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Abstract Surface loading effects related to atmospheric, hydrological, non-tidal ocean, are one of the principal sources of the seasonal oscillations in GNSS time series, and it should be taken into account for improving GNSS accuracy. In this study, the daily vertical time series of 9 GNSS stations at Hong Kong was used to investigate the surface loading (sum of atmospheric loading, hydrological loading, non-tidal ocean loading (AHNL)) contributors of seasonal oscillations in GNSS observations. This paper reveals a correlation between the AHNL deformation and the GNSS vertical time series, with an average correlation coefficient of 0.5. The GNSS vertical time series and the corresponding AHNL deformation at all stations exhibit identical amplitudes and phases. The average root mean square (RMS) reduction is 15% at all stations after removing the AHNL deformation from the GNSS vertical time series, implying that AHNL may contribute to non-linear fluctuations in GNSS observations at Hong Kong. Furthermore, the independent component analysis (ICA) method was performed to extract periodic signals from the GNSS time series. ICA method can effectively separate the seasonal signals related to AHNL, the seasonal signals show a strong correlation with AHNL deformations, with Lin correlation coefficients above 0.6. Finally, we carried out cross wavelet transform (XWT) method to quantitatively express the annual phase relationship between GNSS vertical time series and AHNL deformation. The XWT result shows AHNL mainly contribute to the annual oscillation in GNSS observations.

Abstract GNSS time series prediction plays a crucial role in monitoring crustal plate movements, dam deformation, and maintaining the global coordinate framework. To address the shortcomings of traditional GNSS time series prediction methods including insufficient feature selection, limited stability, and low predictive accuracy, this paper proposes a prediction model that combines the Ensemble Empirical Mode Decomposition (EEMD) with Long Short-Term Memory (LSTM) algorithm. The model utilizes the EEMD method to obtain reconstructed signals, which are then input as features into the LSTM model to predict the original time series. To validate the performance of the EEMD-LSTM combined model in GNSS time series prediction, experiments are conducted using time series data from 10 GNSS observation stations. The experimental results demonstrate that the EEMD model can accurately and effectively extract data features. Compared with the EMD-LSTM model, the EEMD-LSTM model achieves an average reduction of 28.32% in RMSE values, an average reduction of 28.52% in MAE values, and an average increase of 24.24% in R2 values. The EEMD-LSTM model exhibits higher predictive accuracy and a strong correlation with the original time series, thus demonstrating its capability to forecast target time series effectively. Therefore, this prediction model holds significant application value in GNSS time series prediction.

To solve the problems of difficult to model parameter selections, useful signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition and long short-term memory (GVLSTM). Firstly, we used the grey wolf optimization (GWO) algorithm to optimize the parameters of variable mode decomposition (VMD), obtaining the optimal parameter combination. Secondly, we used multiscale permutation entropy (MPE) as a standard to select signal screening, determining and recon-structing the effective modal components. Finally, the long short-term memory neural network (LSTM) was used to learn the dam deformation characteristics. The result shows that the GVLSTM model can effectively reduce the estimation deviation of the prediction model. Compared with VMDGRU and VMDANN, the average RMSE and MAE value of each station is increased by 19.11%~28.58% and 27.66%~29.63%, respectively. We used determination (R2) coefficient to judge the performance of the prediction model, and the value of R2 was 0.95~0.97, indicating that our method has good performance in predicting dam deformation. The proposed method has outstanding advantages of high accuracy, reliability, and stability for dam deformation prediction.

