Model Mapping of the Inner Magnetosphere During Major Space Storms

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Model Mapping of the Inner Magnetosphere During
Major Space Storms

• Nikolai Tsyganenko
• USRA, GSFC

Presented at the
Chapman Conference on the Physics and Modeling of
the Inner Magnetosphere Helsinki,
Finland August 25-29, 2003
with special thanks to Howard Singer Justin
Kasper
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Outline

• Motivation and outstanding problems
• Recent advances modeling the storm-time inner
  magnetosphere JGR, v.108(A5), 2003
• Whats next ? A new approach to parameterizing
  the models, based on empirically derived response
  functions and decay timescales.
• Summary

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Motivation and Outstanding Problems

• Major storms are very interesting but rare
  events, so that stormy periods with Dstlt-100
  correspond to only a few percent of all available
  data.
• ? Existing empirical models (T89, T96, T02) can
  faithfully represent only quiet and moderately
  disturbed magnetosphere. Using them for
  storm-time periods is a questionable
  extrapolation.
• Existing models approximate the strengths of the
  model field sources as linear functions of the
  solar wind/IMF input, which is unlikely the case
  in major events.
• Delayed response and inertia effects either
  ignored (T96) or replicated by sliding
  averaging over preceding hourly intervals (T02).
  In reality, different parts of the
  magnetosphere have largely different relaxation
  timescales.

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Modeling storm-time inner magnetosphere
JGR, v.108(A5), paper SMP-18, 2003
Data set

• Covers 37 events with -340ltDstlt-65, from Oct 22,
  1996 to Nov 8, 2000
• Most magnetospheric B data came from Polar,
  GOES-8,-9,-10, Geotail
• Each event fully covered by solar wind IMF data
• 
  

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Events included in the set Example 1
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Events included in the set Example 2
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Elements of the storm-time field model

• Approximations for all individual field sources
  similar to those in the
• 2002 model JGR, v.107, two companion
  papers in A8 issue
• 1. Symmetrical and partial ring currents
  (scalable, shielded, based
• on particle pressure models JGR,
  v.105(A12), 2000).
• 2. Cross-tail current (two modules,
  innermost and a more distant
• one, shielded, movable, include a
  tilt-related deformation).
• 3. Region 1/2 Birkeland currents
  (shielded, scalable, each system
• includes two Fourier terms in the LT
  variation.
• 4. Magnetopause, with the shape/size
  based on Shue et al. 1998
• (but driven only by the solar wind
  pressure in this model).
• 5. Interconnection field, proportional to
  interplanetary By and Bz,
• and replicating the IMF penetration
  in the magnetosphere.
• Parameterization somewhat different from what
  we used before

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Storm-time model Parameterizing the input

• Cross-tail current magnitude

where P is the solar wind ram pressure,
and

• Region 1 2 Birkeland currents and the partial
  ring current

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Model mapping for the 04/06/2000 storm
Main phase max Dst -320
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Model mapping for the 03/31/2001 storm
Main phase max Dst -410
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Whats next ?

• Replace the Dst index by a set of
  solar-wind/IMF-driven parameters, separately
  fitted to each source of model B

• - Even though the Dst field is a fair
  indicator of average disturbance level,
• it is unacceptable for accurate
  parameterization, because
• (a) it mixes the fields
  from different sources,
• (b) at different phases of
  a storm, contributions of these sources
• to the Dst are
  dramatically different,
• (c) Dst is not available in
  real time ? not suitable for predicting
• the space weather.
• In the real world, each storm has its own unique
  features. Their accurate replication is possible
  only by taking into account the entire history of
  the external driving, in some cases, extending
  far back into the initial quiet period Thomsen
  et al., 2003.
• The essence of our effort transition from a
  synoptic description of an average magnetosphere
  to the modeling of the storm process in its
  entirety.

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Outline of the approach

• Basic assumption each individual field source
  (e.g., ring, tail, Birke-land currents) evolves
  in time according to the equation (used by
  Bur-ton et al., 1975, for predicting Dst)

whose solution is
and the source function F can be
sought, for example, in the form

• The decay rate T and the quantities a, b, g, d
  are different for each current system. They
  can be treated as unknown model parameters to
  be derived from the data.

• Full coverage by the solar wind/IMF data of each
  event in the multi-storm data set is crucially
  important.

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A simplified test predicting the Dst

• Used 143,000 5-min avg. data (Dst, solar wind,
  IMF), based on the same 37-storm data set for the
  period 1996-2000.
• The model Dst included 3 terms with 3 unknown
  decay timescales T1, T2, T3, a correction for the
  external pressure, and a free term
• Running a nonlinear fitting code resulted in a
  rms deviation 12 nT, and a correlation
  coefficient R0.943.
• Two distinctly different timescales were found
  T14 hrs and T2T31 day, corresponding to the
  partial and symmetric ring currents,
  respectively.
• Our main goal was to test the method and get a
  rough estimate of the parameters, not to predict
  the Dst itself e.g., Temerin and Li, 2003
  Valdivia et al., 1996 etc.

summation here starts from the beginning of the
storm
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Observed vs model Dst for the 04/06/2000 event
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Summary

• A data-based model of the storm-time field in the
  inner magnetosphere has been developed, using
  spacecraft observations of 37 major events.
• The model is driven by upstream S.W./IMF
  parameters and takes into account (1) finite
  response and recovery times and (2) nonlinear
  saturation of the field sources at times of
  extremely strong external driving.
• During the main phase of great space storms, the
  inner magnetosphere becomes severely distorted,
  especially in the dusk sector, so that field
  lines with footpoints at 53-55o become
  essentially tail-like.
• ? Particle simulations of storms in the inner
  magnetosphere should not rely on quasi-dipolar
  models.
• We are on track to develop a new model, based on
  a more realistic para-meterization, to describe
  and forecast storm-time evolution of
  magneto-spheric field in individual events.