Direct measurement of diurnal polar motion by ring laser gyroscopes

We report the first direct measurements of the very small effect of forced diurnal polar motion, successfully observed on three of our large ring lasers, which now measure the instantaneous direction of Earth's rotation axis to a precision of 1 part in 10^8 when averaged over a time interval of several hours. Ring laser gyroscopes provide a new viable technique for directly and continuously measuring the position of the instantaneous rotation axis of the Earth and the amplitudes of the Oppolzer modes. In contrast, the space geodetic techniques (VLBI, SLR, GPS, etc.) contain no information about the position of the instantaneous axis of rotation of the Earth, but are sensitive to the complete transformation matrix between the Earth-fixed and inertial reference frame. Further improvements of gyroscopes will provide a powerful new tool for studying the Earth's interior.Comment: 5 pages, 4 figures, agu2001.cl

First results from the GPS atmosphere sounding experiment TOR aboard the TerraSAR-X satellite

GPS radio occultation events observed between 24 July and 17 November 2008 by the IGOR occultation receiver aboard the TerraSAR-X satellite are processed and analyzed. The comparison of 15 327 refractivity profiles with collocated ECMWF data yield a mean bias between zero and −0.30 % at altitudes between 5 and 30 km. Standard deviations decrease from about 1.4 % at 5 km to about 0.6 % at 10 km altitude, however, increase significantly in the upper stratosphere. At low latitudes mean biases and standard deviations are larger, in particular in the lower troposphere. The results are consistent with 15 159 refractivity observations collected during the same time period by the BlackJack receiver aboard GRACE-A and processed by GFZ's operational processing system. The main difference between the two occultation instruments is the implementation of open-loop signal tracking in the IGOR (TerraSAR-X) receiver which improves the tropospheric penetration depth in terms of ray height by about 2 km compared to the conventional closed-loop data acquired by BlackJack (GRACE-A)

Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data

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

Geosciences Roadmap for Research Infrastructures 2025–2028 by the Swiss Geosciences Community

This community roadmap presents an integrative approach including the most urgent infrastructure requests for the future development of geosciences in Switzerland. It recommends to strengthen the multidisciplinary nature of the geosciences by putting all activities under the roof of the Integrated Swiss Geosciences supported by four specific research infrastructure pillars. The roadmap represents the view of the Swiss scientific community in the field of geosciences and is a formal element of the process to elaborate the Swiss Roadmap for Research Infrastructures 2023. This bottom-up contribution to the identification and selection of important national and international research infrastructures has been coordinated by the Swiss Academy of Sciences (SCNAT) on a mandate by the State Secretariat for Education, Research and Innovation (SERI).ISSN:2297-1564ISSN:2297-157

The CAMALIOT project

This invited presentation was given at an information event about the European Space Agency’s (ESA) Navigation Innovation and Support Programme (NAVISP) hosted by the Austrian Agency for the Promotion of Science (FFG) in preparation for the ESA Ministerial Conference 2022. The presentation was about the CAMALIOT project, which is currently funded through NAVISP and by FFG, outlining the initial results and what the next steps in the project are. In particular, information about the CAMALIOT crowdsourcing campaign (being run by IIASA) was provided as well as the status of the CAMALIOT machine learning infrastructure and the science uses cases in the project

A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single-frequency double-difference approach employing GPS and Galileo observations, whereas satellite-specific STEC time series are obtained through carrier-to-code leveling based on the geometry-free linear combination of observations from both GPS and Galileo constellations. Although the ZTD and STEC time series from smartphones were derived on a demonstration basis, a rather good level of consistency of such estimates with respect to the reference time series was found. For the considered periods, the RMS of differences between the derived smartphone-based time series of differential zenith wet delay and reference values were below 3.1 mm. In terms of satellite-specific STEC time series expressed with respect to the reference STEC time series, RMS of the offset-reduced differences below 1.2 TECU was found. Smartphone-based observations require special attention including additional processing steps and a dedicated parameterization in order to be able to acquire reliable atmospheric estimates. Although with lower measurement quality compared to traditional sources of GNSS data, an augmentation of ground-based networks of fixed high-end GNSS receivers with GNSS-capable smartphones would however, form an interesting source of complementary information for various studies relying on GNSS observations