Spatio-temporal filtering for determination of common mode error in regional GNSS networks

AbstractThe spatial correlation between different stations for individual components in the regional GNSS networks seems to be significant. The mismodelling in satellite orbits, the Earth orientation parameters (EOP), largescale atmospheric effects or satellite antenna phase centre corrections can all cause the regionally correlated errors. This kind of GPS time series errors are referred to as common mode errors (CMEs). They are usually estimated with the regional spatial filtering, such as the "stacking". In this paper, we show the stacking approach for the set of ASG-EUPOS permanent stations, assuming that spatial distribution of the CME is uniform over the whole region of Poland (more than 600 km extent). The ASG-EUPOS is a multifunctional precise positioning system based on the reference network designed for Poland. We used a 5- year span time series (2008-2012) of daily solutions in the ITRF2008 from Bernese 5.0 processed by the Military University of Technology EPN Local Analysis Centre (MUT LAC). At the beginning of our analyses concerning spatial dependencies, the correlation coefficients between each pair of the stations in the GNSS network were calculated. This analysis shows that spatio-temporal behaviour of the GPS-derived time series is not purely random, but there is the evident uniform spatial response. In order to quantify the influence of filtering using CME, the norms L1 and L2 were determined. The values of these norms were calculated for the North, East and Up components twice: before performing the filtration and after stacking. The observed reduction of the L1 and L2 norms was up to 30% depending on the dimension of the network. However, the question how to define an optimal size of CME-analysed subnetwork remains unanswered in this research, due to the fact that our network is not extended enough.

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Evaluating Short-Term Spatio-Temporal Tropospheric Variability in Multi-Temporal SAR Interferograms Using LES Models

10.31223/x5dc8z ◽

2021 ◽

Author(s):

Fengming Hu ◽

Ramon Hanssen ◽

Pier Siebesma ◽

Kevin Helfer

Keyword(s):

Time Series ◽

Weather Condition ◽

Deformation Monitoring ◽

Sar Interferometry ◽

Integration Time ◽

Tropospheric Delay ◽

Atmospheric Effects ◽

Phase Gradient ◽

Weather Situation ◽

Spatio Temporal

Atmospheric delay has a significant impact on synthetic aperture radar (SAR) interferometry, inducing spatial phase errors and decorrelation in extreme weather condition. For Low Earth Orbit (LEO) SAR missions, the atmosphere can be considered as being spatio-temporally frozen due to the short integration time. Geosynchronous (GEO) SAR missions, however, have short revisit times and extensive imaging coverage but with a longer integration time. As a result, GEOSAR interferograms can provide continuous deformation monitoring and integrated refractivity for weather forecasting. However, as the troposphere may vary significantly within the integration time, this may lead to a degradation during focusing and decorrelation of the InSAR pair. Here we simulate a time-series refractivity distribution with a high spatio-temporal resolution, for a fair-weather situation using an advanced Large Eddy Simulation (LES) model, to show the spatio-temporal variability of the troposphere on short time scales. Given GEO orbit parameters with different viewing angles along both azimuth and range directions, corresponding time-series of tropospheric interferograms are obtained based on the SAR geometry, and the impacts of different parameters are compared. Tropospheric delay is found to vary rapidly and a lead to phase gradient exceeding one cycle within a few minutes. Yet, for periods of less than ~15 minutes, a frozen-flow approximation may be successful to mitigate atmospheric decorrelation. Consequently, GEOSAR imaging should be iterative to compensate the atmospheric effects.

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Potential Contributors to Common Mode Error in Array GPS Displacement Fields in Taiwan Island

Remote Sensing ◽

10.3390/rs13214221 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4221

Author(s):

Xiaojun Ma ◽

Bin Liu ◽

Wujiao Dai ◽

Cuilin Kuang ◽

Xuemin Xing

Keyword(s):

