Geophysical Research Letters
  • Open Access

GPT2: Empirical slant delay model for radio space geodetic techniques

Authors

  • K. Lagler,

    1. Department of Geodesy and Geoinformation (Research Group Advanced Geodesy), Vienna University of Technology, Vienna, Austria
    2. Institute of Geodesy and Photogrammetry (Geosensors and Engineering Geodesy), ETH Zurich, Switzerland
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  • M. Schindelegger,

    1. Department of Geodesy and Geoinformation (Research Group Advanced Geodesy), Vienna University of Technology, Vienna, Austria
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  • J. Böhm,

    Corresponding author
    • Department of Geodesy and Geoinformation (Research Group Advanced Geodesy), Vienna University of Technology, Vienna, Austria
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  • H. Krásná,

    1. Department of Geodesy and Geoinformation (Research Group Advanced Geodesy), Vienna University of Technology, Vienna, Austria
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  • T. Nilsson

    1. Department of Geodesy and Geoinformation (Research Group Advanced Geodesy), Vienna University of Technology, Vienna, Austria
    2. Section 1.1, GPS/GALILEO Earth Observation, Deutsches GeoForschungsZentrum, Potsdam, Germany
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  • All Supporting Information may be found in the online version of this article.

Corresponding author: J. Böhm, Department of Geodesy and Geoinformation (Research Group Advanced Geodesy), Vienna University of Technology, Vienna, Austria. (johannes.boehm@tuwien.ac.at)

Abstract

[1] Up to now, state-of-the-art empirical slant delay modeling for processing observations from radio space geodetic techniques has been provided by a combination of two empirical models. These are GPT (Global Pressure and Temperature) and GMF (Global Mapping Function), both operating on the basis of long-term averages of surface values from numerical weather models. Weaknesses in GPT/GMF, specifically their limited spatial and temporal variability, are largely eradicated by a new, combined model GPT2, which provides pressure, temperature, lapse rate, water vapor pressure, and mapping function coefficients at any site, resting upon a global 5° grid of mean values, annual, and semi-annual variations in all parameters. Built on ERA-Interim data, GPT2 brings forth improved empirical slant delays for geophysical studies. Compared to GPT/GMF, GPT2 yields a 40% reduction of annual and semi-annual amplitude differences in station heights with respect to a solution based on instantaneous local pressure values and the Vienna mapping functions 1, as shown with a series of global VLBI (Very Long Baseline Interferometry) solutions.

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