SA31E-2376
Quantitative Evaluation of Ionosphere Models for Reproducing Regional TEC During Geomagnetic Storms
Wednesday, 16 December 2015
Poster Hall (Moscone South)
Ja-Soon Shim1,2, Masha Kuznetsova2, Lutz Rastaetter2, Dieter Bilitza2,3, Mihail Codrescu4, Anthea J Coster5, Barbara Emery6, Ben Foster6, Timothy J Fuller-Rowell7,8, Larisa P Goncharenko5, Joseph Huba9, Cathryn N Mitchell10, Aaron J Ridley11, Mariangel Fedrizzi8, Ludger Scherliess12, Robert Walter Schunk12, Jan Josef Sojka12 and Lie Zhu12, (1)Catholic University of America, Washington, DC, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)George Mason University Fairfax, Fairfax, VA, United States, (4)NOAA, Space Weather Prediction Center, Boulder, CO, United States, (5)MIT Haystack Observatory, Westford, MA, United States, (6)High Altitude Observatory, Boulder, CO, United States, (7)SWPC/NOAA, Boulder, CO, United States, (8)CIRES, CU Boulder, Boulder, CO, United States, (9)US Naval Research Laboratory, Washington, DC, United States, (10)University of Bath, Bath, United Kingdom, (11)University of Michigan, Ann Arbor, MI, United States, (12)Utah State University, Logan, UT, United States
Abstract:
TEC (Total Electron Content) is one of the key parameters in description of the ionospheric variability that has influence on the accuracy of navigation and communication systems. To assess current TEC modeling capability of ionospheric models during geomagnetic storms and to establish a baseline against which future improvement can be compared, we quantified the ionospheric models’ performance by comparing modeled vertical TEC values with ground-based GPS TEC measurements and Multi-Instrument Data Analysis System (MIDAS) TEC. The comparison focused on North America and Europe sectors during selected two storm events: 2006 AGU storm (14-15 Dec. 2006) and 2013 March storm (17-19 Mar. 2013). The ionospheric models used for this study range from empirical to physics-based, and physics-based data assimilation models. We investigated spatial and temporal variations of TEC during the storms. In addition, we considered several parameters to quantify storm impacts on TEC: TEC changes compared to quiet time, rate of TEC change, and maximum increase/decrease during the storms. In this presentation, we focus on preliminary results of the comparison of the models performance in reproducing the storm-time TEC variations using the parameters and skill scores. This study has been supported by the Community Coordinated Modeling Center (CCMC) at the Goddard Space Flight Center. Model outputs and observational data used for the study will be permanently posted at the CCMC website (http://ccmc.gsfc.nasa.gov) for the space science communities to use.
