A performance evaluation of the operational Jet Propulsion Laboratory/University of Southern California Global Assimilation Ionospheric Model (JPL/USC GAIM)

Abstract

[1] The Jet Propulsion Laboratory/University of Southern California Global Assimilation Ionospheric Model (JPL/USC GAIM) uses two data assimilation techniques to optimally combine ionospheric measurements with the physics model: a sparse, traditional Kalman filter to estimate the three-dimensional density state, and a four-dimensional variational approach (4DVAR) to estimate ionospheric drivers such as the equatorial E Â B drift or neutral winds. In this paper we study a specific implementation of the JPL/USC GAIM Kalman filter (single ion, low-resolution, and input data from 200 ground GPS sites) and validate its global accuracy over 137 days by comparisons to independent GPS slant total electron content (TEC) observations (''missing site'' tests) and independent JASON vertical TEC observations. The assimilation accuracy is robust with a slant TEC spatial prediction RMS error of 4 TECU (Total Electron Content Unit, 1 Â 10 16 e-/m 2 ) on average and a vertical TEC JASON RMS error of 7 TECU. Removing what appears to be a positive %4.4 TECU bias from the JASON observations, we obtain an improved performance of 5.3 TECU over the oceans. Comparisons with a single, thin shell global ionospheric map model and the International Reference Ionosphere and Bent ionospheric models are also provided.

Detecting ionospheric TEC perturbations caused by natural hazards using a global network of GPS receivers: The Tohoku case study

“…To reveal acoustic and gravity wave-generated TEC disturbances we use the GAIM approach. JPL/USC GAIM is a physics-based 3D data assimilation model that uses both 4DVAR and Kalman filter techniques to solve for the ion and electron density state and key drivers such as equatorial electrodynamics, neutral winds, and ion production terms (e.g., Mannucci et al, 2004;Wang et al, 2004;Mandrake et al, 2005;Pi et al, 2009;Komjathy et al, 2010). GAIM is capable of ingesting multiple data sources, updates the 3D electron density grid every 5-12 minutes, and solves for improved drivers every 1-2 hours.…”

Improved Modeling of Global Ionospheric Total Electron Content Using Prior Information

“…Other scholars have obtained results similarly showing that J2TEC values are larger than the VTEC values derived from GNSS measurements at mid-high latitudes [28,37,44]. In general, the bias among GNSS-derived VTEC maps and J2TEC presented a similar trend, with an average difference of approximately 2 TECU.…”

Calibration of Spaceborne Linearly Polarized Low Frequency Sar Using Polarimetric Selective Radar Calibrators

“…The FR estimates in (30) and (31) lie between ±π/2, leading to an ambiguity of ±kπ. However, this can be removed by using (1) to provide an independent estimate of FR, where TEC is given by the global ionospheric TEC maps estimated by the Global Navigation Satellite System (GNSS) and use is made of the IGRF10 model for the Earth's magnetic field [12].…”

“…However, this can be removed by using (1) to provide an independent estimate of FR, where TEC is given by the global ionospheric TEC maps estimated by the Global Navigation Satellite System (GNSS) and use is made of the IGRF10 model for the Earth's magnetic field [12]. The International GNSS Service provides bi-hourly global TEC maps with grid-points spaced 5 • in longitude and 2.5 • in latitude [12,29], with an overall root mean square (RMS) errors of 3-5 TECU [29,30]. Hence, an unambiguous FR estimator is given by [25]:…”