electrical activity precedes outflow development. The results confirm that the peak in intracloud activity precedes the maximum value of the outflow.

Laroche, P.↗

NASA NTRS1993

Multiphase complete exchange: A theoretical analysis

Complete Exchange requires each of N processors to send a unique message to each of the remaining N-1 processors. For a circuit switched hypercube with N = 2(sub d) processors, the Direct and Standard algorithms for Complete Exchange are optimal for very large and very small message sizes, respectively. For intermediate sizes, a hybrid Multiphase algorithm is better. This carries out Direct exchanges on a set of subcubes whose dimensions are a partition of the integer d. The best such algorithm for a given message size m could hitherto only be found by enumerating all partitions of d. The Multiphase algorithm is analyzed assuming a high performance communication network. It is proved that only algorithms corresponding to equipartitions of d (partitions in which the maximum and minimum elements differ by at most 1) can possibly be optimal. The run times of these algorithms plotted against m form a hull of optimality. It is proved that, although there is an exponential number of partitions, (1) the number of faces on this hull is Theta(square root of d), (2) the hull can be found in theta(square root of d) time, and (3) once it has been found, the optimal algorithm for any given m can be found in Theta(log d) time. These results provide a very fast technique for minimizing communication overhead in many important applications, such as matrix transpose, Fast Fourier transform, and ADI.

Bokhari, Shahid H.↗

NASA NTRS2000

Kinematic and Moisture Environments of Convective Systems During TRMM-LBA: Preliminary Sounding Analyses

Preliminary radiosonde data are analyzed from a four station observation network that operated during TRMM-LBA. These data, which are undergoing quality control, are used to construct mean vertical profiles and time-height sections of u- and v- wind components, and also filtered time series analyses of layer mean relative humidity. Trends are identified in the humidity data which appear similar at all sites, and correlate well with multi-week changes in wind regime identified by Rickenbach et al. Higher-frequency modes of variation (3-5 day) also occur in the humidity and upper tropospheric winds and are spatially coherent among the four locations. The causes of these variations are explored, including interactions among upper tropospheric synoptic features. Finally, an attempt is made to relate the general morphology of convective systems to the vertical shear structure and thermodynamic changes that accompany contrasting wind regimes.

Halverson, J. B.↗

NASA NTRS2005

Automated Wildfire Detection Through Artificial Neural Networks

We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

Miller, Jerry↗

NASA NTRS2013

GRAIL Refinements to Lunar Seismic Structure

Weber, Renee C.↗

NASA NTRS2013

GRAIL Refinements to Lunar Seismic Structure

Joint interpretation of disparate geophysical datasets helps to reduce drawbacks that can result from analyzing them individually. The Apollo seismic network was situated on the lunar nearside surface in a roughly equilateral triangle having sides approximately 1000 km long, with stations 12/14 nearly co‐located at one corner. Due to this limited geographical extent, near‐surface ray coverage from moonquakes is low, but increases with depth. In comparison, gravity surveys and their resulting gravity anomaly maps have traditionally offered optimal resolution at crustal depths. Gravimetric maps and seismic data sets are therefore well suited to joint inversion, since the complementary information reduces inherent model ambiguity. Previous joint inversions of the Apollo seismic data (seismic phase arrival times) and Clementine‐ or Lunar Prospector‐derived gravity data (mass and moment of inertia) attempted to recover the subsurface structure of the Moon by focusing on hypothetical lunar compositions that explore the density/velocity relationship. These efforts typically search for the best fitting thermodynamically calculated velocity/density model, allowing variables like core size, velocity, and/or composition to vary freely. Seismic velocity profiles previously derived from the Apollo seismic data through inversion of travel times vary both in the depth of the crust and mantle layers, and the seismic velocities and densities assigned to those layers. The lunar mass and moment of inertia likewise only constrain gross variations in the density profile beyond that of a uniform density sphere. As a result, composition and structure models previously obtained by jointly inverting these data retain the original uncertainties inherent in the input data sets. We will perform a joint inversion of Apollo seismic delay times and gravity data collected by the GRAIL lunar gravity mission, in order to recover seismic velocities and density as a function of latitude, longitude, and depth within the Moon. We will relate density to seismic velocity using a linear relationship that is allowed to be depth‐dependent. The corresponding coefficient (B) can reflect a variety of material properties that vary with depth, including temperature and composition. The inversion seeks to recover the set of density, velocity, and B‐coefficient perturbations that minimize (in a least‐squares sense) the difference between the observed and calculated data.

