Matthias Schartner

Last Name

Schartner

First Name

Matthias

ORCID

0000-0001-5855-7280

Organisational unit

09707 - Soja, Benedikt / Soja, Benedikt

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Publications 1 - 10 of 116

VGOS Intensives Ishioka-Onsala
Haas, Rüdiger; Varenius, Eskil; Diamantidis, Periklis-Konstantinos; et al. (2021)
25th European VLBI Group for Geodesy and Astronomy Working Meeting 14-18 March 2021 Cyberspace: Information and Book of Abstracts
Geophysically Informed Machine Learning for Improving Rapid Estimation and Short‐Term Prediction of Earth Orientation Parameters
Kiani Shahvandi, Mostafa; Dill, Robert; Dobslaw, Henryk; et al. (2023)
Journal of Geophysical Research: Solid Earth
Rapid provision of Earth orientation parameters (EOPs, here polar motion and dUT1) is indispensable in many geodetic applications and also for spacecraft navigation. There are, however, discrepancies between the rapid EOPs and the final EOPs that have a higher latency but the highest accuracy. To reduce these discrepancies, we focus on a data-driven approach, present a novel method named ResLearner, and use it in the context of deep ensemble learning. Furthermore, we introduce a geophysically constrained approach for ResLearner. We show that the most important geophysical information to improve the rapid EOPs is the effective angular momentum functions of atmosphere, ocean, land hydrology, and sea level. In addition, semidiurnal, diurnal, and long-period tides coupled with prograde and retrograde tidal excitations are important features. The influence of some climatic indices on the prediction accuracy of dUT1 is discussed, and El Niño Southern Oscillation is found to be influential. We developed an operational framework, providing the improved EOPs on a daily basis with a prediction window of 63 days to fully cover the latency of final EOPs. We show that under the operational conditions and using the rapid EOPs of the International Earth Rotation and Reference Systems Service (IERS), we achieve improvements as high as 60%, thus significantly reducing the differences between rapid and final EOPs. Furthermore, we discuss how the new final series IERS 20 C04 is preferred over 14 C04. Finally, we compare against EOP hindcast experiments of the European Space Agency, on which ResLearner presents comparable improvements.
Modelling the Troposphere with Global Navigation Satellite Systems, Meteorological Data and Machine Learning
Crocetti, Laura; Schartner, Matthias; Schindler, Konrad; et al. (2024)
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Global Navigation Satellite Systems (GNSS), such as the American Global Positioning System (GPS) and the Euro pean Galileo system, are capable of monitoring tropospheric properties. An important parameter describing the tropo spheric impact on GNSS is zenith wet delay (ZWD), which is highly correlated to the amount of water vapour in the troposphere and thus interesting for atmospheric and climate research. This work demonstrates how GNSS observations help to sense the atmosphere and its dynamics by using a newly developed machine learning-based ZWD model. The model provides ZWD globally for the years 2010 to 2023 with a positive trend in the Northern Hemisphere and a negative trend in the Southern Hemisphere. Furthermore, the global average ZWD anomaly follows alternating trends, strongly correlated with the El Nino Southern Oscillation (ENSO) ˜ index, increasing up to a correlation coefficient of 0.74 when introducing a time lag of two months.
On the detection of structural breaks in GNSS station coordinate time series caused by earthquakes using machine learning
Crocetti, Laura; Schartner, Matthias; Soja, Benedikt (2021)
IAG 2021 Abstract Book: Geodesy for a Sustainable Earth
Comparison of machine-learning-based predictions of Earth orientation parameters using different input data
Soja, Benedikt; Kiani Shahvandi, Mostafa; Schartner, Matthias; et al. (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 and prediction of EOP, effective angular momentum (EAM) functions, covering the domains of the atmosphere, ocean, and land hydrology, are typically incorporated to achieve the best performance. The Space Geodesy group at ETH Zurich provides operational predictions of EOP and EAM via its Geodetic Prediction Center (GPC; https://gpc.ethz.ch). We have developed machine learning approaches to predict EOP over different time horizons based on existing EOP and EAM times series provided by other institutions. In this contribution, we analyze the quality of our operational predictions with a focus on comparing the results based on different input time series. In particular, we use different rapid EOP products (IERS, SYRTE, and JPL) and EAM forecasts (GFZ and ETH). In terms of EAM, we study the impact of using only atmospheric angular momentum compared to the sum of all different EAM components. Preliminary findings indicate that rapid EOP data from SYRTE or JPL in combination with a full set of EAM functions leads to the best accuracy when considering IERS final products as reference.