The analysis of the Global NFavigation Satellite System (GNSS) time series provides valuable information for geodesy and geodynamics researcFh. Precise data analysis strategies are crucial for accurately obtaining the linear velocity of GNSS stations, enabling high-precision applications of GNSS time series. This study investigates the impact of different stochastic noise models on velocity estimations derived from GNSS time series, specifically under conditions of environmental loading correction and common mode error (CME) removal. By comparing data from various data centers, we find that post-correction, different analysis strategies exhibit high consistency in their noise characteristics and velocity estimation results. Across various analysis strategies, the optimal noise models were predominantly Power Law with White Noise (PLWN) and Fractional Noise with White Noise (FNWN), with the optimal noise models including COMB/JPL, COMB/SOPAC, and COMB/NGL for approximately 50% of the datasets. Most of the stations (approximately 80%) showed velocity differences below 0.3 mm/year and velocity estimation uncertainties below 0.1 mm/year. Nonetheless, variations in amplitudes and periodic signals persisted due to differences in the processing of raw GNSS observations. For instance, the NGL and JPL datasets, which were processed using GipsyX 2.1 software, showed higher amplitudes of the 5.5-day periodic signal. These findings provide a solid empirical foundation for advancing data analysis methods and enhancing the reliability of GNSS time series results in future research.

The expected acceleration in sea level rise (SLR) throughout this century poses significant threats to coastal cities and low-lying regions. Since the early 1990s, high-precision multi-mission satellite altimetry (SA) has enabled the routine measurement of sea levels, providing a continuous 30-year record from which the mean sea level rise (global and regional) and its variability can be computed. The latest reprocessed product from CMEMS span the period from 1993 to 2020, and have enabled the acquisition of accurate sea level data within the coastal range of 0–20 km. In order to fully utilize this new dataset, we establish a global virtual network consisting of 184 virtual SA stations. We evaluate the impact of different stochastic noises on the estimation of the velocity of the sea surface height (SSH) time series using BIC_tp information criterion. In the second step, the principal component analysis (PCA) allows the common mode noise in the SSH time series to be mitigated. Finally, we analyzed the spatiotemporal characteristics and accuracy of sea level change derived from SA. Our results suggest that the stochasticity of the SSH time series is not well described by a combination of random, flicker, and white noise, but is best described by an ARFIM/ARMA/GGM process. After removing the common mode noise with PCA, about 96.7% of the times series’ RMS decreased, and most of the uncertainty associated with the computed SLR decreased. We confirm that the spatiotemporal correlations should be accounted for to yield trustworthy trends and reliable uncertainties. Our estimated SLR is 2.75 ± 0.89 mm/yr, which aligns closely with recent studies, emphasizing the robustness and consistency of our method using virtual SA stations. We additionally introduce open-source software (SA_Tool V1.0) to process the SA data and reduce noise in surface height time series to the community.

Accurate noise model identification for GNSS time series is crucial for obtaining a reliable GNSS velocity field and its uncertainty for various studies in geodynamics and geodesy. Here, by comprehensively considering time span and missing data effect on the noise model of GNSS time series, we used four combined noise models to analyze the duration of the time series (ranging from 2 to 24 years) and the data gap (between 2% and 30%) effects on noise model selection and velocity estimation at 72 GNSS stations spanning from 1992 to 2022 in global region together with simulated data. Our results show that the selected noise model have better convergence when GNSS time series is getting longer. With longer time series, the GNSS velocity uncertainty estimation with different data gaps is more homogenous to a certain order of magnitude. When the GNSS time series length is less than 8 years, it shows that the flicker noise and random walk noise and white noise (FNRWWN), flicker noise and white noise (FNWN), and power law noise and white noise (PLWN) models are wrongly estimated as a Gauss–Markov and white noise (GGMWN) model, which can affect the accuracy of GNSS velocity estimated from GNSS time series. When the GNSS time series length is more than 12 years, the RW noise components are most likely to be detected. As the duration increases, the impact of RW on velocity uncertainty decreases. Finally, we show that the selection of the stochastic noise model and velocity estimation are reliable for a time series with a minimum duration of 12 years.