Weber, Renee C.↗

NASA NTRS2014

Grail Refinements to Lunar Seismic Structure

To probe a planet's interior, seismology provides the most direct constraints on the variables that govern the dynamic properties of the body. However, the GRAIL (Gravity Recovery and Interior Laboratory) mission's high-resolution measurements of the lunar gravity field provide constraints on crustal thickness, mantle structure, core radius and stratification, and core state (solid vs. molten). These data complement seismic investigations, and joint interpretation permits improved constraints on the Moon's internal structure. Joint interpretation of disparate geophysical datasets helps reduce drawbacks that can result from analyzing them individually. The Apollo seismic network was situated on the lunar nearside surface in a roughly equilateral triangle having sides approximately 1000 km long, with stations 12/14 nearly co-located at one corner. Due to this limited geographical extent, near-surface ray coverage from moonquakes is low, but increase with depth. In comparison, gravity surveys and their resulting gravity anomaly maps have traditionally offered optimal resolution at crustal depths. Gravimetric maps and seismic data sets are therefor well suited to joint inversion, since the complementary information reduces inherent model ambiguity. We will perform a joint inversion of Apollo seismic delay times and gravity data collected by GRAIL lunar gravity mission, in order to recover seismic velocity and density as a function of latitude, longitude and depth within the Moon. We will relate density (rho) to seismic velocity (v) using a linear relationship that is allowed to be depth-dependent. The corresponding coefficient (B) can reflect a variety of material properties that vary with depth, including temperature and composition. The inversion seeks to recover the set of rho, v, and B perturbations that minimize (in a least-squares sense) the difference between the observed and calculated data.

Weber, Renee C.↗

NASA NTRS2014

GRAIL Refinements to Lunar Seismic Structure

Joint interpretation of disparate geophysical datasets helps reduce drawbacks that can result from analyzing them individually. The Apollo seismic network was situated on the lunar nearside surface in a roughly equilateral triangle having sides approximately 1000 km long, with stations 12/14 nearly co-located at one corner. Due to this limited geographical extent, near-surface ray coverage from moonquakes is low, but increases with depth. In comparison, gravity surveys and their resulting gravity anomaly maps have traditionally offered optimal resolution at crustal depths. Gravimetric maps and seismic data sets are therefore well suited to joint inversion, since the complementary information reduces inherent model ambiguity. Previous joint inversions of the Apollo seismic data (seismic phase arrival times) and Clementine- or Lunar Prospector-derived gravity data (mass and moment of inertia) attempted to recover the subsurface structure of the Moon by focusing on hypothetical lunar compositions that explored the density/velocity relationship. These efforts typically searched for the best fitting thermodynamically calculated velocity/density model, and allowed variables like core size, velocity, and/or composition to vary freely. Seismic velocity profiles derived from the Apollo seismic data through travel time inversion vary both in the depth of the crust and mantle layers, and the seismic velocities and densities assigned to those layers. The lunar mass and moment of inertia likewise only constrain gross variations in the density profile beyond that of a uniform density sphere. As a result, composition and structure models previously obtained by jointly inverting these data retain the original uncertainties inherent in the input data sets. We perform a joint inversion of Apollo seismic delay times and gravity data collected by the GRAIL lunar gravity mission, in order to recover seismic velocity and density as a function of latitude, longitude, and depth within the Moon. We relate density (p) to seismic velocity (v) using a depth-dependent linear relationship. The corresponding coefficient (B) can reflect a variety of material properties, including temperature and composition. The inversion seeks to recover the set of p, v, and B perturbations that minimize (in a least-squares sense) the difference between the observed and calculated data.

Weber, Renee C.↗

NASA NTRS2016

Retrieval of Daily Maps of PM2.5 Aerosol in the Problematic California Valleys: Bright, Speckled Reflectances, Thin AOT, but High Pollution

