Synergy Between Large Data Sets, First-Principles and Empirical
Models of the Magnetosphere
V. G. Merkin, M. I. Sitnov, A. Y. Ukhorskiy, E. Talaat, J. Gjerloev (JHU/APL)
L. Waldrop (University of Illinois)
This white paper advocates for the need of support for programs that take advantage of both the wealth of
currently available and future ionospheric and magnetospheric measurements and the maturity of physicsbased models of the ionosphere-thermosphere-magnetosphere system (ITM). To motivate and frame the
forthcoming discussion we start with a quote regarding data assimilation in terrestrial weather modeling
[1]: “The improvement in skill of numerical weather prediction over the last 40 years… is due to four
factors:
• The increased power of supercomputers, allowing much finer numerical resolution and fewer
approximations in the operational atmospheric models;
• The improved representations of small-scale physical processes… within the models;
• The use of more accurate methods of data assimilation…
• The increased availability of data…”
It will not be an exaggeration to say that numerical modeling of space weather, or more broadly, of the
ITM system in general, has reached a state where, according to all four factors mentioned, major
advances are possible. Here we would like to specifically concentrate on the last factor, “the increased
availability of data”.
Any numerical model inevitably makes approximations to the physics of the phenomenon studied and/or
to the specification of initial and boundary conditions owing to the lack of observational data. For
instance, the ITM system is largely describable by magnetohydrodynamic (MHD) equations, but there are
major domains of the system where the MHD approximation fails, e.g. in the ionosphere, the inner
Figure 1. First-principles and empirical models and large data sets can work in synergy to produce a
realistic representation of the ionosphere-thermosphere-magnetosphere system. The figures are adopted
from: LFM (Lyon-Fedder-Mobarry global MHD model) [17], IMAGE [18], AMPERE [19], DMSP [20],
SuperDARN [21], SuperMAG [22].
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magnetosphere, as well as in the magnetotail and at the magnetopause, where magnetic reconnection
takes place. While the work on inclusion of new physics through improved model resolution and coupling
with smaller-scale physics models (the first two factors in the above list) is a viable and crucially
important direction (which will undoubtedly be represented by other white papers), here we would like to
draw the community’s attention to the fact that a large and ever-growing collection of data has been
accumulated that can be used to improve the existing models. We provide two examples below:
1. The primary means of cross-scale coupling in the ITM system is through closure of magnetospheric
currents. The ionospheric conductivity, a measure of how freely charged particles in the ionosphere can
flow past the neutrals, controls this process thereby governing the energy deposition in the ionospherethermosphere (via collisions with neutrals) due to Joule heating and particle precipitation and also
regulating the electric fields, flows and distributions of plasma at high altitudes in the magnetosphere. The
conductivity itself is affected by the state of the complex ITM system: the magnetosphere influences
electron density by driving strong ionospheric turbulence and particle precipitation, while the
thermospheric winds and temperature regulate the neutral density distribution and thus collision
frequencies. Ionospheric conductivity is therefore a primary variable in this complicated chain of diverse
physical processes ultimately responsible for coupling of the solar wind-driven fully-ionized
magnetosphere to the neutral atmosphere. The sobering fact however is that ionospheric conductivity is
highly variable in both distribution and magnitude and perniciously difficult to characterize, because
directly measuring ionospheric neutral and electron density profiles, including local turbulent and kinetic
modifications, on a global scale remains unfeasible.
For most global MHD models of the Earth’s magnetosphere as well as kinetic models of the inner
magnetosphere the ionosphere plays the role of the inner boundary condition. Ionospheric conductivity is
crucial for this specification as it controls the coupling, via Ohm’s law, between the field-aligned currents
(calculated by the magnetospheric simulation) and the ionospheric convection electric field (the boundary
condition used in the simulation) [e.g. 2]. Historically, the conductivity tensor (both Pedersen and Hall
terms) has been calculated using semi-empirical models including the background solar EUV ionization
and auroral particle precipitation [e.g. 3]. While the first source is well-specified, the second is associated
with large uncertainties. In addition, these conventional models lack conductivity enhancements due to
ionospheric turbulence – effects that have been theoretically predicted to be capable of doubling the total
height-integrated conductivity during disturbed geomagnetic conditions [4].
