A technique to improve the accuracy of Earth orientation prediction algorithms based on least squares extrapolation

Guo, Jing; Li, Y.B.; Chang-lei, Dai; Shum, C. K.

doi:10.1016/j.jog.2013.06.002

Cited by 22 publications

(9 citation statements)

References 32 publications

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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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Section: Introductionmentioning

confidence: 99%

Earth Rotation Parameters Prediction and Climate Change Indicators in it

Zhou

2022

Artificial Satellites

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“…The ERP prediction is performed by means of the development and application of algorithms such as the least squares extrapolation (LS) of harmonic model and autoregressive (AR) prediction 25 – 28 spectral analysis and least squares extrapolation 29 , 30 , artificial neural networks (ANN) 31 , 32 , least squares collocation (LSC) 33 , wavelet decomposition and auto-covariance prediction 34 , Kalman filter forecasts 35 , 36 and fuzzy inference system 37 , among others.…”

Section: Introductionmentioning

confidence: 99%

A new method to improve the prediction of the celestial pole offsets

Belda

Ferrándiz

Heinkelmann

et al. 2018

Sci Rep

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Knowledge of the Earth’s changing rotation is fundamental to positioning objects in space and on the planet. Nowadays, the Earth’s orientation in space is expressed by five Earth Orientation Parameters (EOP). Many applications in astronomy, geosciences, and space missions require accurate EOP predictions. Operational predictions are released daily by the Rapid Service/Prediction Centre of the International Earth Rotation and Reference Systems Service (IERS). The prediction procedures and performances differ for the three EOP classes: polar motion, rotation angle (UT1-UTC), and the two celestial pole offsets (CPO), dX and dY. The IERS Annual Report 2016 shows Rapid Service CPO predictions errors with respect to IERS 08 C04 observations in 2016 ranging from 120 to 140 μas in 40 days for dX, and 100–160 μas for dY. We test a new method for the CPO prediction based on the recent availability of sophisticated empirical models for the Free Core Nutation, a main component of the CPO variations. We found it allows predicting both CPO with error estimates for the period 2000–2016 lower than the 2016 Rapid Service products, reaching about 85 μas after 40 days and near 90 μas after a year. These results would represent a 35–40% improvement.

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“…The purposes of these activities were to encourage scholars worldwide to utilize different methods to predict EOPs and to assess the forecasting accuracy and applicability of various prediction methods. The prediction methods can be divided into two categories, specifically single models (including both linear and nonlinear models) and combinations of multi models (Freedman et al 1994;Schuh et al 2002;Kosek et al 2004Kosek et al , 2008Xu 2012;Guo et al 2013;Xu and Zhou 2015). One major conclusion that was reached as a result of these competitions is that no single prediction technique is suitable for all EOPs over their entire ranges of variation.…”

Section: Introductionmentioning

confidence: 99%