Forecasting GNSS Zenith Troposphere Delay by Improving GPT3 Model with Machine Learning in Antarctica

Antarctica has a significant impact on global climate change. However, to draw climate change scenarios, there is a need for meteorological data, such as water vapor content, which is scarce in Antarctica. Global navigation satellite system (GNSS) networks can play a major role in overcoming this problem as the tropospheric delay that can be derived from GNSS measurements is an important data source for monitoring the variation of water vapor content. This work intends to be a contribution for improving the estimation of the zenith tropospheric delay (ZTD) obtained with the latest global pressure–temperature (GPT3) model for Antarctica through the use of long short-term-memory (LSTM) and radial basis function (RBF) neural networks for modifying GPT3_ZTD. The forecasting ZTD model is established based on the GNSS_ZTD observations at 71 GNSS stations from 1 January 2018 to 23 October 2021. According to the autocorrelation of the bias series between GNSS_ZTD and GPT3_ZTD, we predict the LSTM_ZTD for each GNSS station for period from October 2020 to October 2021 using the LSTM day by day. Based on the bias between LSTM_ZTD and GPT3_ZTD of the training stations, the RBF is adopted to estimate the LSTM_RBF_ZTD of the verified station, where the LSTM_ZTD represents the temporal forecasting ZTD at a single station, and the LSTM_RBF_ZTD represents the predicted ZTD obtained from space. Both the daily and yearly RMSE are calculated against the reference (GNSS_ZTD), and the improvement of predicted ZTD is compared with GPT3_ZTD. The results show that the single-station LSTM_ZTD series has a good agreement with the GNSS_ZTD, and most daily RMSE values are within 20 mm. The yearly RMSE of the 65 stations ranges from 6.4 mm to 32.8 mm, with an average of 10.9 mm. The overall accuracy of the LSTM_RBF_ZTD is significantly better than that of the GPT3_ZTD, with the daily RMSE of LSTM_RBF_ZTD significantly less than 30 mm, and the yearly RMSE ranging from 5.6 mm to 50.1 mm for the 65 stations. The average yearly RMSE is 15.7 mm, which is 10.2 mm less than that of the GPT3_ZTD. The LSTM_RBF_ZTD of 62 stations is more accurate than GPT3_ZTD, with the maximum improvement reaching 76.3%. The accuracy of LSTM_RBF_ZTD is slightly inferior to GPT3_ZTD at three stations located in East Antarctica with few GNSS stations. The average improvement across the 65 stations is 39.6%.

Numerical Forecast Correction of Temperature and Wind Using a Single-Station Single-Time Spatial LightGBM Method

Achieving high-performance numerical weather prediction (NWP) is important for people’s livelihoods and for socioeconomic development. However, NWP is obtained by solving differential equations with globally observed data without capturing enough local and spatial information at the observed station. To improve the forecasting performance, we propose a novel spatial lightGBM (Light Gradient Boosting Machine) model to correct the numerical forecast results at each observation station. By capturing the local spatial information of stations and using a single-station single-time strategy, the proposed method can incorporate the observed data and model data to achieve high-performance correction of medium-range predictions. Experimental results for temperature and wind prediction in Hainan Province show that the proposed correction method performs well compared with the ECWMF model and outperforms other competing methods.

Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics

This paper addresses the subject of inter-annual variability of the tropical precipitable water vapor (PWV) derived from 18 years of global navigation satellite system (GNSS) observations. Non-linear trends of retrieved GNSS PWV were investigated using the singular spectrum analysis (SSA) along with various climate indices. For most of the analyzed stations (~49%) the GNSS PWV anomaly was related to the El Niño Southern Oscillation (ENSO), although its influence on the PWV variability was not homogeneous. The cross-correlations coefficient values estimated between the Multivariate ENSO Index (MEI) and PWV were up to 0.78. A strong cross-correlation was also found for regional climate pattern expressed through CAR, DMI, HAW, NPGO, TNA and TSA indices. A distinct agreement was also found when instead of climate indices, the local sea surface temperature was examined (average correlation 0.60). The SSA method made it also possible to distinguish small-scale phenomena that affect PWV, such as local droughts or wetter rainy seasons. The overall nature of the investigated changes was also verified through linear trend analysis. In general, not a single station was characterized by a negative trend and its weighted mean value, calculated for all stations was equal to 0.08 ± 0.01 mm/year.

