Matthias Aichinger-Rosenberger


Last Name

Aichinger-Rosenberger

First Name

Matthias

ORCID

0000-0002-2716-4502

Organisational unit

02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry

Filters

2021 - 202525
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Publications 1 - 10 of 25
  • Snow height retrieval using GNSS SNR and local terrain information in a deep learning framework: Initial results
    Item type: Other Conference Item
    Aichinger-Rosenberger, Matthias; Crocetti, Laura (2025)
  • Machine learning-based retrieval of thermodynamic profiles from GNSS-RO observations: Preliminary results
    Item type: Other Conference Item
    Aichinger-Rosenberger, Matthias (2025)
    EGUsphere
    Radio Occultation (RO) using signals from Global Navigation Satellite Systems (GNSS) is one of the most promising remote sensing techniques for global atmospheric sounding. RO is a limb-sounding technique that uses GNSS signals, refracted during their propagation through the Earth’s atmosphere to a receiver on a low-Earth orbit (LEO) satellite. Over the last decades, RO products have been extensively used for data assimilation in Numerical Weather Prediction (NWP) as well as in climate science. The RO retrieval of atmospheric profiles is based on accurately measuring phase deviations, which are induced by atmospheric bending of the signal. Over the past two decades, several improvements of the retrieval process have been achieved, but significant challenges remain, including the dependency of certain retrieval steps on external information or the assumption of spherical symmetry. On the other hand, several RO missions such as the FORMOSAT-3/Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and its successor COSMIC-2 have been initiated over the last two decades. In addition, several commercial companies have launched their own RO payloads, which led to an enormous increase in data amounts in recent years. These large data amounts make it suitable for the application of machine learning (ML) models, which have not been used much by the RO community until now. Only few studies have tested the suitability of ML for replacing classic retrieval algorithms and despite already achieving promising results, they were not able to uncover the full potential of ML, mostly due to the small amounts of data used. This study presents an initial assessment of the performance of ML-based RO retrievals of temperature, pressure and humidity, trained using RO data from e.g. COSMIC-2 and state-of-the-art reanalysis products such as ERA5. It explores the suitability of various experimental setups and evaluates the sensitivity of the results to different feature setups.
  • Feasibility analysis of Swiss GNSS networks for GNSS Interferometric Reflectometry
    Item type: Conference Poster
    Aichinger-Rosenberger, Matthias (2025)
  • Retrieving Soil Moisture with GNSS-IR and Machine Learning
    Item type: Other Conference Item
    Crocetti, Laura; Dib, Elissa; Soja, Benedikt; et al. (2025)
  • Atmospheric Delay Modeling and Uncertainty Quantification with Probabilistic Neural Networks
    Item type: Conference Poster
    Soja, Benedikt; Hadas, Tomasz; Orus Perez, Raul; et al. (2025)
    Accurate modeling of atmospheric delays is crucial for high-precision Global Navigation Satellite System (GNSS) applications. In the ESA-funded project “Probabilistic Neural Network Models for Accurate Atmospheric Modeling”, we utilize Probabilistic Neural Networks (PNNs) to estimate tropospheric and ionospheric delays together with realistic uncertainty measures. The uncertainties, often neglected in machine learning applications, allow us to improve the weighting of observations in geodetic parameter estimation and increase confidence in the results. The data-driven framework is particularly suitable to capture nonlinear atmospheric behavior that is difficult to describe with conventional models. Two strategies are explored: a general-purpose “blind” model using only time, location, and geometry, and an enhanced model that incorporates auxiliary data for demanding use cases such as the Galileo High Accuracy Service (HAS). Both are tested with synthetic and real-world GNSS data to evaluate their impact on Position, Velocity, and Time (PVT) solutions. In this presentation, we present initial results, showing that PNN-based estimates of zenith tropospheric delay and total electron content, along with their calibrated uncertainties, have the potential to improve positioning accuracy and convergence.
  • Expanding the capabilities of GNSS-IR to complex terrain using machine learning: The MAGIC-CH project
    Item type: Other Conference Item
    Aichinger-Rosenberger, Matthias; Crocetti, Laura; Soja, Benedikt (2024)
  • MPG-NET: A low-cost, multi-purpose GNSS co-location station network for environmental monitoring
    Item type: Journal Article
    Aichinger-Rosenberger, Matthias; Wolf, Alexander; Senn, Cornelius; et al. (2023)
    Measurement
    Global Navigation Satellite Systems (GNSS) are very versatile sensors, which can be used for a variety of commercial and scientific applications. This holds especially true for different fields of remote sensing, such as atmospheric sounding or soil moisture monitoring. With the advent of low-cost dual-frequency GNSS equipment, certain applications are no longer restricted to the use of geodetic-grade instrumentation and can fully take leverage of the measurements in a second frequency band. In view of these emerging benefits, this study introduces the development and deployment of a multi-purpose GNSS station network in the Swiss Alps, called MPG-NET. We discuss the technical details of the station setup, in terms of GNSS hardware and technical design, as well as the quality of derived GNSS remote sensing products. In particular, our analyses focus on the quality of derived time series of zenith total delays (ZTD) and volumetric soil moisture content. Products are validated against benchmark data obtained from numerical weather models and in-situ sensors. For a prototype station, the results show a good agreement with the baseline, with errors of few millimeters for ZTD, and a remarkably high correlation for soil moisture content. Beside the documented value of low-cost GNSS for displacement monitoring (such as landslides or strong earthquakes), these findings are another step towards the establishment of a dense high-precision, multi-purpose GNSS network that comes at a very affordable price.
  • Detection of convective storm signatures in GNSS-SNR: Two case studies from the summer of 2021 in Switzerland
    Item type: Other Conference Item
    Aichinger-Rosenberger, Matthias; Aregger, Martin; Kopp, Jérôme; et al. (2023)
  • Troposphere monitoring using low-cost GNSS: Current status and related efforts at ETH Zurich
    Item type: Other Conference Item
    Aichinger-Rosenberger, Matthias; Pan, Yuanxin; Muraleedharan Thundathil, Rohith; et al. (2024)
Publications 1 - 10 of 25