On the 15th of March 2021, the strongest sandstorm in a decade occurred in northern China, and had a great adverse impact on the natural environment and human health in northern China. Real-time monitoring of dust storms is becoming increasingly important. In order to effectively analyze the non-hydrostatic delay (ZNHD) anomaly during a sandstorm, the method based on GNSS-derived tropospheric ZNHD residual to monitor the sandstorm is proposed at the same time. We studied the relationship between ZNHD/PWV and PM10/PM2.5 in Beijing, Changchun, Pingliang and Zhongwei before and after sandstorms. The ZNHD time series was then decomposed by singular spectrum analysis (SSA) and the residuals were obtained. The relationship between the GNSS-derived ZNHD residual and PM10 was analyzed. The results show that the impact of the sandstorm on PM10 is greater than that on PM2.5. Before the sandstorm, the correlation between PM10 and ZNHD was low, less than 0.25. When the sandstorm occurred, the correlation between PM10 and ZNHD increased significantly, and the maximum was greater than 0.7. When the sandstorm ended, the correlation between PM10 and ZNHD decreased significantly. Through the relationship between the ZNHD residual and PM10, it can be found that when the peak-to-peak values of the ZNHD residual are all above 80 mm, sandstorms may occur. But Rainfall, snowfall, haze and other abnormal weather can also lead to ZNHD anomalies.

On 15 January 2022, a violent eruption and tsunami of the Hunga Tonga-Hunga Ha’apai (HTHH) volcano in Tonga, South Pacific, caused widespread international concern. In order to detect the anomalous environmental response caused by the HTHH volcanic eruption based on GNSS ionospheric data, GNSS tropospheric data and GNSS coordinate time series, a new method combining the zenith non-hydrostatic delay difference method and the extreme-point symmetric mode decomposition (ESMD) method, was proposed to detect tropospheric anomalies. The moving interquartile range method and the ESMD method were introduced to detect ionospheric anomalous and coordinate time series anomalies, respectively. The results showed that 9–10 h before the eruption of the Tonga volcano and 11–12 h after the eruption of the Tonga volcano, obvious total electron content (TEC) anomalies occurred in the volcanic eruption center and its northeast and southeast, with the maximum abnormal value of 15 TECU. Significant tropospheric anomalies were observed on the day of the HTHH volcano eruption as well as 1–3 days and 16–17 days after the eruption, and the abnormal intensity was more than 10 times that of normal. The coordinate time series in direction E showed very significant anomalies at approximately 2:45 p.m. on 14 January, at approximately 4:30 a.m.–5:40 a.m. on 15 January, and at approximately 3:45 a.m. on 16 January, with anomalies reaching a maximum of 7–8 times daily. The abnormality in the direction north (N) is not obvious. Very prominent anomalies can be observed in the direction up (U) at approximately 4:30 a.m.–5:40 a.m., with the intensity of the anomalies exceeding the normal by more than 10 times. In this study, GNSS was successfully used to detect the anomalous environmental response during this HTHH volcano eruption.

The bright stars in the clear night sky with weak background lights should be observed in the traditional deflection of the vertical (DOV) measurement so that the DOV cannot be observed under all-weather conditions, which limits its wide applications. An all-weather DOV measurement method combining three-dimensional (3D) laser tracking free-fall and multi-GNSS baselines is proposed in this paper. In a vacuum environment, the 3D laser tracking technique is used to continuously track and observe the motion of free-fall with high frequency and precision for obtaining 3D coordinate series. The plumb line vector equation is established to solve the gravity direction vector in the coordinate system of the laser tracker at the measuring point using least squares fitting coordinate series. Multi-GNSS observations are solved for obtaining the precise geodetic cartesian coordinates of the measuring point and GNSS baseline information. A direction transformation method based on the baseline information proposed in this paper is used to convert the gravitational direction vector in the laser tracker coordinate system into the geodetic cartesian coordinate system. The geodetic cartesian coordinates of the measuring point are used to calculate the ellipsoid normal vector, and the angle between this and the gravity direction vector in the geodetic cartesian coordinate system is estimated to obtain the astrogeodetic DOV. The DOV is projected to the meridian and prime vertical planes to obtain the meridian and prime vertical components of the DOV, respectively. The astronomical latitude and longitude of the measuring point are calculated from these two components. The simulation experiments were carried out using the proposed method, and it was found that the theoretical precision of the DOV measured by the method could reach 0.2″, which could realise all-weather observation.

This study utilizes a low-cost Global Navigation Satellite System (GNSS) buoy platform, combined with multi-system GNSS data, to investigate the impact of GNSS signal quality and multipath effects on the accuracy of atmospheric precipitable water vapor (PWV) retrievals. It also explores the methods for oceanic rainfall event forecasting and precipitation prediction based on GNSS-PWV. By analyzing the data quality from various GNSS systems and using the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 dataset as a reference, the study assesses the accuracy of PWV retrievals in dynamic marine environments. The results show that the GNSS-derived PWV from the buoy platform is highly consistent with ERA5 data in both trend and characteristics, with an RMSE of 3.8 mm for the difference between GNSS-derived PWV and ERA5 PWV. To enhance rainfall forecasting accuracy, a balanced threshold selection (BTS) method is proposed, significantly improving the balance between the probability of detection (POD) and false alarm rate (FAR). Furthermore, a Random Forest model based on multiple meteorological parameters optimizes precipitation forecasting, especially in reducing false alarms. Additionally, a particle swarm optimization (PSO)-based BP Neural Network model for rainfall prediction achieves high precision, with an R² of 97.8%, an average absolute error of 0.08 mm, and an RMSE of 0.1 mm. The findings demonstrate the potential of low-cost GNSS buoy for monitoring atmospheric water vapor and short-term rainfall forecasting in dynamic marine environments.

This work introduces a novel method for estimating hydrological loading displacement using 3D Convolutional Neural Networks (3D-CNN). This approach utilizes vertical displacement time series data from 41 Global Navigation Satellite System (GNSS) stations across Yunnan Province, China, and its adjacent areas, coupled with spatiotemporal variations in terrestrial water storage derived from the Gravity Recovery and Climate Experiment satellites (GRACE). The 3D-CNN method demonstrates markedly higher inversion precision compared to conventional load Green’s function inversion techniques. This improvement is evidenced by substantial reductions in deviations from GNSS observations across various statistical metrics: the maximum deviation decreased by 1.34 millimeters, the absolute minimum deviation by 1.47 millimeters, the absolute mean deviation by 79.6%, and the standard deviation by 31.4%. An in-depth analysis of terrestrial water storage and loading displacement from 2019 to 2022 in Yunnan Province revealed distinct seasonal fluctuations, primarily driven by dominant annual and semi-annual cycles, and these periodic signals accounted for over 90% of the variance. The spatial distribution of terrestrial water loading displacement is strongly associated with regional precipitation patterns, showing smaller amplitudes in the northeast and northwest and larger amplitudes in the southwest. The research findings presented in this paper offer a novel perspective on the spatiotemporal variations of environmental load effects, particularly those related to the terrestrial water loading deformation with significant spatial heterogeneity. Accurate assessment of the effects of terrestrial water loading displacement (TWLD) is of considerable importance for precise geodetic observations, as well as for the establishment and maintenance of high-precision dynamic reference frames. Furthermore, the development of TWLD model that integrates GRACE and GNSS data provides valuable data support for the higher-precision inversion of changes in terrestrial water storage.