Weighted empirical mode decomposition for processing GNSS position time series with the consideration of formal errors

AbstractMost of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.&#160;Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought

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Detecting Slow-Slip Events in GNSS Position Time Series Using Relative Strength Index

Lecture Notes in Electrical Engineering - China Satellite Navigation Conference (CSNC) 2019 Proceedings ◽

10.1007/978-981-13-7751-8_9 ◽

2019 ◽

pp. 82-97

Author(s):

Jiugang Xie ◽

Cuilin Kuang ◽

Hongke Hou ◽

Xiaotao Bai

Keyword(s):

Time Series ◽

Relative Strength ◽

Slow Slip ◽

Strength Index ◽

Slow Slip Events ◽

Position Time Series ◽

Gnss Position Time Series

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Extracting Common Mode Errors of Regional GNSS Position Time Series in the Presence of Missing Data by Variational Bayesian Principal Component Analysis

Sensors ◽

10.3390/s20082298 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2298 ◽

Cited By ~ 2

Author(s):

Wudong Li ◽

Weiping Jiang ◽

Zhao Li ◽

Hua Chen ◽

Qusen Chen ◽

...

Keyword(s):

Principal Component Analysis ◽

Time Series ◽

Missing Data ◽

Principal Component ◽

Component Analysis ◽

Common Mode ◽

Variational Bayesian ◽

North East ◽

Position Time Series ◽

Gnss Position Time Series

Removal of the common mode error (CME) is very important for the investigation of global navigation satellite systems’ (GNSS) error and the estimation of an accurate GNSS velocity field for geodynamic applications. The commonly used spatiotemporal filtering methods normally process the evenly spaced time series without missing data. In this article, we present the variational Bayesian principal component analysis (VBPCA) to estimate and extract CME from the incomplete GNSS position time series. The VBPCA method can naturally handle missing data in the Bayesian framework and utilizes the variational expectation-maximization iterative algorithm to search each principal subspace. Moreover, it could automatically select the optimal number of principal components for data reconstruction and avoid the overfitting problem. To evaluate the performance of the VBPCA algorithm for extracting CME, 44 continuous GNSS stations located in Southern California were selected. Compared to previous approaches, VBPCA could achieve better performance with lower CME relative errors when more missing data exists. Since the first principal component (PC) extracted by VBPCA is remarkably larger than the other components, and its corresponding spatial response presents nearly uniform distribution, we only use the first PC and its eigenvector to reconstruct the CME for each station. After filtering out CME, the interstation correlation coefficients are significantly reduced from 0.43, 0.46, and 0.38 to 0.11, 0.10, and 0.08, for the north, east, and up (NEU) components, respectively. The root mean square (RMS) values of the residual time series and the colored noise amplitudes for the NEU components are also greatly suppressed, with average reductions of 27.11%, 28.15%, and 23.28% for the former, and 49.90%, 54.56%, and 49.75% for the latter. Moreover, the velocity estimates are more reliable and precise after removing CME, with average uncertainty reductions of 51.95%, 57.31%, and 49.92% for the NEU components, respectively. All these results indicate that the VBPCA method is an alternative and efficient way to extract CME from regional GNSS position time series in the presence of missing data. Further work is still required to consider the effect of formal errors on the CME extraction during the VBPCA implementation.

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Application of MEEMD-ARIMA combining model for annual runoff prediction in the Lower Yellow River

Journal of Water and Climate Change ◽

10.2166/wcc.2019.271 ◽

2019 ◽

Vol 11 (3) ◽

pp. 865-876 ◽

Cited By ~ 1

Author(s):

Xianqi Zhang ◽

Wei Tuo ◽

Chao Song

Keyword(s):

Time Series ◽

Empirical Mode Decomposition ◽

Yellow River ◽

Arima Model ◽

Ensemble Empirical Mode Decomposition ◽

Annual Runoff ◽

Lower Yellow River ◽

The Lower Yellow River ◽

Mode Decomposition ◽

Runoff Prediction

Abstract The prediction of annual runoff in the Lower Yellow River can provide an important theoretical basis for effective reservoir management, flood control and disaster reduction, river and beach management, rational utilization of regional water and sediment resources. To solve this problem and improve the prediction accuracy, permutation entropy (PE) was used to extract the pseudo-components of modified ensemble empirical mode decomposition (MEEMD) to decompose time series to reduce the non-stationarity of time series. However, the pseudo-component was disordered and difficult to predict, therefore, the pseudo-component was decomposed by ensemble empirical mode decomposition (EEMD). Then, intrinsic mode functions (IMFs) and trend were predicted by autoregressive integrated moving average (ARIMA) which has strong ability of approximation to stationary series. A new coupling model based on MEEMD-ARIMA was constructed and applied to runoff prediction in the Lower Yellow River. The results showed that the model had higher accuracy and was superior to the CEEMD-ARIMA model or EEMD-ARIMA model. Therefore, it can provide a new idea and method for annual runoff prediction.

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