Time Series ◽

Component Analysis ◽

Mass Loading ◽

Common Mode ◽

Temporal Filtering ◽

Coordinate Time ◽

Coordinate Time Series ◽

Common Mode Error ◽

Taiwan Island ◽

Spatio Temporal

The existence of the common mode error (CME) in the continuous global navigation satellite system (GNSS) coordinate time series affects geophysical studies that use GNSS observations. To understand the potential contributors of CME in GNSS networks in Taiwan and their effect on velocity estimations, we used the principal component analysis (PCA) and independent component analysis (ICA) to filter the vertical coordinate time series from 44 high-quality GNSS stations in Taiwan island in China, with a span of 10 years. The filtering effects have been evaluated and the potential causes of the CME are analyzed. The root-mean-square values decreased by approximately 14% and 17% after spatio-temporal filtering using PCA and ICA, respectively. We then discuss the relationship between the CME sources obtained by ICA and the environmental loads. The results reveal that the independent displacements extracted by ICA correlate with the atmospheric mass loading (ATML) and land water storage mass loading (LWS) of Taiwan in terms of both its amplitude and phase. We then use the white noise plus power law noise model to quantitatively estimate the noise characteristics of the pre- and post-filtered coordinate time series based on the maximum likelihood estimation criterion. The results indicate that spatio-temporal filtering reduces the amplitude of the PL and the periodic terms in the GPS time series.

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Evolving Fuzzy Time Series for Spatio-temporal Forecasting in Renewable Energy Systems

Renewable Energy ◽

10.1016/j.renene.2021.02.117 ◽

2021 ◽

Author(s):

Carlos A. Severiano ◽

Petrônio de Cândido de Lima e Silva ◽

Miri Weiss Cohen ◽

Frederico Gadelha Guimarães

Keyword(s):

Time Series ◽

Renewable Energy ◽

Energy Systems ◽

Fuzzy Time Series ◽

Renewable Energy Systems ◽

Spatio Temporal

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Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

Remote Sensing ◽

10.3390/rs13112174 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2174

Author(s):

Lijian Shi ◽

Sen Liu ◽

Yingni Shi ◽

Xue Ao ◽

Bin Zou ◽

...

Keyword(s):

Time Series ◽

Sea Ice ◽

Ocean Circulation ◽

The Arctic ◽

Retrieval Algorithm ◽

Atmospheric Effects ◽

Observation Data ◽

Long Time Series ◽

Ice Concentration ◽

Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

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A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System

Energies ◽

10.3390/en14113299 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3299

Author(s):

Ashish Shrestha ◽

Bishal Ghimire ◽

Francisco Gonzalez-Longatt

Keyword(s):

Time Series ◽

Kinetic Energy ◽

Power System ◽

Bayesian Model ◽

Arima Model ◽

Power Converter ◽

Series Data ◽

Percentage Error ◽

Optimal Size ◽

Short Term

Withthe massive penetration of electronic power converter (EPC)-based technologies, numerous issues are being noticed in the modern power system that may directly affect system dynamics and operational security. The estimation of system performance parameters is especially important for transmission system operators (TSOs) in order to operate a power system securely. This paper presents a Bayesian model to forecast short-term kinetic energy time series data for a power system, which can thus help TSOs to operate a respective power system securely. A Markov chain Monte Carlo (MCMC) method used as a No-U-Turn sampler and Stan’s limited-memory Broyden–Fletcher–Goldfarb–Shanno (LM-BFGS) algorithm is used as the optimization method here. The concept of decomposable time series modeling is adopted to analyze the seasonal characteristics of datasets, and numerous performance measurement matrices are used for model validation. Besides, an autoregressive integrated moving average (ARIMA) model is used to compare the results of the presented model. At last, the optimal size of the training dataset is identified, which is required to forecast the 30-min values of the kinetic energy with a low error. In this study, one-year univariate data (1-min resolution) for the integrated Nordic power system (INPS) are used to forecast the kinetic energy for sequences of 30 min (i.e., short-term sequences). Performance evaluation metrics such as the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) of the proposed model are calculated here to be 4.67, 3.865, 0.048, and 8.15, respectively. In addition, the performance matrices can be improved by up to 3.28, 2.67, 0.034, and 5.62, respectively, by increasing MCMC sampling. Similarly, 180.5 h of historic data is sufficient to forecast short-term results for the case study here with an accuracy of 1.54504 for the RMSE.

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Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

Earth Planets and Space ◽

10.1186/s40623-020-01293-0 ◽

2020 ◽

Vol 72 (1) ◽

Author(s):

Masayuki Kano ◽

Shin’ichi Miyazaki ◽

Yoichi Ishikawa ◽

Kazuro Hirahara

Keyword(s):

Time Series ◽

Data Assimilation ◽

Evaluation Method ◽

Temporal Evolution ◽

Recovery Process ◽

Postseismic Deformation ◽

Coseismic Slip ◽

Megathrust Earthquakes ◽

Spatio Temporal ◽

Gnss Time Series

Abstract Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation, but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A–B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

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