The IMPTAM, version for electrons (Ganushkina et al., 2013, 2014, 2015, 2019), originally developed for ions (Ganushkina et al., 2001, 2005, 2006), traces distributions of electrons in the drift approximation (first and second adiabatic invariants conserved) with arbitrary pitch angles from the plasma sheet (starting at 10 RE) to the inner L shell regions (2-3 RE) with energies reaching up to hundreds of keVs in time-dependent magnetic and electric fields. Liouville's theorem is used to gain information on the entire distribution function with losses taken into account. For electron losses, convection outflow and pitch angle diffusion are considered. Instead of using the pitch angle diffusion coefficients directly, the parameterizations of the electron lifetimes due to interactions with chorus and hiss waves obtained by Orlova and Shprits  and Orlova et al. [2014, 2016] with plasmapause location by Carpenter and Anderson  are incorporated. For the obtained distribution function, radial diffusion is applied by solving the radial diffusion equation (Schulz & Lanzerotti, 1974). Kp-dependent radial diffusion coefficients DLL for the magnetic field fluctuations are computed following Brautigam and Albert (2000). After that, the order of calculation is repeated: First, solve transport with losses and then apply the diffusion. Inside IMPTAM, the set of models is (1) a dipole model for the internal magnetic field, (2) T96 model Tsyganenko (1995) for the external magnetic field, and (3) Boyle et al. (1997) polar cap potential mapped to the magnetosphere. We set the model boundary at 10 RE and use the kappa electron distribution function. Parameters of the kappa distribution function are the number density n and temperature T in the plasma sheet given by the empirical model derived from THEMIS data by Dubyagin et al. (2016).
Requires conservation of first and second adiabatic invariants.
Details can be found here http://citrine.engin.umich.edu/imptam/
IMF and solar wind parameters, geomagnetic indices (Kp, Dst, AL)
Electron fluxes in the energy range from 1 to 300 keV everywhere in 3D inner magnetosphere at distances from 2 to 10 RE, with specific output at GEO, GTO and MEO orbits.
Model is time-dependent.
- Magnetosphere / Global Magnetosphere
- Magnetosphere / Inner Magnetosphere / RingCurrent
Space Weather Impacts
- Near-earth radiation and plasma environment (aerospace assets functionality)
- Geomagnetic Storms
- Geomagnetic Sub-storms
- Plasma Sheet
- Particle Dynamics
- Inner Magnetosphere and Outer Magnetosphere / Tail Coupling
- Ganushkina, N. Y., T. I. Pulkkinen, V. F. Bashkirov, D. N. Baker and X. Li (2001), Formation of intense nose structures, GRL,
- Ganushkina, N. Yu., T. I. Pulkkinen, T. Fritz, Role of substorm-associated impulsive electric fields in the ring current development during storms, Annales Geophysicae, 23, 579-591, 2005.
- Ganushkina, N. Y.; Pulkkinen, T. I.; Milillo, A.; Liemohn, M. Evolution of the proton ring current energy distribution during 21-25 April 2001 storm, J. Geophys. Res., Vol. 111, No. A11, A11S08, 10.1029/2006JA011609, 2006.
- Ganushkina, N. Y., O. A. Amariutei, Y. Y. Shprits, and M. W. Liemohn, Transport of the plasma sheet electrons to the geostationary distances, J. Geophys. Res.: Space Physics, 118, doi:10.1029/2012JA017923, 2013.
- Ganushkina, N. Y., M. W. Liemohn, O. A. Amariutei, and D. Pitchford, Low-energy electrons (5-50 keV) in the inner magnetosphere, J. Geophys. Res. Space Physics, 119, doi:10.1002/2013JA019304, 2014.
- Ganushkina, N. Y., O. A. Amariutei, D. Welling, and D. Heynderickx, Nowcast model for low-energy electrons in the inner magnetosphere, Space Weather, 13, doi:10.1002/2014SW001098, 2015.
- Ganushkina, N. Y., Sillanpää, I., Welling, D. T, Haiducek, J. D, Liemohn, M. W., Dubyagin, S., and Rodriguez, J. V., Validation of Inner Magnetosphere Particle Transport and Acceleration Model (IMPTAM) with long‐term GOES MAGED measurements of keV electron fluxes at geostationary orbit, Space Weather, 17, https://doi.org/10.1029/2018SW002028, 2019
Code Languages: C++ and FORTRAN, Python
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