Abstract. In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, longterm time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol.

With the availability of low-cost, mass-market dual-frequency GNSS (Global Navigation Satellite System) receivers, standalone processing methods such as Precise Point Positioning (PPP) are no longer restricted to geodetic-grade GNSS equipment only. However, with cheaper equipment, data quality is expected to degrade. This same principle also affects low-cost GNSS antennas, which usually suffer from poorer multipath mitigation and higher antenna noise compared to their geodetic-grade counterparts. This work assesses the quality of a particular piece of low-cost GNSS equipment for real-time PPP and high-rate dynamic monitoring applications, such as strong-motion seismology. We assembled the u-blox ZED-F9P chip in a small and light-weight data logger. With observational data from static experiments—which are processed under kinematic conditions—we assess the precision and stability of the displacement estimates. We tested the impact of different multi-band antenna types, including geodetic medium-grade helical-type (JAVAD GrAnt-G3T), as well as a low-cost helical (Ardusimple AS-ANT2B-CAL) and a patch-type (u-blox ANN-MB) antenna. Besides static tests for the assessment of displacement precision, strong-motion dynamic ground movements are simulated with a robot arm. For cross-validation, we collected measurements with a JAVAD SIGMA G3T geodetic-grade receiver. In terms of precision, we cross-compare the results of three different dual-frequency, real-time PPP solutions: (1) an ambiguity-float solution using the Centre National d’Études Spatiales (CNES) open-source software, (2) an ambiguity-float and an AR (ambiguity-resolved) solution using the raPPPid software from TU Vienna, and (3) and a PPP-RTK solution using the u-blox PointPerfect positioning service. We show that, even with low-cost GNSS equipment, it is possible to obtain a precision of one centimeter. We conclude that these devices provide an excellent basis for the densification of existing GNSS monitoring networks, as needed for strong-motion seismology and earthquake-early-warning.

By means of the time derivatives of Global Navigation Satellite System (GNSS) carrier-phase measurements, the instantaneous velocity of a stand-alone, single GNSS receiver can be estimated with a high precision of a few mm/s; it is feasible to even obtain the level of tenths of mm/s. Therefore, only data from the satellite navigation message are needed, thus discarding any data from a reference network. Combining this method with an efficient movement-detection algorithm opens some interesting applications for geohazard monitoring; an example is the detection of strong earthquakes. This capability is demonstrated for a case study of the 6.5 Mw earthquake of October 30, 2016, near the city of Norcia in Italy; in that region, there are densely deployed GNSS stations. It is shown that GNSS sensors can detect seismic compressional (P) waves, which are the first to arrive at a measurement station. These findings are substantiated by a comparison with data of strong-motion (SM) seismometers. Furthermore, it is shown that the GNSS-only hypocenter localization comes close (less than a kilometer) to the solutions provided by official seismic services. Finally, we conclude that this method can provide important contributions to a real-time geohazard early-warning system.

The 2016 Mw 7.0 Kumamoto earthquake resulted in exceptional datasets of Global Navigation Satellite Systems (GNSS) and seismic data. We explore the spatial similarity of the signals and investigate procedures for combining collocated sensor data. GNSS enables the direct observation of the long-period ground displacements, limited by noise levels in regimes of millimeters to several centimeters. Strong-motion accelerometers are inertial sensors and therefore optimally resolve middle- to high-frequency strong ground motion. The double integration from acceleration to displacement amplifies long-period errors introduced by tilt, rotation, noise, and nonlinear instrument responses and can lead to large nonphysical drifts. For the case study of the Kumamoto earthquake, 39 GNSS stations (1  samples/s) with nearby located strong-motion accelerometers (100  samples/s) are investigated. The GNSS waveforms obtained by precise point positioning under real-time conditions prove to be very similar to the postprocessed result. Real-time GNSS and nearby located accelerometers show consistent observations for periods between ∼3–5 and ∼50–100  s. The matching frequency range is defined by the long-period noise of the accelerometer and the low signal-to-noise ratio (SNR) of GNSS, when it comes to small displacements close to its noise level. Current procedures in fusing the data with a Kalman filter are verified for the dataset of this event. Combined data result in a very broadband waveform that covers the optimal frequency range of each sensor. We explore how to integrate fused processing in a real-time network, including event detection and magnitude estimation. Carrying out a statistical test on the GNSS records allows us to identify seismic events and sort out stations with a low SNR, which would otherwise impair the quality of downstream products. The results of this study reinforce the emerging consensus that there is real benefit to collocation GNSS and strong-motion sensors for the monitoring of moderate-to-large earthquakes.

