Gregor Möller

Last Name

Möller

First Name

Gregor

ORCID

0000-0002-6153-3084

Show all metadata

Search Results

Publications 1 - 10 of 41

A New Deep-Learning-Assisted Global Water Vapor Stratification Model for GNSS Meteorology: Validations and Applications
Zhang, Wenyuan; Gou, Junyang; Möller, Gregor; et al. (2024)
IEEE Transactions on Geoscience and Remote Sensing
Layer precipitable water (LPW), a water vapor product similar to precipitable water vapor (PWV), reports partial moisture content within a specified vertical range. Compared with PWV data, the latest LPW products can describe more refined distributions and variations in water vapor in the troposphere. Global Navigation Satellite Systems (GNSSs), as a powerful water vapor sensing tool, only provide the opportunity to retrieve all-weather PWV, not LPW products. To this end, we develop the first deep-learning-assisted, global water vapor stratification (GWVS) model to estimate the GNSS LPW within any given vertical range. The proposed model is trained and tested using the global radiosonde data, with the training and testing root mean square error (RMSE) of 0.94 and 1.10 mm for radiosonde LPW, indicating the excellent generalization of the GWVS model. Furthermore, the model is comprehensively validated using the data from the two regional GNSS networks and one global network. The RMSEs of the predicted GNSS LPW from the three GNSS networks compared with the co-located radiosonde LPW are 1.52, 1.80, and 1.54 mm, respectively. To study potential applications, we use the model-derived GNSS LPW products to calibrate Geostationary Operational Environmental Satellite-16 (GOES-16) LPW products and improve the GNSS water vapor tomography technique. Results show that the accuracy of three GOES-16 LPW products is improved by 31.3%, 23.3%, and 17.9%, respectively, and the RMSE of the tomography results is reduced from 2.28 to 1.67g/m(3). Both validation and application results highlight that the GWVS model retrieves the required GNSS LPW products and provides additional value for water-vapor-related studies.
Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland
Aichinger-Rosenberger, Matthias; Brockmann, Elmar; Crocetti, Laura; et al. (2022)
Atmospheric Measurement Techniques
Remote sensing of water vapour using the Global Navigation Satellite System (GNSS) is a well-established technique and reliable data source for numerical weather prediction (NWP). However, one of the phenomena rarely studied using GNSS are foehn winds. Since foehn winds are associated with significant humidity gradients between two sides of a mountain range, tropospheric estimates from GNSS are also affected by their occurrence. Time series reveal characteristic features like distinctive minima and maxima as well as a significant decrease in the correlation between the stations. However, detecting such signals becomes increasingly difficult for large datasets. Therefore, we suggest the application of machine learning algorithms for the detection and prediction of foehn events by means of GNSS troposphere products. This initial study develops a new, machine learning-based method for detection and prediction of foehn events at the Swiss station Altdorf by utilising long-term time series of high-quality GNSS troposphere products. Data from the Automated GNSS Network Switzerland (AGNES) and various GNSS sites from neighbouring countries as well as records of an operational foehn index are used to investigate the performance of several different classification algorithms based on appropriate statistical metrics. The two best-performing algorithms are fine tuned and tested in four dedicated experiments using different feature setups. The results are promising, especially when reprocessed GNSS products are utilised and the most dense station setup is used. Detection- and alarm-based measures reach levels between 66 %-80 % for both tested algorithms and thus are comparable to those from studies using data from meteorological stations and NWP. For operational prediction, limitations due to the availability and quality of GNSS products in near-real time (NRT) exist. However, they might be mitigated to a significant extent by provision of additional NRT products and improved data processing in the future. Results also outline benefits for the results when including geographically relevant stations (e.g. high-altitude stations) in the utilised datasets.
A cubesat mission concept for the remote sensing of the Martian atmosphere
Möller, Gregor; Ao, Chi O.; Mannucci, Anthony J. (2020)
ESS Open Archive
The atmospheric measurements made by the six Mars orbiters in operation (as of July 2020) significantly improved our understanding of the Martian weather and climate. However, while some of these orbiters will reach their lifetime, innovative and cost-effective missions are requested - not only to guarantee continued observation but also to address potential gaps in the existing observing network. Inspired by the success of the two Mars Cube One (MarCO) satellites we have established a mission concept, which is based on a series of cubesats, carried to Mars and injected into a low-Mars orbit as secondary payload on a larger orbiter. Each cubesat will be equipped with the necessary features for cross-link radio occultation (RO) measurements in X-band. Intelligent attitude control will allow for maintaining the cubesats in a so-called "string-of-pearls" formation over a period of about 150 solar days. During this period, a series of RO experiments will be carried out with the larger orbiter for up to 180 measurement series per day. Due to the specific observation geometry, we will obtain a unique set of globally distributed cross-link occultations. For processing of the observations, tomographic principles are applied to the RO measurements for reconstruction of high-resolution 2D temperature and pressure fields of the lower Martian atmosphere. The obtained products will give an insight into various unresolved atmospheric phenomena - especially of those which are characterized by distinct horizontal gradients in pressure and temperature, e.g. as observed at the day-night terminator, during dust storms, or over complex terrain.
Nanosatellites: The Next Big Chapter in Atmospheric Tomography
Möller, Gregor (2023)
Inverse Problems - Recent Advances and Applications
