One-step method for predicting LOD parameters based on LS+AR model

Wu, Fei; Chang, Guobin; Deng, Kazhong

doi:10.1080/14498596.2019.1618401

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…On the other hand, it is worth noting that EOP 14 C04 and IERS gpsrapid.daily sometimes have increased latency in practice, which will result in a slower performance of the new strategy. However, as long as the two products are released on time, the performance will be ensured Compared to the LS+AR combination model, the other improved prediction models and methods (e.g., [16,17]) can also be used in this new strategy after appropriate modifications, and the performance should be better. However, this was not assessed and verified here, and future works are required.…”

Section: Methods 2 (B)mentioning

confidence: 99%

Improved Ultra-Rapid UT1-UTC Determination and Its Preliminary Impact on GNSS Satellite Ultra-Rapid Orbit Determination

Yuan

Deng

2020

Remote Sensing

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Errors in ultra-rapid UT1-UTC primarily affect the overall rotation of spatial datum expressed by GNSS (Global Navigation Satellite System) satellite ultra-rapid orbit. In terms of existing errors of traditional strategy, e.g., piecewise linear functions, for ultra-rapid UT1-UTC determination, and the requirement to improve the accuracy and consistency of ultra-rapid UT1-UTC, the potential to improve the performance of ultra-rapid UT1-UTC determination based on an LS (Least Square) + AR (Autoregressive) combination model is explored. In this contribution, based on the LS+AR combination model and by making joint post-processing/rapid UT1-UTC observation data, we propose a new strategy for ultra-rapid UT1-UTC determination. The performance of the new strategy is subsequently evaluated using data provided by IGS (International GNSS Services), iGMAS (international GNSS Monitoring and Assessment System), and IERS (International Earth Rotation and Reference Systems Service). Compared to the traditional strategy, the numerical results over more than 1 month show that the new strategy improved ultra-rapid UT1-UTC determination by 29–43%. The new strategy can provide a reference for GNSS data processing to improve the performance of ultra-rapid products.

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Section: Methods 2 (B)mentioning

confidence: 99%

Improved Ultra-Rapid UT1-UTC Determination and Its Preliminary Impact on GNSS Satellite Ultra-Rapid Orbit Determination

Yuan

Deng

2020

Remote Sensing

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“…Various algorithms have been created to improve the accuracy of LOD predictions such as the auto-covariance (AC) [ 10 , 11 ], wavelet decomposition [ 12 ], neural network (NN), [ 4 , 13 , 14 , 15 ], combination of least squares and autoregression (LS+AR), and autoregression moving average (ARMA) algorithms [ 11 , 16 , 17 ], among others. In addition to these examples, other approaches use a direct combination of LOD data and the axial component of the effective angular momentum (EAMz) [ 6 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)

Guessoum

Belda

Ferrándiz

et al. 2022

Sensors

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Accurate Earth orientation parameter (EOP) predictions are needed for many applications, e.g., for the tracking and navigation of interplanetary spacecraft missions. One of the most difficult parameters to forecast is the length of day (LOD), which represents the variation in the Earth’s rotation rate since it is primarily affected by the torques associated with changes in atmospheric circulation. In this study, a new-generation time-series prediction algorithm is developed. The one-dimensional convolutional neural network (1D CNN), which is one of the deep learning methods, is introduced to model and predict the LOD using the IERS EOP 14 C04 and axial Z component of the atmospheric angular momentum (AAM), which was taken from the German Research Centre for Geosciences (GFZ) since it is strongly correlated with the LOD changes. The prediction procedure operates as follows: first, we detrend the LOD and Z-component series using the LS method, then, we obtain the residual series of each one to be used in the 1D CNN prediction algorithm. Finally, we analyze the results before and after introducing the AAM function. The results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict the LOD for up to 7 days.

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“…In recent decades, single or hybrid mathematical models have been employed to EOP predictions, such as the least-square extrapolation (LS) and autoregressive (AR) model (Wu et al 2019;Xu et al 2015), spectral analysis combined with LS (Zotov et al 2018;Guo et al 2013), artificial neural networks (ANN) (Lei et al 2017;Schuh et al 2002), wavelet decomposition and auto-covariance method (Su et al 2014;Kosek et al 2005) and Kalman filter (Xu et al 2012;Gross et al 1998). Considering the contributions of the surface fluid (atmospheric and oceanic angular momentum [AAM and OAM, respectively]), numerous studies have added these geophysical excitations to improve EOP predictions (Modiri et al 2020;Dill et al 2019;Wang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Earth Rotation Parameters Prediction and Climate Change Indicators in it

Zhou

2022

Artificial Satellites

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As one of the participants in the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), we submitted two data files. One is 365 days’ predictions into the future for Earth orientation parameters (EOP) (the position parameters Px and Py, the time parameters UT1-UTC and length of day changes ΔLOD), processed by the traditional least-square and autoregressive (LS + AR) model. Another is 90 days’ predictions by the combined least-square and convolution method (LS + Convolution), with effective angular momentum (EAM) from Earth System Modelling GeoForschungsZentrum in Potsdam (ESMGFZ). Results showed that the LS + Convolution method performed better than the LS + AR model in short-term EOP predictions within 10 days, while the traditional LS + AR model presented higher accuracy in medium-term predictions over 10–90 days. Furthermore, based on the climate change information in Earth’s rotation (mainly in the interannual variations of LOD), the climate change indicators are investigated with ΔLOD observations and long-term predictions. After two intermediate La Nina events were detected in the climate-related ΔLOD observations during the period of 2020–2022, another stronger La Nina phenomenon is indicated in the climate-related ΔLOD long-term predictions.

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One-step method for predicting LOD parameters based on LS+AR model

Cited by 11 publications

References 19 publications

Improved Ultra-Rapid UT1-UTC Determination and Its Preliminary Impact on GNSS Satellite Ultra-Rapid Orbit Determination

Improved Ultra-Rapid UT1-UTC Determination and Its Preliminary Impact on GNSS Satellite Ultra-Rapid Orbit Determination

The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)

Earth Rotation Parameters Prediction and Climate Change Indicators in it

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