Cross-correlation of ionospheric parameters using machine-learning analysis of data from multiple DMSP spacecraft

Details Cross-correlation of ionospheric parameters using machine-learning analysis of data from multiple DMSP spacecraft Cross-correlation of ionospheric parameters using machine-learning analysis of data from multiple DMSP spacecraft

Dec 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011agufmsa32b..04h&link_type=abstract

American Geophysical Union, Fall Meeting 2011, abstract #SA32B-04

Computer Science

Learning

[2437] Ionosphere / Ionospheric Dynamics, [2439] Ionosphere / Ionospheric Irregularities, [2475] Ionosphere / Polar Cap Ionosphere, [2494] Ionosphere / Instruments And Techniques

Scientific paper

Many future space missions will consist of multiple spacecraft measuring the same set of geophysical parameters at different times and locations and orbits. Such multiple observations would enable us to separate spatial variations from temporal variations in the parameters, thus giving us a better understanding of the dynamics of the space environment. The plasma parameters from the various DMSP spacecraft can serve a test case for developing tools and methods for analyzing observations from multiple satellites. For this study several years of data from F13, F15, F16, and F17 were processed by a machine-learning algorithm to compare the various observed geophysical parameters. The parameters of density, temperature, composition, and ion flow velocities are compared in the polar regions where the spacecraft orbits overlap. We are undertaking this project in order to discover new correlations between these data that will allow us to expand the spatial and temporal range of our observations and improve the space environment modeling efforts.

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