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Utah State University Global Assimilation of Ionospheric Measurements (USU-GAIM) Model


CCMC Services available for USU-GAIM
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Model Developer(s)
R.W. Schunk, L. Scherliess, J.J. Sojka, D.C. Thompson, L. Zhu
Center for Atmospheric & Space Sciences, Utah State University

Model Description
Physics-based data assimilation models of the ionosphere were developed at Utah State University as part of a DoD Multidisciplinary University Research Initiative (MURI) program. The USU effort was called Global Assimilation of Ionospheric Measurements (GAIM). One of the USU data assimilation models is now available at the CCMC for use by the scientific community. This model is a Gauss-Markov Kalman Filter (GMKF) model, and it uses a physics-based model of the ionosphere and a Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) measurements. The physics-based model is the Ionosphere Forecast Model (IFM), which is global and covers the E-region, F-region, and topside from 90 to 1400 km. It takes account of five ion species (NO+, O2+, N2+, O+, H+). However, the main output of the model is a 3-dimensional electron density distribution at user specified times. In addition, auxiliary parameters are also provided, including NmE, hmE, NmF2, hmF2, slant and vertical TEC. The Gauss-Markov Kalman Model assimilates bottom-side Ne profiles from a variable number of ionosondes, slant TEC from a variable number of ground GPS/TEC stations, in situ Ne from four DMSP satellites, and line-of-sight UV emissions measured by satellites. Quality control algorithms for all of the data types are provided as an integral part of the model and the model takes account of latent data (up to 3 hours). With the GMKF model the ionospheric densities obtained from the IFM constitute a background ionospheric density field on which perturbations are superimposed based on the available data sources and their errors. The density perturbations and the associated errors evolve over time via a statistical Gauss-Markov process.

Model Input
While submitting a run on request the user sets the start date (year, day of the year) and the duration of the run in days (currently 1 day up to 7 days). All input parameters and data required by the model are obtained automatically.

The IFM model uses F10.7, average F10.7, daily Ap and eight 3-hour Kp indices. The IFM also uses empirical inputs for the neutral atmosphere and magnetosphere parameters needed by the model, e.g., neutral wind, electric field, auroral precipitation, solar EUV, and resonantly scattered radiation.

The USU GAIM 2.3 model accepts data from multiple sources, including slant TEC from GPS ground stations via RINEX files, a-priori bias information for GPS satellites and ground-stations, true-height electron density profiles from DISS ionosondes via SAO formatted files, SSULI UV radiances via SDF2 files, and DMSP IES in-situ electron densities via EDR files. At present, the automated CCMC runs on request system only uses GPS observations from up to 400 ground receivers spread around the world. Data from other sources will be added at later times when they become available for automatic acquisition.

Model Output
The primary output from the USU GAIM 2.3 model is a time-dependent 3-dimensional global electron density distribution. The CCMC on-line visualization tool allows a view of the full GAIM model output as well as the output obtained from the IFM. Vertical Equivalent Total Electron Content (TEC) obtained from the leveled, bias corrected, slant TEC values assimilated by the model and GPS stations coordinates are also provided.

Limitations and Caveats
The current version of the USU-GAIM model provides the 3-D ionospheric plasma density distribution over the globe. As a consequence the resolution of the model is rather coarse (15° longitude, 4.66° latitude).

The version of the model at the CCMC only assimilates GPS/TEC data between ±60° geographic latitude. The plasma density distribution at high latitudes is provided by the IFM and is currently only driven by the geophysical conditions.

The current version of the model cannot describe small-scale dynamic structures, such as spread-F and plasma bubbles.

The model results are more reliable near data sources.

Acknowledgements/Publication Policies:
Use of the USU-GAIM model results must be acknowledged in any presentations, publications, or proposals.

All publications should carry the acknowledgement: “The USU-GAIM Model was developed by the GAIM team (R.W. Schunk, L. Scherliess, J.J. Sojka, D.C. Thompson, L.Zhu) at Utah State University”.

Rules of the Road:
If USU-GAIM results are used in a presentation, publication, or proposal, contact the USU-GAIM team to ensure that the model results are being used properly.

