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Ionosphere Data Assimilation: Problems Associated with Missing Physics

  • R.W. SchunkEmail author
  • L. Scherliess
  • D.C. Thompson
Chapter
Part of the IAGA Special Sopron Book Series book series (IAGA, volume 2)

Abstract

Physics-based data assimilation models can be used for a wide range of applications in space physics, but as with all models the data assimilation models have limitations. The limitations can be associated with the data, the assimilation technique, or the background physics-based model. Here, we focused on the ionosphere and on elucidating the problems associated with missing physics in the background ionosphere model. The study was conducted with the Global Assimilation of Ionospheric Measurements-Gauss-Markov (GAIM-GM) physics-based data assimilation model. Simulations relevant to the low and middle latitude ionosphere were conducted in order to show how missing physics in the background ionosphere model affects the reconstructions. The low-latitude simulation involved the presence of equatorial plasma bubbles and a background physics-based ionosphere model that does not self-consistently describe bubbles. This problem, coupled with insufficient data, led to a Gauss-Markov reconstruction that contained a broad region of relatively low nighttime Total Electron Density (TEC) values instead of the individual plasma bubbles. The implications of plasma bubbles for reconstructions with the GAIM-FP (Full Physics) data assimilation model, where the electric fields and neutral winds are determined self-consistently, are noted. The mid-latitude simulation involved a Gauss-Markov ionosphere reconstruction for a geomagnetic storm where a Storm Enhanced Density (SED) appeared across the United States. Again, the background physics-based model (Ionosphere Forecast Model) could not produce the SED feature because this model does not take account of penetrating electric fields. Nevertheless, in this case there were sufficient data to overcome the deficiency in the background ionosphere model and the Gauss-Markov data assimilation reconstruction successfully described the SED feature and surrounding ionosphere.

Keywords

Geomagnetic Storm Neutral Wind Ionosphere Model Ensemble Kalman Filter Plasma Bubble 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was supported by Office of Naval Research grant N00014-09-1-0292 to Utah State University.

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Center for Atmospheric and Space SciencesUtah State UniversityLoganUSA
  2. 2.Center for Atmospheric and Space SciencesUtah State UniversityLoganUSA

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