Optimizing the noise model for global navigation satellite system (GNSS) vertical time series is vital to obtain reliable uplift (or subsidence) deformation velocity fields and assess the associated uncertainties. In this study, by thoroughly considering the effects of hydrological loading (HYDL) that dominates the seasonal fluctuations and common mode error (CME), we analyzed the optimal noise characteristics of GNSS vertical time series at 39 stations spanning from January 2011 to August 2019 in the Chuandian region, southeast of the Qinghai–Tibet Plateau. Our results showed that the optimal noise models without HYDL correction were white noise plus flicker noise (WN + FN), white noise plus power law noise (WN + PL), and white noise plus Gauss–Markov noise (WN + GGM), which accounted for 87%, 10%, and 3% of GNSS stations, respectively. By contrast, the optimal noise models at all stations were WN + FN and WN + PL after correction by different HYDLs. The correlation between CME and HYDL provided by the School and Observatory of Earth Sciences (EOST), namely EOST_HYDL, was 0.63~0.8 and the value of RMS reduction was 18.9~40.3% after removing EOST_HYDL time series from the CME, with a mean value of 31.8%, there is a good correlation and consistency between CME and EOST_HYDL. The absolute value of vertical velocity and its uncertainty with and without EOST_HYDL correction varied from 0.11 to 0.55 mm/a and 0 to 0.23 mm/a, respectively, implying that the effect of HYDL should not be neglected when performing optimal noise model analysis for GNSS vertical time series in the Chuandian region.

In view of the fact that there is no unified understanding of the GNSS horizontal velocity field in the Beijing Plain and the serious land subsidence in this area, we collected GNSS data from 2011 to 2021 and Sentinel 1A data from 2017 to 2021 and conducted high-precision GNSS data processing and PS-InSAR verification in order to determine the reason for the differences in the GNSS horizontal velocity field in the Beijing Plain. The results show that, under the stable Eurasian framework, the horizontal velocity of GNSS stations in the Beijing Plain is significantly inconsistent. The velocity of all GNSS stations ranged from −1.32 to 10.41 mm/yr in the E component and from −8.83 to 3.00 mm/yr in the N component. From 2011 to 2021, there was significant uneven land subsidence in the Beijing Plain, and the maximum land subsidence rate from 2017 to 2021 reached 107 mm/yr. In analyzing the observation data of the GNSS and InSAR, we conclude that the land subsidence in the Beijing Plain will indeed affect the GNSS horizontal velocity field in the subsidence area. Under the EURA_I08 reference framework, the horizontal deformation field in the Beijing Plain is mainly caused by the tectonic activity-derived overall SEE-direction movement, accompanied by the velocity field anomaly caused by local land subsidence.

Seasonal variations in the vertical Global Positioning System (GPS) time series are mainly caused by environmental loading, e.g., hydrological loading (HYDL), atmospheric loading (ATML), and nontidal oceanic loading (NTOL), which can be synthesized based on models developed by various institutions. A comprehensive comparison among these models is essential to extract reliable vertical deformation data, especially on a regional scale. In this study, we selected 4 HYDL, 5 ATML, 2 NTOL, and their 40 combined products to investigate their effects on seasonal variations in vertical GPS time series at 27 GPS stations in Yunnan, southwest China. These products were provided by the German Research Center for Geosciences (GFZ), School and Observatory of Earth Sciences (EOST), and International Mass Loading Service (IMLS). Furthermore, we used the Cross Wavelet Transform (XWT) method to analyze the relative phase relationship between the GPS and the environmental loading time series. Our result showed that the largest average Root-Mean-Square (RMS) reduction value was 1.32 mm after removing the deformation associated with 4 HYDL from the vertical GPS time series, whereas the RMS reductions after 5 ATML and 2 NTOL model corrections were negative at most stations in Yunnan. The average RMS reduction value of the optimal combination of environmental loading products was 1.24 mm, which was worse than the HYDL (IMLS_GEOSFPIT)-only correction, indicating that HYDL was the main factor responding for seasonal variations at most stations in Yunnan. The XWT result showed that HYDL also explained the annual variations reasonably. Our finding implies that HYDL (IMLS_GEOSFPIT) contributes the most to the environmental loading in Yunnan, and that the ATML and NTOL models used in this paper cannot be effective to correct seasonal variations.