The San Joaquin Valley suffers from severe episodes of respirable aerosol (PM2.5) in wintertime.We provide maps of aerosol episodes using daily snapshots of PM2.5 and its changing features despite numerous difficulties inherent to sampling the region. Linear relationships relating aerosol optical thickness (AOT) to PM2.5 give an explained variance of approximately 3.The GEO-CAPE mission has as a goal the provision of relevant measures of respirable aerosol to the community,but has not formulated a science goal beyond the limited goal of retrieval of AOT, bringing the usefulness of GEO-CAPE into doubt.Our special focus was on the DISCOVER-AQ period, Jan-Feb 2013, which had many supporting measurements.Both high pollution and retrieval difficulties tend to occur in many Mediterranean agricultural regions like the San Joauin. One difficulty is the relatively bright surfaces with considerable exposed soil. NASAs MAIAC and MODIS Deep Blue retrieval techniques are shown to have considerable skill even at low aerosol optical thickness (AOT) values, as evaluated by concurrent AERONET sunphotometer measurements.More significantly, these AOT values can correspond to high daytime PM2.5 since aerosol mixed layer depth is thin and variable, 200m 600 m. The thin layers derive from typical subsidence of dry air between more stormy periods. This situation provides an advantage: water vapor column is also almost completely limited to a similar mixed layer depth, and can thus serve as a measure of aerosol dilution.Using the MAIAC Water Vapor Column:In order to make the maps below, we used the MAIAC data but subtracted partial water-vapor columns estimated from MERRA Reanalysis Data availabe from the GSFC GMAO using kriging. We did not use the mixed-layer estimates from MERRA, since such analyses were found problematic during our forecasting exercises for DISCOVER-AQ. Observations from the aircraft soundings suggested that this overlying moisture was mostly due to larger scale flows, not ML venting.However, the specific humidity at the surface and a nearly well-mixed ML was analyzed by kriging from the surface network (MesoWest, University of Utah). These were thought to be truer, uninfluenced by physical process modeling that combines with data observations. (TBD: How different are they?) Procedure: Subtract overlying partial water columns from MAIAC column water and divide this by a surface value of water vapor. (MAIAC column is expressed in cm of water, i.e., water vapor at surface conditions.This method appears to bring out useful details in the distribution of submicron particles in the very problematic Wintertime San Joaquin Valley, and allow analysis of pollution episodes throughout the valley, rather than long-term averages.

san joaquin valley↗

NASA NTRS2019

The ILRS: Approaching 20 Years and Planning for the Future

The International Laser Ranging Service (ILRS) was established by the International Association of Geodesy (IAG) in 1998 to support programs in geodesy, geophysics, fundamental constants and lunar research, and to provide the International Earth Rotation Service with data products that are essential to the maintenance and improvement in the International Terrestrial Reference Frame (ITRF), the basis for metric measurements of changes in the Earth and Earth–Moon system. Other scientific products derived from laser ranging include precise geocentric positions and motions of ground stations, satellite orbits, components of Earth’s gravity field and their temporal variations, Earth Orientation Parameters, precise lunar ephemerides and information about the internal structure of the Moon. Laser ranging systems are already measuring the one-way distance to remote optical receivers in space and are performing very accurate time transfer between remote sites in the Earth and in Space. The ILRS works closely with the IAG’s Global Geodetic Observing System. The ILRS develops (1) the standards and specifications necessary for product consistency, and (2) the priorities and tracking strategies required to maximize network efficiency. The service collects, merges, analyzes, archives and distributes satellite and lunar laser ranging data to satisfy a variety of scientific, engineering, and operational needs and encourages the application of new technologies to enhance the quality, quantity, and cost effectiveness of its data products. The ILRS works with (1) new satellite missions in the design and building of retroreflector targets to maximize data quality and quantity, and (2) science programs to optimize scientific data yield. Since its inception, the ILRS has grown to include forty laser ranging stations distributed around the world. The ILRS stations track more than ninety satellites from low Earth orbit (LEO) to the geosynchronous orbit altitude as well as retroreflector arrays on the surface of the Moon. Applications have been expanded to include time transfer, asynchronous ranging for targets at extended ranges, free space quantum telecommunications, and the tracking of space debris. Laser ranging technology is moving to lower energy, higher repetition rates (kHz), single-photon-sensitive detectors, shorter pulse widths, shorter normal point intervals for faster data acquisition, and increased pass interleaving, automated to autonomous operation with remote access, and embedded software for real-time updates and decision making. An example of pass interleaving is presented for the Yarragadee station (see Fig. 4); tracking of LEO satellites is often accommodated during break in LEO and GNSS passes. New satellites arrays provide more compact targets and work continues on the development of lighter less expensive arrays for satellites and the moon. The service now provides operational ITRF products including daily/ weekly station positions and daily resolution Earth orientation products; the flow of weekly combination of satellite orbit files for LAGEOS/Etalon-1 and -2 has recently been established. New products are under testing through a pilot project on systematic error monitoring currently underway. The article will give an overview of activities underway within the service, paths forward presently envisioned, and current issues and challenges.

Laser retroreflectors↗

NASA NTRS2019

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency's (ESA's) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration's (NASA's) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from theArgo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS.We also identify the impact of the corrections for atmospheric effectsrecently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

SSS↗