GPT2: Empirical slant delay model for radio space geodetic techniques

By K. Lagler, M. Schindelegger, Johannes Böhm, H. Krásná, Tobias Nilsson. 2013

586 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Astronomy and Astrophysics, Signal Processing

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Citations in the corpus

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Compressing Visual-linguistic Model via Knowledge Distillation (2021, background): “Following the prominent progress in the transformer-based [68] pretraining in natural language [14, 51, 38, 5, 11, 52], visual-linguistic pre-training models, either for image+text [44, 64, 9, 42, 26, 81, 41, 18, 40, 45] or for video+text [61, 40, 46, 84, 39].”
Improved one/multi-parameter models that consider seasonal and geographic variations for estimating weighted mean temperature in ground-based GPS meteorology (2014, methodology): “Lagler et al. (2013) improved the GPT model by estimating amplitudes and phases of the annual and semi-annual amplitudes, yielding the GPT2 model.”
Development of an improved empirical model for slant delays in the troposphere (GPT2w) (2015, methodology): “…of GPT2w in predicting the IGS zenith total delays with respect to other models, we determined bias plus RMS statistics at the 341 test locations for RTCA-MOPS (1999), the ESA blind model (ESA GALTROPO; Krueger et al. 2004, 2005; Martellucci 2012), and GPT2 (Lagler et al. 2013) by analogy to Fig.”