Estimates and projections of sea level change are critical for coastal areas. In this work, we utilize satellite altimetry (SA) and tide gauge (TG) technologies to estimate variations in sea level, and we also evaluate the consistency of sea level changes obtained using TG and SA from 1993 to 2020. Additionally, we use deep learning models (artificial neural network (ANN), gated recurrent unit (GRU), and long short-term memory (LSTM)) to forecast sea level changes with SA time series. Our results reveal that the average absolute sea level (ASL) rate in the China Seas and the neighboring ocean based on SA is 3.55 mm/yr, which is higher than the global rate of 3.30 mm/yr. Specifically, the ASL rates of East China Sea and South China Sea are 3.21 mm/yr and 4.24 mm/yr, respectively. The sea level change in the South China Sea is significantly greater than that in the East China Sea. Secondly, the relative sea level (RSL) rate based on TGs is 3.88 mm/yr. We perform VLM correction on TGs with co-located GNSS following the method of Zhou et al. (2022) and obtain a TG-based ASL result of 3.77 mm/yr. Our results show that there is good consistency between coastal sea level changes estimated using tide gauges and satellite radar altimetry. Finally, we use the ANN, GRU, and LSTM models to predict sea level change with SA. The results show that LSTM's prediction accuracy is better than that of the other models, with average RMSE, MAE, and R² values of 48.92 mm, 35.99 mm, and 0.85, respectively. [ABSTRACT FROM AUTHOR]

Sea-level rise directly caused by climate change is impacting coasts around the world and lowlying islands, requiring a continuous accurate monitoring. We analyze the sea-level data observed by 20 tide gauges located in the east coast of the United States of America (USA) over the period January 1972 to December 2021 by using an open-source toolbox SLR_APP. After mitigating noise using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) method, we estimate the trend change and its uncertainty of sea-level considering the stochastic noise properties of the observations. The sea-level estimates and associated uncertainty are smaller than the raw observations after the noise reduction. Our results show that: the average values of the absolute trend change are 1.51 % and 0.82 %, and the mean trend uncertainty are reduced by 44.78 % and 21.26 % after PCA and ICA noise reduction, respectively. We conclude that PCA method performs better than ICA especially in reducing the associated trend uncertainty of the sea level change. Improving the sea-level rise estimation and prediction contribute globally to enhance public safety, in particular for the coastal communities. [ABSTRACT FROM AUTHOR]

BackgroundHemorrhagic fever with renal syndrome (HFRS) is a severe public health problem in Jiangxi province, China. Previous studies reported genetic variants of Orthohantavirus hantanense (Hantaan virus, HTNV) in rodents in this area. However, the relationship between HTNV variants and human infection needs to be confirmed. This study aimed to identify the HTNV variants in patients and to understand the clinical characteristics of HFRS caused by these variants.MethodsSamples were collected from hospitalized suspected cases of HFRS during the acute phase. HFRS cases were confirmed using quantitative real-time RT-PCR. Peripheral blood mononuclear cells (PBMC) from patients with HFRS were inoculated into Vero-E6 cells for viral isolation. The genomic sequences of HTNV from patients were obtained by amplicon-based next-generation sequencing. A retrospective analysis was conducted on the clinical characteristics of the patients.ResultsHTNV RNA was detected in 53 of 183 suspected HFRS patients. Thirteen HTNVs were isolated from 32 PBMCs of HFRS cases. Whole genome sequences of 14 HTNVs were obtained, including 13 isolates in cell culture from 13 patients, and one from plasma of the fatal case which was not isolated successfully in cell culture. Genetic analysis revealed that the HTNV sequence from the 14 patients showed significant variations in nucleotide and amino acid to the HTNV strains found in other areas. Fever (100%, 53/53), thrombocytopenia (100%, 53/53), increased serum aspartate aminotransferase (100%, 53/53), and increased lactate dehydrogenase (96.2%, 51/53) were the most common characteristics. Severe acute kidney injury was observed in 13.2% (7/53) of cases. Clinical symptoms, such as pain, petechiae, and gastrointestinal or respiratory symptoms were uncommon.ConclusionThe HTNV genetic variants cause human infections in Jiangxi. The clinical symptoms of HFRS caused by the HTNV genetic variant during the acute phase are atypical. In addition to renal dysfunction, attention should be paid to the common liver injuries caused by these genetic variants.