Abstract. This paper presents a method to derive the ionospheric total electron content (TEC) and to estimate the biases of GPS satellites and dual frequency receivers using the GPS Earth Observation Network (GEONET) in Japan. Based on the consideration that the TEC is uniform in a small area, the method divides the ionosphere over Japan into 32 meshes. The size of each mesh is 2 • by 2 • in latitude and longitude, respectively. By assuming that the TEC is identical at any point within a given mesh and the biases do not vary within a day, the method arranges unknown TECs and biases with dual GPS data from about 209 receivers in a day unit into a set of equations. Then the TECs and the biases of satellites and receivers were determined by using the leastsquares fitting technique. The performance of the method is examined by applying it to geomagnetically quiet days in various seasons, and then comparing the GPS-derived TEC with ionospheric critical frequencies (foF2). It is found that the biases of GPS satellites and most receivers are very stable. The diurnal and seasonal variation in TEC and foF2 shows a high degree of conformity. The method using a highly dense receiver network like GEONET is not always applicable in other areas. Thus, the paper also proposes a simpler and faster method to estimate a single receiver's bias by using the satellite biases determined from GEONET. The accuracy of the simple method is examined by comparing the receiver biases determined by the two methods. Larger deviation from GEONET derived bias tends to be found in the receivers at lower (<30 • N) latitudes due to the effects of equatorial anomaly.

A post sunset bubble manifested by total electron content depletion was observed at midlatitudes (∼30°–34°N, ∼130°–134°E) during the main phase of a storm on 12 February 2000. With loss of lock and the rate of the total electron content index maps, the bubble was seen to bifurcate at its early growth phase. The upward drift speed was observed ∼300 m/s at ∼2150 km, and decreasing with increasing altitude and time. The bubble had unusually large latitudinal extension reaching midlatitude of 36.5°N (31.5°N magnetic latitude), indicating an apex height of ∼2500 km. In process of the evolution, the bubble drifted eastward at a speed of ∼50 m/s. The F region peak height and density obtained by a meridional ionosonde chain suggested a prompt penetrating magnetospheric electric field helped to trigger the super bubble.

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[1] In this paper we present a new method based on a Residual Minimization Training Neural Network (RMTNN) to reconstruct the three-dimensional electron density distribution of the local ionosphere with high spatial resolution (about 50 km  50 km in east/west and 30 km in altitude) using GPS and ionosonde observation data. In this method we reconstruct an approximate three-dimensional electron density distribution as a computer tomographic image by making use of the excellent capability of a multilayer neural network to approximate an arbitrary function. For this application the network training is carried out by minimizing the squared residuals of an integral equation. We combine several additional techniques with the new method, i.e., input space discretization, use of ionosonde observation data to improve the vertical resolution, automatic estimation of the biases of the satellite and the ground receivers by using the parameter estimation method, and estimation of plasmasphere contributions to the total electron content on the basis of an assumption of diffusive equilibrium with constant scale height. Numerical experiments for the actual positions of the GPS satellites and the ground receivers are used to validate the reliability of the method. We also applied the method to the analysis of real observation data and compared the results with ionosonde observations which were not used for the network training.Citation: Ma, X. F., T. Maruyama, G. Ma, and T. Takeda (2005), Three-dimensional ionospheric tomography using observation data of GPS ground receivers and ionosonde by neural network,