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Research projects on surface and plasma physics, computer programming, information processing, superconductivity, ionospheric data, network synthesis and related fields
The Space-plasma Computer Analysis Network (SCAN) currently connects a large number of U.S. Spacelab investigators into a common computer network. Used primarily by plasma physics researchers at present, SCAN provides access to Spacelab investigators in other areas of space science, to Spacelab and non-Spacelab correlative data bases, and to large Class VI computational facilities for modeling. SCAN links computers together at remote institutions used by space researchers, utilizing commercially available software for computer-to-computer communications. Started by the NASA's Office of Space Science in mid 1980, SCAN presently contains ten system nodes located at major universities and space research laboratories, with fourteen new nodes projected for the near future. The Stanford University computer gateways allow SCAN users to connect onto the ARPANET and TELENET overseas networks.
The field of space physics has a long history of utilizing dimensionality reduction methods to distill data, including but not limited to spherical harmonics, the Fourier Transform, and the wavelet transform. Here, we present a technique for performing dimensionality reduction on ion counts distributions from the Multiscale Mission/Fast Plasma Investigation (MMS/FPI) instrument using a data-adaptive method powered by neural networks. This has applications to both feeding low-dimensional parameterizations of the counts distributions into other machine learning algorithms, and the problem of data compression to reduce transmission volume for space missions. The algorithm presented here is lossy, and in this work, we present the technique of validating the reconstruction performance with calculated plasma moments under the argument that preserving the moments also preserves fluid-level physics, and in turn a degree of scientific validity. The method presented here is an improvement over other lossy compressions in loss-tolerant scenarios like the Multiscale Mission/Fast Plasma Investigation Fast Survey or in non-research space weather applications.
The on-line interactive systems of the National Space Science Data Center (NSSDC) are examined. The worldwide computer network connections that allow access to NSSDC users are outlined. The services offered by the NSSDC new technology on-line systems are presented, including the IUE request system, ozone TOMS data, and data sets on astrophysics, atmospheric science, land sciences, and space plasma physics. Plans for future increases in the NSSDC data holdings are considered.
The on-line interactive systems of the National Space Science Data Center (NSSDC) are examined. The worldwide computer network connections that allow access to NSSDC users are outlined. The services offered by the NSSDC new technology on-line systems are presented, including the IUE request system, Total Ozone Mapping Spectrometer (TOMS) data, and data sets on astrophysics, atmospheric science, land sciences, and space plasma physics. Plans for future increases in the NSSDC data holdings are considered.
In this work, we investigate the use of deep learn-ing techniques as surrogate models, to enhance the estimationof effects of subgrid turbulent transport for 3D radiatuve hy-drodynamic simulations of the quiet Sun. We develop two dis-tinct 3D Convolutional Neural Networks (3DCNNs) to capturespatio-temporal dependencies in 3D velocity fields, leveragingdifferent activation functions and architectural designs. Thesemodels integrate both averaged velocity vector components andscalar features such as plasma density to enhance predictionaccuracy. Additionally, a Multilayer Perceptron (MLP) modelis employed to approximate complex nonlinear relationships,offering a comparison in performance between convolutionaland fully connected architectures. Logarithmic transformationis applied to the targets to handle heavily skewed data, im-proving model performance. All models are compared againsta physics-based Gradient Model. Results show that the 3DCNNmodels excel at approximating Reynolds stress tensors, makingthem a candidate for assisting in producing reduced resolutionsimulations, and thereby reducing computational overheadwhile maintaining higher accuracy than the baseline. Thesefindings demonstrate the potential of deep learning, particu-larly CNNs, to advance scalable and accurate simulations ofsolar dynamics, offering a promising alternative to traditionalturbulence models.
