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Abstract

Within the first two years following the activation of the GPS radio occultation experiment aboard the geoscience satellite CHAMP more than 100,000 occultation events have been observed. Between 70 and 80% of these observations were successfully processed to yield vertical profiles of atmospheric refractivity, temperature and humidity. In the upper troposphere and stratosphere the derived atmospheric refractivities agree with ECMWF meteorological analyses to better than 0.5%; in the lower troposphere, however, a negative bias exceeding several percent is observed. End-to-end simulation studies were performed to investigate possible causes for the observed refractivity bias. Using the multiple phase screen method C/A-code modulated L1 signals are propagated through a spherically symmetric refractivity field derived from a high-resolution radio sonde observation. The propagated signals are tracked by a software GPS receiver and converted to refractivity profiles using the canonical transform technique and the Abel inversion. Ignoring noise and assuming an ideal receiver tracking behaviour the true refractivity profiles are reproduced to better than 0.1% at altitude above 2 km. The non-ideal case is simulated by adding between 14 and 24 dB of Gaussian white noise to the signal and tracking the signal with receivers operating at 50 and 200 Hz sampling frequency using two different carrier phase detectors. In the upper troposphere and stratosphere the receiver models reproduce the true refractivity profile to better than 0.1%. However, in the mid-troposphere down to altitudes of about 2 km a Costas-type phase-locked loop tracking induces negative refractivity biases on the order of −1 to −2% at 50 Hz sampling frequency. Modifications to the receiver tracking algorithm improve the retrieved signal signifi- cantly. Based on these simulation results a heuristic procedure based on the canonical transform method and the sliding spectral technique is proposed. The procedure is applied to simulated profiles as well as observations within the existing CHAMP data set.