The calibration of satellite radar altimetry has been extremely important for altimetry community and studying sea level changes. The main purpose of this contribution is to provide ongoing absolute calibration of altimeter bias near the Southern seas of Iran using the Iranian tide gauge network that equipped with GPS receivers to measure the sea surface heights synchronously in the same geocentric reference frame as the corresponding altimetry records. The sea level time series of coastal tide gauges have been used to estimate the bias, drift and annual/semiannual constituents of altimeter range measurements using (i) linear regression and (ii) combination of linear regression and harmonic analysis. To this end, three Iranian tide gauges located at Bushehr, Bandar Abbas and Chahbahar ports as well as Geophysical Data Records (GDR) products of Topex/Poseidon, Jason-1and Jason-2 have been considered. The numerical results have indicated that the mean absolute biases of Topex/Poseidon, Jason-1 and Jason-2 are about –26.23, 120.21 and 205.17 mm, respectively. The reliability of method has been assessed via GPS vessel at the altimeter bin nearby the Bushehr tidal stations. The presented method is viable to perfectly estimate the systematic errors, and as such, it can address the demands of high-accurate applications.
The international GNSS service (IGS) started publishing the precise ephemeris files in the form of the standard products #3, version C (the sp3c files) in which the GPS satellite orbits and clocks and their uncertainties were available since 2004. Incorporating these uncertainties into the GPS observation equations results in a better stochastic model of the processing system. The reality of these uncertainties is questioned and studied in this paper. Precise point positioning (PPP) model, statistical tests and variance component estimation (VCE) techniques are employed for this study. The results confirm the efficiency of the proposed method in the assessment of reality of the published ephemeris uncertainties.
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