Remote sensing of the plasmasphere mass density using ground magnetometers and the FLIP model

Details Remote sensing of the plasmasphere mass density using ground magnetometers and the FLIP model Remote sensing of the plasmasphere mass density using ground magnetometers and the FLIP model

Dec 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011agufmsa43b..06z&link_type=abstract

American Geophysical Union, Fall Meeting 2011, abstract #SA43B-06

Physics

[2730] Magnetospheric Physics / Magnetosphere: Inner, [2752] Magnetospheric Physics / Mhd Waves And Instabilities, [2768] Magnetospheric Physics / Plasmasphere, [2788] Magnetospheric Physics / Magnetic Storms And Substorms

Scientific paper

The SAMBA (South American Meridional B-field Array) chain is a Southern Hemisphere meridional chain of 12 magnetometers, 11 of them at L=1.1 to L=2.5 along the coast of Chile and in the Antarctica peninsula, and one auroral station along the same meridian. SAMBA is ideal for low and mid-latitude studies of geophysical events and ULF waves. The MEASURE (Magnetometers along the Eastern Atlantic Seaboard for Undergraduate Research and Education) and McMAC (Mid-continent Magnetoseismic Chain) chains are Northern Hemisphere meridional chains in the same local time as SAMBA, but cover low to sub-auroral latitudes. SAMBA is partly conjugate to MEASURE and McMAC chains, offering unique opportunities for inter-hemispheric studies. We use 5 of the SAMBA stations and an even larger number of conjugate stations from the Northern hemisphere to determine the field line resonance (FLR) frequency of closely spaced flux tubes in the inner magnetosphere. Standard inversion techniques are used to derive the equatorial mass density of these flux tubes from the FLRs. We thus yield the mass density distribution of the plasmasphere for specific events and compare our results with results from the FLIP thermosphere-ionosphere model model. We find that for moderate activity the model determined FLR radial distribution is in excellent agreement with the observed distribution. During storm time observations indicate stronger depletion than predicted by the model initial runs.

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