The long-standing problem of poor specification of ionospheric conductivity in magnetospheric modeling
may finally be mitigated now that there exist multiple observational data sets of ionospheric
electrodynamic quantities, e.g. AMPERE (field-aligned currents), SuperDARN, DMSP (convection
electric field), and SuperMAG (ground magnetic perturbations). Combination of such comprehensive
synoptic data sets from these large and global-scale networks of radars, ground stations and satellites may
be applied synergistically to produce improved specifications of ionospheric conductivity, and, in fact,
can be assimilated into global magnetosphere, ring current or coupled ITM models. Such efforts will
enable new understanding of cross-scale interactions in the ITM system driven by realistic conductivity
distributions.
2. Another example comes from the need to evaluate hot plasma pressure, which determines the strength
of the ring current responsible for the strongest disturbances in geospace, magnetic storms. Global MHD
models underpredict the pressure in the inner magnetosphere, because they do not include an adequate
description of energy-dependent drift physics and they underestimate consequences of explosive magnetic
reconnection in the tail. Dedicated kinetic ring current models, in turn, may have the right micro-physics
but lack global self-consistent magnetic fields and suffer from uncertainties in specification of boundary
conditions, whereby the corresponding information is ingested into the model either at geosynchronous
orbit, using LANL satellite data on plasma density and temperature [e.g. 5], or further outside, using
crude empirical relationships connecting density and temperature in the plasma sheet with the bulk
plasma parameters in the solar wind [e.g. 6].
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On the other hand, plasma pressure can be reconstructed from extensive measurements of magnetospheric
magnetic field, using simple force balance condition ∇P=JxB and the abundance of space magnetometer
data from many past and present missions. Moreover, modern data-mining and data-fitting techniques
allow one to organize these data so as to reconstruct or even predict the detailed empirical picture of the
geomagnetic field and underlying currents. In particular, the recent empirical model TS07D [7] combines
magnetic field measurements from years of observations by such magnetospheric spacecraft as Cluster,
Geotail, Polar, IMP8, GOES 8, 9, 10, and 12, and can be augmented by other observers, e.g. the 5
THEMIS spacecraft. Such a model can conceivably be used to nudge a first-principles magnetospheric
model toward a more realistic solution either through a data assimilation scheme or some other way of
data ingestion. The quasi measurements of global magnetic field distribution may be used directly or
indirectly, through a pressure-reconstruction scheme based on force balance described above. In this case,
TS07D can be used as a source of information about hot particle pressure in the inner magnetosphere– the
plasma population component underrepresented in first-principles models– and, again, assimilated or
ingested to produce stronger inner magnetosphere pressure, ring and Region 2 currents, and thence
magnetotail stretching. A similar data ingestion concept could be formulated for hot particle pressure
distributions reconstructed from Energetic Neutral Atom (ENA) images of the magnetosphere [e.g. 8]
available from past (IMAGE [9]), present (TWINS [10]) and prototype (GEMINI [11]) spacecraft
missions.
There are already instances of very successful application of conventional data assimilation methods in
the physics of the ITM system, particularly in the ionosphere [12-14] and radiation belts [15, 16]. In other
cases, such as the ones we presented above, detailed data assimilation and ingestion schemes and
algorithms are yet to be worked out. The goal of this white paper is by no means to introduce such
schemes; rather, it is to urge the funding agencies to recognize the fact that the existence of large
observational datasets and well-developed empirical and physics-based models make our field ripe for
scientific endeavors building on synergy between them.
References
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[19] http://multivu.prnewswire.com/mnr/iridium/45153
[20] http://www.stratcom.mil/imagelibrary/space/21/278
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