Simulation of Ionospheric Response to Solar Disturbance Mechanisms

S. T. Wu, Principal Investigator
P. G. Richards and G. A. Germany, Co-Investigators

AFOSR Contract F49620-00-0-0204
Home | Proposal | Reports | Fall AGU 2000

Abstract We propose a three year study to investigate the ionospheric response to solar disturbances. One of the most important solar initiation mechanisms for geomagnetic storms are coronal mass ejections (CMEs) [Gosling et al., 1991]. Accordingly, the first step of our study is to investigate geoeffective parameters associated with interplanetary coronal mass ejections and the magnitude of their impact on the earth's ionosphere. For this task, we will use a three-dimensional, axisymmetric self-consistent MHD code to study the initiation and propagation of CMEs from the Sun to the Earth and to predict parameters such as the duration and strength of the southward turning of the interplanetary magnetic field, the shock arrival time and its strength, and the strength and duration of the solar wind ram pressure. We will then use the field line interhemispheric plasma (FLIP) model to predict the ionospheric response to the CME, using the MHD code predictions of geophysical parameters. The predicted ionospheric response will be compared to observations from ground-based measurements by ionosondes, radars, and satellite measurements of total electron content. The ionospheric investigation will include the magnitude of positive/negative ionospheric storms, the links between storm phases and changes in the thermospheric composition (atomic to molecular composition), and the nature of these composition differences.

Our goal is to investigate the feasibility of providing a self-consistent coupling between solar propagation models, which typically stop at 1 AU, and ionospheric models which assume an input function as a proxy to solar activity. Successful completion of this goal will mark the first time such a self-consistent coupling has been done. Specifically, this proposal addresses the following questions.

1. How well can we model geoeffective physical parameters such as a) the duration and strength of southward turning of the IMF at 1 AU, b) the shock arrival time and its strength, and c) the strength and duration of the ram pressure?

2. How effectively can we correlate geomagnetic storms and conventional magnetic indices with the modeled CME physical parameters?

3. How well can we model ionospheric storms based solely on the modeled CME physical parameters?

In short, this proposed program is aiming to lay the foundation for the development of a science-based prediction technology for space weather.


Plan of Work 1. Model CME Parameters
  • Test and fine tune CME code
  • Events: Jan 6-12, 1997; April 29-May 6, 1998; Space Weather Week in Sept. 1999
  • Model Inputs: EIT/LASCO observations and velocity profiles; solar magnetograph data
  • Model Comparisons: WIND solar wind observations (1 AU cloud); ISEE-3 observations (slow solar wind); Big Bear vector magnetograph (transverse field magnitude)

2. Correlate CME Parameters with Magnetic Storms

  • Test for "false alarms"
    Use Tsurutani et al. [1988] list of 46 high speed CME events that did not cause storms.
    • For each event, determine if they were definitely magnetic clouds and, if so, what conditions were present at the source at the sun.
    • Look for WIND non-cloud events to include in study.
    • Model their release, acceleration, and propagation to 1 AU.

  • Explore solar wind-ionosphere coupling
    • Look for correlations between modeled solar wind parameters and geophysical indices.
    • Correlate with Akasofu's work to develop predictors of geophysical indices.

3. Model Ionospheric Response

  • Model previous CME events using observed indices.
    • Use same events as in Task 1.
    • Model hmF2, NmF2, and Tn for each event.
    • Compare with observations of TEC from groundbased ionosondes, radars, and satellites.
    • Model magnitude of ionospheric storms, links between storm phases and changes in composition, and nature of composition differences.
  • Repeat modeling using modeled CME parameters.
  • Compare results of both studies.