The GPS (Global Positioning System) radio occultation experiment onboard the German CHAMP (CHAllenging Minisatellite Payload) satellite was successfully started in February 2001. By the end of July 2001 more than 11,000 globally distributed vertical profiles of atmospheric temperature were derived. Operational data processing is based on the double difference method to correct for satellite clock errors. This technique requires a continuous data stream from a global network of GPS ground stations. Termination of the GPS Selective Availability mode on May 2, 2000 reduced the GPS clock errors by orders of magnitude, thereby rendering space‐based single differencing feasible. First results using this single difference technique without direct use of GPS ground station data are presented. The comparison of two data sets consisting of 436 occultations, one processed with space‐based single differencing, the other with classical double differencing, yields no discernible temperature bias below 30 km altitude, a standard deviation of <0.6 K below 20 km and <1.2 K below 30 km. The comparison of both data sets with corresponding meteorological analyses shows nearly identical results with differences in mean and standard deviation of <0.15 K up to 30 km.

The TanDEM-X mission will derive a global digital elevation model (DEM) with satellite SAR interferometry. Height references play an important role to ensure the required height accuracy of 10m absolute and 2m relative for 90% of the data. In this paper the main height reference data sets ICESat (for DEM calibration), SRTM (for phase unwrapping) and kinematic GPS-Tracks (KGPS -for DEM verification) are analyzed regarding to their accuracy. For the ICESat data a reliable quality measure is developed. For SRTM an improved version adjusted to reliable ICESat data is presented and a concept for collecting and evaluating decimeter-precise kinematic GPS tracks is proposed.

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Modelling correlations within laser scanning point clouds can be achieved by using synthetic covariance matrices. These are based on the elementary error model which contains different groups of correlations: non-correlating, functional correlating and stochastic correlating. By applying the elementary error model on terrestrial laser scanning several groups of error sources should be considered: instrumental, atmospheric and object based. This contribution presents calculations for the Leica HDS 7000. The determined variances and the spatial correlations of the points are estimated and discussed. Hereby, the mean standard deviation of the point cloud is up to 0.6

All measurements are affected by systematic and random deviations. A huge challenge is to correctly consider these effects on the results. Terrestrial laser scanners deliver point clouds that usually precede surface modeling. Therefore, stochastic information of the measured points directly influences the modeled surface quality. The elementary error model (EEM) is one method used to determine error sources impact on variances-covariance matrices (VCM). This approach assumes linear models and normal distributed deviations, despite the non-linear nature of the observations. It has been proven that in 90% of the cases, linearity can be assumed. In previous publications on the topic, EEM results were shown on simulated data sets while focusing on panorama laser scanners. Within this paper an application of the EEM is presented on a real object and a functional model is introduced for hybrid laser scanners. The focus is set on instrumental and atmospheric error sources. A different approach is used to classify the atmospheric parameters as stochastic correlating elementary errors, thus expanding the currently available EEM. Former approaches considered atmospheric parameters functional correlating elementary errors. Results highlight existing spatial correlations for varying scanner positions and different atmospheric conditions at the arch dam Kops in Austria.