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Comparison of machine-learning-based predictions of Earth orientation parameters using different input data
(2023)Earth orientation parameters (EOP) are needed for precise navigation on Earth and in space and to connect the terrestrial to the celestial reference frame, and for several real-time applications. EOP are typically determined from the observations of different space-geodetic techniques. In order to overcome latencies in the processing and combination of these observations, accurate predictions of EOP are essential. To improve the modeling ...Other Conference Item -
Improving the accuracy of rapid Earth Orientation Parameters with the "ResLearner" machine learning method
(2023)EGUsphereDetermination of Earth Orientation Parameters (EOP) with utmost accuracy requires the combination of various data sources from different space geodetic techniques, some of which requiring long processing time. This results in a latency of up to several weeks by which the so-called final EOP are released. Since some of the important applications, including satellite navigation and orientation of deep space telescopes, require instantaneous ...Other Conference Item -
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Machine learning-based exploitation of crowdsourced GNSS data for atmospheric studies
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Uncertainty quantification in deep learning applied to geodetic problems
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Land motion in Europe imaged by GNSS
(2023)EGUsphereThe densification of high-quality, permanent GNSS stations in Europe enables a large-scale investigation of deformation processes on the Earth’s surface. This work aims to interpolate the horizontal and vertical GNSS station velocities and thus produce velocity fields showing the land motion for Switzerland, the Alps and Europe. The GNSS station velocities are provided by the EUREF Working Group on European Dense Velocities. The data set ...Other Conference Item -
A machine learning approach to recover GRACE-B accelerometer data
(2023)EGUsphereIn gravimetry satellite missions GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (GRACE Follow-On), accelerometer measurements from both satellites are necessary for the gravity field recovery. The accelerometer provides accurate measurements of the non-gravitational forces acting on the spacecraft, such as atmospheric drag, solar radiation pressure and albedo. These measurements are required to separate any non-gravitational ...Other Conference Item -
Machine learning for global modeling of the ionosphere based on multi-GNSS data
(2023)EGUsphereHigh-precision global ionospheric modeling is important for radio communication, navigation, or studies on space weather. Traditional spatial ionospheric modeling approaches include spherical harmonics and trigonometric B-splines. The Ionospheric Associated Analysis Centers (IAAC) of the International GNSS Service (IGS) use these methods to model vertical total electron content (VTEC) globally, and generate Global Ionospheric Maps (GIMs). ...Other Conference Item -
Detection of convective storm signatures in GNSS-SNR: Two case studies from the summer of 2021 in Switzerland
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