Transcript A Model of Coronal Helmets with Prominences E. Greenfield

GITM Synthetic TEC comparison with GPS Data:
Global Ionosphere-Thermosphere Model Validation
J. A. Feldt and M. B. Moldwin
Atmospheric, Oceanic, and Space Sciences, University of Michigan
Abstract
Global Results
Since the ionosphere is the interface between the Earth and space environments and impacts
radio, television and satellite communication, it is imperative to model and predict it well. The
ionosphere undergoes changes due to plasma transport, chemistry and impact ionization
during geomagnetic storms and intervals where energy, mass and momentum are rapidly
transferred from the solar wind to the Earth’s space environment. Ionospheric changes can
cause scintillation in Global Positioning System (GPS) signals, and interruption in satellite
communication. Geomagnetic storms also generate changes in the plasmasphere and
therefore impact the radiation belts and the ring current. Unless ionospheric and plasmaspheric
dynamics can be detected and predicted, space weather problems will impact society with
regards to communication and space travel.
At the University of Michigan, Aaron Ridley created the Global Ionosphere-Thermosphere
Model (GITM) to contribute to the Space Weather Modeling Framework, whose goal is to
model from Sun to the mud. GITM covers the main ionospheric electron density peak, but not
the topside ionosphere or plasmasphere (which also contributes to electron density).
A good determination of an effective ionospheric model is looking at how well the electron
distribution matches with data. One way of doing this is to look at the total electron content
(TEC). TEC is the column density of electrons between two points within an area of one meter
squared, which gives an altitude integrated measure of the electron density variation. Fig. 1
shows a TEC extraction from GITM during quiet time. In this study GITM is compared to GPS
TEC data to determine the effects of the topside ionosphere and plasmasphere on TEC values
and to test the accuracy of the model. Note however, that observations of TEC are limited to
where there are ground based receivers and therefore most areas of the Earth (oceans) are
not sampled.
Regional Results
US
Europe
Japan
Fig. 1: A GITM Global TEC observation where the color indicates the magnitude of
TEC. Note that the highest values of TEC are at noon local time and near the
equator.
Malaysia
Model Background
GITM is a model of the thermosphere and ionosphere within the altitudes of 90 to 500 km. It
uses an altitude-based grid that stretches in latitude and altitude and assumes a nonhydrostatic solution, which strengthens the model in the auroral regions. GITM is initialized by
the densities and temperatures from the bottom of the atmosphere, data from MSIS and IRI, or
from a previous run. It allows for input from IRI, MSIS, satellite data (such as CHAMP), F10.7
data from the Flare Irradiance Spectral Model from LASP, NOAA/POES Hemispheric Power
Index Data, and IMF data. GITM also allows the user to turn on, off or set values for various
source terms. For this study all source terms were left on or set to the default values and the
model was initiated from the densities and temperatures at the bottom of the atmosphere.
Fig 3: These twelve plots show a whole year of differences between GPS Tec and GITM synthetic TEC. Note that the
dark blue indicates GITM over estimating TEC. Also note that through the months of October to May the Malaysian
difference displays a distinct underestimation.
Fig 6: A closer look at the regions with distinction in the global view or with the most GPS receivers
and each regions average difference between GPS TEC and GITM TEC over a year from October
2007 to September 2008.
GPS TEC
The GPS TEC data used in this study is from the Madrigal Database at the Haystack
Observatory at MIT. TEC is measured by the delayed phase of radio transmission from GPS
satellites, which transmit in dual frequencies.
Conclusions
GITM is greatly overestimating TEC, which is unexpected since it covers only a portion of the
atmosphere and it has a much lower density profile than IRI, which is known to be highly
comparable to data. Looking at individual regions, GITM overestimates more in the Summer to
Fall months over US and Europe, while remaining fairly constant in Japan. Malaysia shows a
much greater difference in the beginning of the year, which may be due to the biases of the
GPS receivers in Malaysia. Next step would be to turn on and off source terms in GITM’s code
and also to check the biases of GPS receivers in Malaysia to determine what is causing these
great differences.
Resources
Fig. 2: A Madrigal World Wide GPS Receiver Network observation where the color
indicates the magnitude of TEC. Note that, just like the GITM observation the highest
values of TEC are at noon local time and near the equator.
Fig 4: A comparison of the density profiles of IRI and GITM on Fig 5: A comparison of the global average difference between
01/03/08 at 12 UT.
GPS TEC and GITM TEC over a year from October 2007 to
September 2008.
Coster, A., and A. Komjathy (2008), Space Weather and the Global Positioning System, Space Weather,
6, 15-19.
Ridley, A.J., Y. Deng, and G. Tóth (2006), The global ionosphere-thermosphere model, J. of Atmo. and
Solar-Terr. Phys., 68, 839-864.
Jee, G., Schunk, R.W., and Scherliess, L. (2005), Comparison of IRI-2001 with TOPEX TEC
measurements, Journal of Atmospheric and Solar-Terrestrial Physics, 67, 365–380
Acknowledgements
Feldt is supported by the NASA Graduate Student Research Project through JPL and the Rackham Merit
Fellowship.