Nanosatellite technology opens up new possibilities for earth observation. In the next decade, large satellite constellations will arise with hundreds, up to thousand of satellites in low earth orbit. A number of satellites will be equipped with rather low-cost sensors, such as GNSS receivers, suited for atmospheric monitoring. However, the future evolution in atmospheric science leans not only on densified observing systems but also on new, more complex analysis methods. In this regard, tomographic principles provide a unique opportunity for sensor fusion. The difficulty in performing the conversion of integral measurements into 3D images is that the signal ray path is not a straight line and the number of radio sources and detectors is limited with respect to the size of the object of interest. Therefore, the inverse problem is either solved linearly or iterative nonlinear. In this chapter, an overview about the individual solving techniques for the tomographic problem is presented, including strategies for removing deficiencies of the ill-posed problem by using truncated singular value decomposition and the L-curve technique. Applied to dense nanosatellite formations, a new quality in the reconstruction of the 3D water vapor distribution is obtained, which has the potential for leading to further advances in atmospheric science.
Modeling tropospheric effects in space geodetic signals by machine learning and collocation
Shehaj, Endrit; Geiger, Alain; Miotti, Luca; et al. (2021)
Abstract Volume 19th Swiss Geoscience Meeting
High-Resolution Tropospheric Refractivity Fields by Combining Machine Learning and Collocation Methods to Correct Earth Observation Data
Shehaj, Endrit; Miotti, Luca; Geiger, Alain; et al. (2021)
IAC 2021 Congress Proceedings
Signals used for Earth observation, when travelling through the atmosphere, are sensitive to refractivity; especially high spatio-temporal variations of water vapor are difficult to model and correct. Remaining unmodeled tropospheric delays deteriorate the positioning solution and therefore limit the accuracy of sensing and navigation applications. These delays are usually computed with empirical models based on ground meteorological parameters (pressure, temperature and water vapor partial pressure). However, existing models are not accurate enough for high-precision applications such as GNSS, where in consequence the so-called zenith total delay (ZTD) has to be estimated together with other unknown parameters (coordinates etc.). For decades the Institute of Geodesy and Photogrammetry at ETH Zurich has been studying collocation methods for modeling of tropospheric delays using meteorological parameters, successfully interpolating pointwise or integral atmospheric observations. Meanwhile, machine learning has become a widely used and valuable alternative when big datasets are available for the training process. Indeed, we have already successfully predicted ZTDs based on meteorological parameters with an accuracy of 1-2 cm for locations (GNSS stations) already seen in the training phase. However, difficulties arise to predict delays at new locations. In this work, we take a step forward in investigating the combination of machine learning algorithms and physical models used in a collocation approach to derive atmospheric delay fields at a very high resolution. Thus, without processing any GNSS data we can predict tropospheric delay fields everywhere in the area of investigation. In this paper, we firstly describe the designed architecture of the neural network, secondly, the combination of least-squares collocation and artificial neural network for high resolution prediction of tropospheric delays. We benefit from the complementary characteristics of these algorithms. While machine learning is capable of successfully predicting the variation of time series for given points, empirical models based on collocation are well suited for describing spatial variations within the area of investigation. Finally, we report the achieved performance for the entire territory of Switzerland, showing that the synergic combination of these algorithms can overcome the individual drawbacks of each method and provide more accurate delay estimates than either method individually. Datasets of 11 years, covering the territory of Switzerland, consisting of GNSS ZTDs from 72 permanent AGNES/COGEAR (swisstopo, ETHZ) stations and meteorological data from MeteoSwiss were used for this research.
A high-precision commercial off-the-shelf GNSS payload board for nanosatellite orbit determination and timing
Möller, Gregor; Sonnenberg, Flavio; Wolf, Alexander; et al. (2022)
In-space cubesat constellation experiment: Preparation of a dual-frequency commercial off-the-shelf GNSS receiver for space
Möller, Gregor; Müller, Lukas; Chen, Kangkang; et al. (2021)
In 2013, the CubETH project was initiated at ETH Zürich with the vision to perform GNSS measurements onboard cubesats with very efficient, small, low-cost and low-power GNSS receivers. In December 2018, the developed GNSS payload board was flown the first time on a 3U cubesat - built and operated by the Swiss company Astrocast. As of March 2021, the payload board is still functioning, providing positioning and timing information for the Astrocast mission, and valuable insights into the long-term performance of commercial off-the-shelf GNSS hardware components in space. Motivated by the success of the first generation, we started in 2019 with the development of the next generation of GNSS payload board for high-performance applications. The basis is a commercial off-the-shelf GNSS receiver of the u-blox ZED-F9P series. This power-efficient, low-weight GNSS receiver does not only allow for the tracking of all four GNSS on two frequencies with a sampling rate of up to 20 Hz but also for the computation of real-time onboard solutions based on code and carrier phase measurements. In an initial field campaign, the performance of the GNSS receiver has been assessed. In a consecutive step, the suitability of the receiver for space operation was analyzed in a thermal vacuum chamber at RUAG Space and at a proton irradiation facility at the Paul Scherrer Institute. In this presentation, we will provide an insight into the performance of commercial off-the-shelf GNSS receivers in space. This will include the results of a two-year in-space cubesat experiment as well as a series of performance tests using state-of-the-art dual-frequency GNSS equipment. Based on the outcome, we conclude on the suitability of the selected GNSS hardware for dm to cm orbit accuracy and, therefore, on the operation of the next generation of GNSS receivers onboard large cubesat constellations.
Formation of a GNSS network in space based on LEO satellites
Müller, Lukas; Möller, Gregor; Rothacher, Markus; et al. (2023)
XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data
Pan, Yuanxin; Kłopotek, Grzegorz; Crocetti, Laura; et al. (2024)
Atmospheric Measurement Techniques
The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Given that nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately 20 000 raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free precise point positioning (PPP)-based zenith total delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of millimeter-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding satellite positioning service of the German National Survey (SAPOS) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data. This study demonstrates high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations.

Publications 1 - 10 of 41

Gregor Möller

Last Name

First Name

ORCID

Organisational unit

Filters

Search Results