Coseismic Stability Assessment of a Damaged Underground Ammunition Storage Chamber Through Ambient Vibration Recordings and Numerical Modelling

In the past decade, ambient vibration measurements found numerous applications on unstable rock slopes and developed into a powerful tool for site characterization of slope instabilities. In this study, for the first time ambient vibration measurements were applied to a rock mass strongly disturbed and damaged by subsurface explosions. The site above the ammunition storage chamber at Mitholz (Switzerland) is especially interesting because the subsurface geology below the seismic array is well known, including the location of the caverns, and the degree of degradation caused by the subsurface explosions in 1947 of around 40 t TNT of ammunition. Measurement data were analyzed using current state-of-the-art seismic single-station and array methods, focusing on surface-wave dispersion analysis, wave field polarization, wave amplification using site-to-reference spectral ratios and analysis of normal mode behavior. The results allow for calibrating the elastic properties of a 2D numerical rock mechanical model which was used to simulate the stability of the disturbed rock mass during seismic loading. Therefore, ambient vibration measurements can contribute not only to a better understanding of the subsurface, but also for the assessment of earthquake risk.

The ISC Electronic Archive of Printed Station and Network Bulletins

We present an archive of scanned instrumental seismic bulletins pertaining to either a single station or a set of stations (network). This new service by the International Seismological Centre (ISC), called the ISC Electronic Archive of Station and Network Bulletins, is openly available at the ISC website. The archive is likely to be the most comprehensive to date, thanks to the collection from various sources done by the ISC in the past several decades. The search for scans of a seismic bulletin is based on the location of the town of the institution producing a bulletin. As such, the electronic archive is easy to use and is likely to facilitate the work of a wide community interested in studying past earthquakes and involved in preservation and digitization of analog recordings.

Detection of low-frequency earthquakes by the matched filter technique using the product of mutual information and correlation coefficient

The matched filter technique is often used to detect microearthquakes such as deep low-frequency (DLF) earthquakes. It compares correlation coefficients (CC) between waveforms of template earthquakes and the observed data. Conventionally, the sum of CC at multiple seismic stations is used as an index to detect the DLF earthquakes. A major disadvantage of the conventional method is drastically reduced detection accuracy when there are too few seismic stations. The new matched filter method proposed in this study can accurately detect microearthquakes using only a single station. It adopts mutual information (MI) in addition to CC to measure the similarity between the template and target waveforms. The method uses the product of MI and CC (MICC) as an index to detect DLF earthquakes. This index shows a distinct peak corresponding to an earthquake signal in a synthetic data set consisting of artificial noise and the waveform of a DLF earthquake. Application of this single-station method to field observations of Kirishima volcano, one of the most active volcanoes in Japan, detected a total of 354 events from the data in December 2010, whereas the catalog of the Japan Meteorological Agency shows only two. Of the detected events, 314 (89%) are likely DLF earthquakes and other events may be false detections. Most of the false detections correspond to surface-wave arrivals from teleseismic events. The catalog of DLF earthquakes constructed here shows similar temporal behavior to that found by the conventional matched filter method using the sum of the CC of the six stations near the volcano. These results suggest that the proposed method can greatly contribute to the accurate cataloging of DLF earthquakes using only a single seismic station. Graphical Abstract

Simulation Analysis of Detection Area for Flying Wing Layout UAV Using X-Band Bistatic Radar

The shape design of flying wing UAV can effectively reduce the detection and tracking probability of single-station radar. Bistatic radar has the advantage of anti-stealth for stealth targets due to the characteristics of multi-station distribution. However, there is no literature to study the detection area of flying wing UAV by bistatic radar. To solve this problem, FEKO electromagnetic simulation software is used to establish the X-band electromagnetic model of X-47B UAV, and the bistatic RCS of 0° pitch angle is simulated. According to the bistatic radar equation, the effective detection area of X-47B UAV lateral flight is simulated. The results show that the detection range of bistatic radar is mainly in the vicinity of 5 banded regions and bistatic radar. The five strip regions in the effective detection range correspond to the five bright lines of bistatic RCS one by one. When the baseline of bistatic radar is 20 km, the effective detection range is the largest. The research provides data support for the layout optimization of bistatic radar station and the trajectory optimization of flying wing UAV.