The Geotail spacecraft will be provided by the Institute of Space and Astronautical Science (ISAS) and will provide a Delta Launch Vehicle, tracking support by the Deep Space Network (DSN), and data processing support by GSFC. In exchange, ISAS will reserve part of the payload for NASA instruments together with a certain number of investigators from the United States. As the solar wind flows toward the Earth, some of the energy is modified by the Earth's magnetosphere, ionosphere, and upper atmosphere. This interaction causes the flow to be altered, creating a plasmasphere, plasma sheet, and ring currents in the Earth's Geomagnetic Tail region. The result is a series of distinct regions which affect processes on the Earth. By traversing the tail region to a variety of depths, Geotail will be able to determine the size, position, and other properties of these regions. When correlated with information obtained from the other ISAS spacecraft, Geotail data should help to provide a more complete understanding of how the solar processes affect the Earth's environment. The flight profile is given, and information is presented in tabular form on the following topics: DSN support, frequency assignments, telemetry, command, and tracking support responsibility.
The Apollo 15 lunar-surface magnetometer (LSM) is one of a network of magnetometers that have been deployed on the moon to study intrinsic remanent magnetic fields and global magnetic response of the moon to large-scale solar and terrestrial magnetic fields. From these field measurements, properties of the lunar interior such as magnetic permeability, electrical conductivity, and temperature can be calculated. In addition, correlation with solar-wind-spectrometer data allows study of the the solar-wind plasma interaction with the moon and, in turn, investigation of the resulting absorption of gases and accretion of an ionosphere. These physical parameters and processes determined from magnetometer measurements must be accounted for by comprehensive theories of origin and evolution of the moon and solar system.
The existing uncertainties about density profiles in the topside ionosphere, i.e., in the height regime from hmF2 to approx. 2000 km, requires the search for new data sources. Millions of ionograms had been recorded by the ISIS and Alouette satellites in the sixties and seventies, that never were analyzed in terms of electron density profiles. In recent years an effort started to digitize the analog recordings to prepare the ionograms for computerized analysis. This paper shows how the digital ionograms are processed and the electron density profiles (from satellite orbit altitude, 1400 km for ISIS-2, down to the F peak) are calculated. The most difficult part of the task is the automatic scaling of the echo traces in the ISIS ionograms. Unlike the ionograms from modern ionosondes, the ISIS ionograms do not identify the wave polarization of the different echo traces, so physical logic must be applied to identify the ordinary ()) and extraordinary (X) traces, and this is not always successful. Characteristic resonance features seen in the topside ionograms occur at the gyro and plasma frequencies. An elaborate scheme was developed to identify these resonance frequencies in order to determine the local plasma and gyrofrequencies. This information helps in the identification of the O and X traces, and it provides the starting density of the electron density profile. The inversion of the echo traces into electron density profiles uses the same modified Chebyshev polynomial fitting technique that is successfully applied in the ground-based Digisonde network. The automatic topside ionogram scaler with true height algorithm TOPIST is successfully scaling approx. 70% of the ionograms. An 'editing process' is available to manually scale the more difficult ionograms. The home page for the ISIS project is at http://nssdc.gsfc.nasa.gov/space/isis/isis-status.html. It provides access to as of January 2001, 3000,000 digitized ISIS ionogram data and to related software. A search page lets users select data location, time, and a host of other search criteria. The automated processing of the ISIS ionograms will begin later this year and the electron density profiles will be made available from the project home page. The ISIS data restoration efforts are supported through NASA's Applied Systems and Information Research Program.
We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC-derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML-AIM inputs are 60-min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML-AIM produces physically accurate ionospheric potential patterns such as the two-cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML-AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data-assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML-AIM than others. ML-AIM is unique and innovative because it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi-static ionospheric condition under quasi-steady solar wind/IMF conditions. Plans are underway to improve ML-AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.
