Slant tropospheric models based on machine learning
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Date
2025
Publication Type
Other Conference Item
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yes
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Abstract
Tropospheric errors pose a major challenge for high-precision applications of space geodetic techniques such as GNSS, InSAR, and VLBI. To estimate tropospheric delays and support real-time geodetic applications, empirical tropospheric models are needed. For example, GPT3, a state-of-the-art model developed by TU Wien, provides meteorological parameters, mapping functions and tropospheric gradients at any time and location and has been widely used by the community. However, traditional models rely on simplistic representations of tropospheric parameters, such as mean values combined with annual and semi-annual amplitudes. They also assume symmetry and use gradient compensation to account for asymmetries, which may limit their accuracy. Given the powerful modeling capabilities of machine learning, we explore its potential to enhance empirical models by directly predicting mapping functions in any direction. In this study, we processed over five years of ray-tracing results from ERA5 to create a training dataset and developed machine-learning-based models for mapping functions. We demonstrate that these models can predict mapping factors in a single step for arbitrary elevation and azimuth angles. Our new model is significantly faster in computation and more compact in size than the traditional GPT3 model, while maintaining comparable accuracy regarding global performance.
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published
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Book title
IAG Scientific Assembly 2025: Abstract Book
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Volume
Pages / Article No.
100 - 100
Publisher
International Association of Geodesy
Event
IAG Scientific Assembly 2025
Edition / version
Methods
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Date collected
Date created
Subject
Machine learning; Mapping function; Space geodesy; Ray-tracing; ERA5
Organisational unit
09707 - Soja, Benedikt / Soja, Benedikt
Notes
Conference lecture on September 5, 2025