Active mitigation of spaceborne radio frequency interference
Schartner, Matthias; Wunderlin, Luisa; Habana, Nlingilili; et al. (2023)
26th European VLBI Group for Geodesy and Astronomy Working Meeting. Information and Book of Abstracts
Active spaceborne RFI mitigation strategy for VLBI
Schartner, Matthias; Soja, Benedikt (2024)
Achievements of the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC)
Śliwińska, Justyna; Kur, Tomasz; Nastula, Jolanta; et al. (2023)
EGUsphere
The accurate determination of Earth Orientation Parameters (EOP) requires post-processing of observational data collected from various space geodetic techniques, which causes delays in providing EOP solutions. However, receiving instantaneous information about EOP in real time is crucial in precise positioning and navigation. Therefore, EOP prediction (particularly short-term) has become a subject of increased attention within the international geodetic community. In the light of the developments of advanced geodetic data processing, modelling effective angular momentum functions, and developing new prediction methods, a re-assessment of the various EOP predictions was pursued in the frame of the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC). The campaign was run by Centrum Badań Kosmicznych Polskiej Akademii Nauk (CBK PAN), in cooperation with GeoForschungsZentrum (GFZ) and under the auspices of the International Earth Rotation and Reference Systems Service (IERS). The campaign started on 1st September 2021 and finished on 28th December 2022, giving 7327 submitted predictions within 82 weeks. The campaign was a great success of the geodetic community thanks to international cooperation. The presentation provides the summary of the 2nd EOP PCC. We focus on the recap of the statistics on the involved participants, i.e., the number of prediction methods and input data. Then we will present the accuracy of EOP predictions based on the mean absolute error computed for IERS 14 C04 solution as a reference. Additionally, we present the quality of predictions in clusters created from the campaign participants (IDs) based on their modern prediction methods combined with input data (EOP observations and data on the Earth’s surficial fluids) We conclude the presentations with plans for the prediction assessment, also in terms of the possible continuation of the campaign involving new release of IERS C04 20.
Optimal VLBI baseline geometry for UT1-UTC Intensive observations
Schartner, Matthias; Kern, Lisa; Nothnagel, Axel; et al. (2021)
Journal of Geodesy
One of the main tasks of Very Long Baseline Interferometry (VLBI) is the rapid determination of the highly variable Earth’s rotation expressed through the difference between Universal Time UT1 and Coordinated Universal Time UTC (dUT1). For this reason, dedicated one hour, single baseline sessions, called “Intensives”, are observed on a daily basis. Thus far, the optimal geometry of Intensive sessions was understood to include a long east–west extension of the baseline to ensure a dUT1 estimation with highest accuracy. In this publication, we prove that long east–west baselines are the best choice only for certain lengths and orientations. In this respect, optimal orientations may even require significant inclination of the baseline with respect to the equatorial plane. The basis of these findings is a simulation study with subsequent investigations in the partial derivatives of the observed group delays τ with respect to dUT1 ∂τ/∂dUT1. Almost 3000 baselines between artificial stations located on a regular 10×10 degree grid are investigated to derive a global and generally valid picture about the best length and orientation of Intensive baselines. Our results reveal that especially equatorial baselines or baselines with a center close to the equatorial plane are not suited for Intensives although they provide a good east–west extension. This is explained by the narrow right ascension band of visible sources and the resulting lack of variety in the partial derivatives. Moreover, it is shown that north–south baselines are also capable of determining dUT1 with reasonable accuracy, given that the baseline orientation is significantly different from the Earth rotation axis. However, great care must be taken to provide accurate polar motion a priori information for these baselines. Finally, we provide a better metric to assess the suitability of Intensive baselines based on the effective spread of ∂τ/∂dUT1.
Detecting earthquakes in GNSS station coordinate time series using machine learning algorithms
Crocetti, Laura; Schartner, Matthias; Soja, Benedikt (2021)
EGUsphere

Publications 1 - 10 of 116

Matthias Schartner

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