If something is observed in the model results that appears “strange” please contact the USU-GAIM team to investigate the possible problem.

One copy of any published paper should be sent to Dr. R.W. Schunk so that a bibliography of papers and publications using the USU-GAIM model results can be maintained.

The same rules also apply for the use of results obtained from the Ionosphere Forecast Model (IFM).

References and relevant publications
Schunk, R.W., L. Scherliess, and J.J. Sojka, Ionospheric Specification and Forecast Modeling, Journal of Spacecraft and Rockets, 39, 314, 2002.

Scherliess, L., R.W. Schunk, J.J. Sojka, and D. Thompson, Development of a Physics-Based Reduced State Kalman Filter for the Ionosphere, Proceedings of 2002 Ionospheric Effects Symposium, edited by J.M. Goodman, JMG Associates, Alexandria, VA, 2002.

Schunk, R.W., L. Scherliess, J.J. Sojka, and D. Thompson, Global Assimilation of Ionospheric Measurements (GAIM), Proceedings of 2002 Ionospheric Effects Symposium, edited by J.M. Goodman, JMG Associates, Alexandria, VA, 2002.

Schunk, R.W., L. Scherliess, and J.J. Sojka, Recent Approaches to Modeling Ionospheric Weather, Advances in Space Reasearch, Vol. 31, No. 4, 2003.

Scherliess, L., R.W. Schunk, J.J. Sojka, and D. Thompson, Development of a Physics-Based Reduced State Kalman Filter for the Ionosphere, Radio Science, 39, RS1S04, doi:10.1029/2002RS002797, 2004.

Schunk, R.W., L. Scherliess, J.J. Sojka, and D. Thompson, Global Assimilation of Ionospheric Measurements (GAIM), Radio Science, 39, RS1S02, doi:10.1029/2002RS002794, 2004.

Schunk, R.W., L. Scherliess, J.J. Sojka, D.C. Thompson and L. Zhu, An Operational Data Assimilation Model of the Global Ionosphere, Proceedings of 2005 Ionospheric Effects Symposium, edited by J.M. Goodman, JMG Associates, Alexandria, VA,, 2005.

Scherliess, L., Data Assimilation: A ‘new’ Tool for Ionospheric Sciences and Applications, CEDAR Post, 2005.

Schunk, R.W., L. Scherliess, J.J. Sojka, D.C. Thompson and L. Zhu, An Operational Data Assimilation Model of the Global Ionosphere, Space Weather Journal, in press, 2005.

McDonald, S. E., S. Basu, K.M. Groves, C.E. Valladares, L. Scherliess, D. Thompson, R. W. Schunk, J.J. Sojka and L. Zhu, Extreme Longitudinal Variability of Plasma Structuring in the Equatorial Ionosphere on a magnetically quiet equinoctial day, Radio Science, in press, 2005.

Thompson, D.C., L. Scherliess, J.J Sojka, R.W. Schunk, The Utah State University Gauss-Markov Kalman Filter of the Ionosphere: The Effects of Slant TEC and Electron Density Profile Data on Model Fidelity, Journal of Atmospheric and Solar-Terrestrial Physics, in press, 2005.

Zhu, L., R.W. Schunk, L. Scherliess, J.J. Sojka, and D.C. Thompson, Validation of the Ionospheric Forecasting Model (IFM) using TOPEX TEC Measurements, Radio Science, in press, 2005.

Scherliess, L., R.W. Schunk, J.J. Sojka, D.C. Thompson, and L. Zhu, The USU GAIM Gauss-Markov Kalman Filter Model of the Ionosphere: Model Description and Validation, Journal of Geophysical Research, submitted, 2005

Relevant links
GPS RINEX Data from the SOPAC Data Archive

CCMC Contact(s)

301-286-4040

Developer Contact(s)

435-797-2357

National Aeronautics and Space Administration Air Force Materiel Command Air Force Office of Scientific Research Air Force Research Laboratory Air Force Weather Agency NOAA Space Environment Center National Science Foundation Office of Naval Research

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