## 1. Introduction

[2] During the last years the possibility to estimate ionopsheric parameters using GPS observations has opened a very active and promising field of research. Dual-frequency GPS observations provide information on the integrated electron density along the raypath of the signals from the satellites to the receivers and hence GPS is primarily used to estimate the total electron content (TEC). There are today a variety of approaches for processing GPS observations and producing maps of the vertical TEC (VTEC) distribution with high spatial and temporal resolution [e.g., *Gao et al.*, 1994; *Feltens*, 1998; *Mannucci et al.*, 1998; *Hernández-Pajares et al.*, 1999; *Schaer*, 1999]. Many of these studies have been possible thanks to the existence of a worldwide network of GPS receivers that operates under the umbrella of the International Global Navigation Satellite Systems Service (IGS) [*Beutler et al.*, 1999; *Hernández-Pajares*, 2003].

[3] The radial geometry of GPS observations collected from ground-based receivers limits their capability to provide information on the vertical distribution of the electron density. *Hajj et al.* [1994], *Howe et al.* [1998], *Meza et al.* [2000], among others, used simulated data to demonstrate that this limitation can be overcome by adding observations collected from the space by GPS receivers flying onboard of low-Earth orbiting (LEO) satellites (e.g., GPS-Met, CHAMP, GRACE, SAC-C, TOPEX/Poseidon, Jason 1, etc.). Raypaths from the higher GPS to a LEO satellite provide the TEC at different heights through the ionosphere, thus allowing to estimate the vertical distribution of the electron density. In addition, *Ruffini et al.* [1999], *Hernández-Pajares et al.* [2000], *Jakowski et al.* [2002], *García-Fernández et al.* [2003], among others, demonstrated the capability to estimate the actual vertical electron distribution by means of tomographic processing strategies that make use of space-based GPS observations.

[4] In previous works [*Meza*, 1999; *Brunini et al.*, 2003], we presented a method to estimate the three-dimensional (latitude, longitude and time) VTEC distribution, as well as the four-dimensional (including height) electron density distribution, using ground-based GPS observations belonging to the IGS network and space-based GPS observations collected by the NASA's GPS-Met mission. We used a nontomographic approach based on an Oxygen Chapman profile to represent the vertical distribution of the electron density. Then, we adjusted the electron density of the peak of the profile in order to minimize the differences between the TEC measured by GPS and computed by integration of the electron density described by the Chapman approach. On the other hand, *Komjathy et al.* [1998], *Hernández-Pajares et al.* [2002], *Nava et al.* [2003], *Hajj et al.* [2004], among others, discussed different nontomographic approaches that rely upon different empirical models of the ionosphere and make use of different strategies to ingest GPS data into those models.

[5] In this contribution we present a new procedure in which the rather simple Chapman approach, commonly used as a standard model, is replaced by the more complex but realistic NeQuick model [*Radicella and Leitinger*, 2001]. The method is not intended to be an improvement over the tomographic approach; rather, is to be viewed as a device to improve the mean parameter of a ionospheric model (the NeQuick model in our case) by means of GPS data, so as to better understand the underlying physics. A good model would allow us to obtain TEC values where data is not available, contrary to the tomographic method, which bases its predictions on measured data. The method herein proposed to ingest GPS observations into the NeQuick model is discussed in the second section of this paper. That section encompasses four subsections: the first presents the relation that links the TEC to the GPS observations; the second summarizes the main features of the NeQuick model and how it can be used to compute the TEC; the third proposes a parameterization of the NeQuick model in terms of a set of constant parameters that describes the electron density of the F2 ionospheric peak; and the fourth establishes the equation of observation that connects the previously mentioned parameters with the GPS observations and explains how those parameters can be estimated from the data. In the third section of this paper, we apply the method previously described to ingest GPS observations into the NeQuick model and we assess the achieved improvements by comparing our results to Digisonde measurements. Finally, we close the paper with our conclusions.