Modeling the Plasma Mass Density of the Inner Magnetosphere From EMMA Magnetometer Network Observations
Abstract
Plasma mass density distribution in the inner magnetosphere is very sensitive to geomagnetic activity variations and its dynamics strongly influences the wave excitation and wave-particle interaction. A few attempts to model plasma mass density have been made so far but a complete model which is both local time and geomagnetic activity dependent is still missing.
We present a local time dependent empirical model of the equatorial plasma mass density derived from field line resonance observations at the European quasi-Meridional Magnetometer Array (EMMA). Models of the plasmasphere, plasmatrough and plasmapause are derived separately and then combined. The whole model is limited to the local time (LT) sector 06:00-18:00 and to the range of equatorial distances 2.3-8 RE . At this stage, the dependence on the geomagnetic activity is considered only to determine the plasmapause position and is parameterized by the Kp index. It well describes the recovery phase following a plasmasphere erosion but is still not able to reproduce highly dynamical phases or transient structures like plumes or notches. The plasmasphere model is limited to the range 2.3-4.5 RE and predicts a mass density increase in the daytime sector (10-18 LT) which becomes more evident with increasing L. A comparison with previous models of plasmaspheric electron density suggests that the diurnal variation is mainly contributed by the heavy ions. The plasmatrough model is limited to the range 3.8-8 RE and predicts a diurnal variation even more pronounced. Comparison with electron density models suggests that the average ion mass density can increase from ∼2.0 to ∼4-5 amu during daytime hours. In the next future, when a larger measurements database will be available, we plan to include in the model EUV solar flux and annual dependences, and more advanced parameterizations of the geomagnetic activity as well.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMSM13F3371D
- Keywords:
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- 1910 Data assimilation;
- integration and fusion;
- INFORMATICS;
- 1942 Machine learning;
- INFORMATICS;
- 2753 Numerical modeling;
- MAGNETOSPHERIC PHYSICS;
- 7924 Forecasting;
- SPACE WEATHER