Erosion and refilling of the plasmasphere during a geomagnetic storm modeled by a neural network
Abstract
We present a history‐dependent model of the equatorial plasma density of the inner magnetosphere using a feedforward neural network with two hidden layers. As the model inputs, we take locations and time series of SYM‐H, AL, and F10.7 indices. By considering not only the instantaneous values but also the past values of geomagnetic and solar indices, the model is history dependent on levels of geomagnetic and solar activity. The modeled electron density is continuous both spatially and temporally so that the evolution of the density can be studied (such as plasmaspheric refilling). The model is trained using the electron density inferred from the spacecraft potential from three THEMIS probes. The equatorial electron density is shown to be accurately reconstructed with a correlation coefficient of r ~ 0.953 between data and model target. Since the model is history dependent, it succeeds in reconstructing various density features and dynamic behaviors, such as the quiet time plasmasphere, erosion and recovery of the plasmasphere, as well as the plume formation during a storm on 4 February 2011. Our model may provide unprecedented insight into the behavior of the equatorial density at any time and location; as an example we show the inferred refilling rate from our model and compare it to previous estimates.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 01, 2017
- Source ID
- 10.1002/2017ja023948
Entities
People
- Jacob Bortnik
- Qianli Ma
- Richard Thorne
- V. Angelopoulos
- Wen Li
- Xiangning Chu
Organizations
- Air Force Office of Scientific Research
- Boston University
- National Aeronautics and Space Administration
- National Science Foundation
- University of California, Los Angeles