Earth orientation parameters (EOPs) are essential in geodesy, linking the terrestrial and celestial reference frames. Due to the time needed for data processing and combining different space geodetic techniques, EOPs of the highest quality suffer latencies from several days to several weeks. However, real-time EOPs are needed for multiple geodetic and geophysical applications. Predictions of EOPs in the ultra-short term can overcome the latency of EOP products to a certain extent. Traditionally, predictions are performed using statistical methods. With the rapid expansion of computing capacity and data volume, the application of deep learning in geodesy has become increasingly promising in recent years. In particular, the Long Short-Term Memory (LSTM) neural networks, one of the most popular Recurrent Neural Network varieties, are promising for geodetic time series prediction. In this study, we investigate the potential of using LSTM to predict daily length of day (LOD) variations up to ten days in advance, accounting for the contribution of effective angular momentum (EAM). The data are first preprocessed to obtain residuals by combining physical and statistical models. Then, we employ LSTM networks to predict the LOD residuals using both LOD and EAM residuals as input features. Our methods outperform all other state-of-the-art methods in the first eight days with an improvement of up to 43% under the first EOP Prediction Comparison Campaign conditions. In addition, we assess the performance of LOD predictions using more extended time series to consider the improvements of EOP products over the last decade. The results show that extending data volume significantly increases the performance of the methods.

No abstract

No abstract

<p>The Earth Orientation Parameters (EOP) are fundamentals of geodesy, connecting the terrestrial and celestial reference frames. The typical way to generate EOP of highest accuracy is combining different space geodetic techniques. Due to the time demand for processing data and combining different techniques, the combined EOP products often have latencies from several days to several weeks. However, real-time EOP are needed for multiple geodetic and geophysical applications, including precise navigation and operation of satellites. Predictions of EOP in ultra-short time can overcome the problem of latency of EOP products to a certain extent.</p><p>In 2010, the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) collected predictions from 20 methods, which were mainly based on statistical approaches, and provided a combined solution. In recent years, more hybrid and machine learning methods have been introduced for EOP prediction.</p><p>The rapid expansion of computing power and data volume in recent years has made the application of deep learning in geodesy increasingly promising. In particular, the Long Short-Term Memory (LSTM) network, one of the most popular variations of Recurrent Neural Network (RNN), is promising for geodetic time series prediction. Thanks to the special structure of its cells, LSTM network can capture the non-linear structure between different time epochs in the time series. Therefore, it is suitable for EOP prediction problems.</p><p>In this study, we investigate the potential of using LSTM for the prediction of Length of Day (LOD). The LOD data from a combination of space geodetic techniques are first preprocessed in order to obtain residuals. For this step, we experiment with the application of Savitzky-Golay filters, Singular Spectrum Analysis and the Gauss Markov model. We then employ LSTM networks of different architectures and its variations such as bidirectional LSTM networks to predict the LOD residuals in ultra-short time. Furthermore, we study the impact of Atmospheric Angular Momentum (AAM) and its forecast data on the predictions. The performance of this method is compared with other results of EOP PCC in a hindcast experiment under the same conditions. In addition, we assess the performance of LOD predictions using longer time series than for the EOP PCC to consider improvements of EOP products over the last decade.</p>