A global network of ground-based observatories for the study of Halley's Comet in 1985/1986 is discussed. Recommendations are made with respect to improving coordination between reporting observatories, in order to ensure detailed imaging of such fast-generating cometary phenomena as plasma-tail knots, helices, disconnected tails, rays and condensations. A method for calibrating telescopes is considered by which well-studied objects will be photographed to provide references for images of Halley's Comet. This procedure is expected to reduce errors to approximately 0.05 mag. A coordinated study of Halley's Comet will provide important data on the physical properties of the Comet. Examples of the topics of study related to the plasma physics of the Comet's tail include: magnetic reconnection, rippling and tearing modes, kink instability, Kelvin-Helmholtz instability, and the flute instability.
The ESA Venus Express spacecraft was observed at X-band with the Wettzell radio telescope in October-December 2009 in the framework of an assessment study of the possible contribution of the European VLBI Network to the upcoming ESA deep space missions. A major goal of these observations was to develop and test the scheduling, data capture, transfer, processing, and analysis pipeline. Recorded data were transferred from Wettzell to Metsahovi for processing, and the processed data were sent from Mets ahovi to JIVE for analysis. A turnover time of 24 hours from observations to analysis results was achieved. The high dynamic range of the detections allowed us to achieve a milliHz level of spectral resolution accuracy and to extract the phase of the spacecraft signal carrier line. Several physical parameters can be determined from these observational results with more observational data collected. Among other important results, the measured phase fluctuations of the carrier line at different time scales can be used to determine the influence of the solar wind plasma density fluctuations on the accuracy of the astrometric VLBI observations.
The rate of TEC index (ROTI) is a measurement that characterizes ionospheric irregularities. It can be obtained from standard GNSS dual-frequency phase data collected using a geodetic type of GNSS receiver. By processing GPS data from ground-based networks of International GNSS Service and Continuously Operating Reference Station (CORS), ROTI maps have been produced to observe global and regional scintillation activities. A major mid-latitude scintillation event in the contiguous United States is reported here that was captured in ROTI maps produced using CORS GPS data collected during a space weather storm. The analyses conducted in this work and previously by another group indicate that ROTI is a good occurrence indicator of both amplitude and phase scintillations of GPS L-band signals, even though the magnitudes of ROTI, S4, and sigma(sub phi) can be different. For example, our analysis indicates that prominent ROTI and the L1 phase scintillation (sigma(sub phi)) are well correlated temporally in the polar region while L1 amplitude scintillation rarely occurs. The differences are partially attributed to physics processes in different latitude regions, such as high-speed plasma convection in the polar region that can suppress the amplitude scintillation. An analysis of the impact of ionospheric scintillation on precise positioning, which requires use of dual-frequency phase data, is also conducted. The results indicate that significant (more than an order of magnitude) positioning errors can occur under phase scintillation conditions.
The heliosphere is pervaded by interplanetary energetic particles, traditionally also called cosmic rays, from solar, internal heliospheric, and galactic sources. The particles species of interest to heliophysics extend from plasma energies to the GeV energies of galactic cosmic rays still measurably affected by heliospheric modulation and the still higher energies contributing to atmospheric ionization. The NASA and international Heliospheric Network of operational and legacy spacecraft measures interplanetary fluxes of these particles. Spatial coverage extends from the inner heliosphere and geospace to the heliosheath boundary region now being traversed by Voyager 1 and soon by Voyager 2. Science objectives include investigation of solar flare and coronal mass ejection events, acceleration and transport of interplanetary particles within the inner heliosphere, cosmic ray interactions with planetary surfaces and atmospheres, sources of suprathermal and anomalous cosmic ray ions in the outer heliosphere, and solar cycle modulation of galactic cosmic rays. The Virtual Energetic Particle Observatory (VEPO) will improve access and usability of selected spacecraft and sub-orbital NASA heliospheric energetic particle data sets as a newly approved effort within the evolving heliophysics virtual observatory environment. In this presentation, we will describe current VEPO science requirements, our initial priorities and an overview of our strategy to implement VEPO rapidly and at minimal cost by working within the high-level framework of the Virtual Heliospheric Observatory (VHO). VEPO will also leverage existing data services of NASA's Space Physics Data Facility and other existing capabilities of the U.S. and international heliospheric research communities.