In the estimation of the ionospheric total electron content from the Global Positioning System (GPS) observables, various instrumental systematic effects such as the biases in the GPS satellites and receivers must be modeled. This paper describes a procedure, based on a Kalman filtering approach, for estimating these instrumental biases as well as the total electron content at each GPS station, using dual GPS data. The method is applied to six data sets, of 48 hours each, spanning one year, from the Deep Space Network with GPS stations in Australia, Spain, and the United States. The formal errors for the estimated satellite biases and for the total electron content at each station are about 0.07 ns and 0.2×1016 el/m2, respectively. The variation in time of the satellite biases (relative to the mean of all of them) estimated in different epochs during 1‐year period, is below 1 ns.

Abstract. Tomographic techniques are successfully applied to obtain 4D images of the tropospheric refractivity in a local dense network of global positioning system (GPS) receivers. We show here how GPS data are processed to obtain the tropospheric slant wet delays and discuss the validity of the processing. These slant wet delays are the observables in the tomographic processing. We then discuss the inverse problem in 4D tropospheric tomography making extensive use of simulations to test the system and de®ne the resolution and the impact of noise. Finally, we use data from the Kilauea network in Hawaii for February 1, 1997, and a local 4 Â 4 Â 40 voxel grid on a region of 400 km 2 and 15 km in height to produce the corresponding 4D wet refractivity ®elds, which are then validated using forecast analysis from the European Center for Medium Range Weather Forecast (ECMWF). We conclude that tomographic techniques can be used to monitor the troposphere in time and space.

A track of sea ice reflected Global Navigation Satellite System (GNSS) signal collected by the TechDemoSat‐1 mission is processed to perform phase altimetry over sea ice. High‐precision carrier phase measurements are extracted from coherent GNSS reflections at a high angle of elevation (>57°). The altimetric results show good consistency with a mean sea surface (MSS) model, and the root‐mean‐square difference is 4.7 cm with an along‐track sampling distance of ∼140 m and a spatial resolution of ∼400 m. The difference observed between the altimetric results and the MSS shows good correlation with the colocated sea ice thickness data from Soil Moisture and Ocean Salinity. This is consistent with the reflecting surface aligned with the bottom of the ice‐water interface, due to the penetration of the GNSS signal into the sea ice. Therefore, these high‐precision altimetric results have potential to be used for determination of sea ice thickness.

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GEROS-ISS stands for GNSS REflectometry, radio occultation, and scatterometry onboard the International Space Station (ISS). It is a scientific experiment, successfully proposed to the European Space Agency in 2011. The experiment as the name indicates will be conducted on the ISS. The main focus of GEROS-ISS is the dedicated use of signals from the currently available Global Navigation Satellite Systems (GNSS) in L-band for remote sensing of the Earth with a focus to study climate change. Prime mission objectives are the determination of the altimetric sea surface height of the oceans and of the ocean surface mean square slope, which is related to sea roughness and wind speed. These geophysical parameters are derived using reflected GNSS signals (GNSS reflectometry, GNSS-R). Secondary mission goals include atmosphere/ionosphere sounding using refracted GNSS signals (radio occultation, GNSS-RO) and remote sensing of land surfaces using GNSS-R. The GEROS-ISS mission objectives and its design, the current status, and ongoing activities are reviewed and selected scientific and technical results of the GEROS-ISS preparation phase are described.

Tomographic techniques are successfully applied to obtain 4D images of the tropospheric refractivity in a local dense network of global positioning system (GPS) receivers. We show here how GPS data are processed to obtain the tropospheric slant wet delays and discuss the validity of the processing. These slant wet delays are the observables in the tomographic processing. We then discuss the inverse problem in 4D tropospheric tomography making extensive use of simulations to test the system and de®ne the resolution and the impact of noise. Finally, we use data from the Kilauea network in Hawaii for February 1, 1997, and a local 4 Â 4 Â 40 voxel grid on a region of 400 km 2 and 15 km in height to produce the corresponding 4D wet refractivity ®elds, which are then validated using forecast analysis from the European Center for Medium Range Weather Forecast (ECMWF). We conclude that tomographic techniques can be used to monitor the troposphere in time and space.

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