Achievements of the Earth orientation parameters prediction comparison campaign

Kalarus, Maciej; Schuh, Harald; Kosek, W.; Akyılmaz, O.; Bizouard, Christian; Gambis, D.; Gross, R. S.; Jovanović, Božidar; Kumakshev, S. A.; Kutterer, Hansjörg; Cerveira, P. J. Mendes; Pasynok, Sergei L.; Zotov, Leonid

doi:10.1007/s00190-010-0387-1

Cited by 113 publications

(86 citation statements)

References 7 publications

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“…Assuming that the mean l of CV errors is l CV ffi 0, andr MAE for an n element set with standard deviation r CV can be calculated with the formula (Kalarus et al 2010; Eq. 8):…”

Section: Statistical and Spatial Analysis Methodsmentioning

confidence: 99%

Spatial Interpolation of Ewert’s Index of Continentality in Poland

Szymanowski

Bednarczyk

Kryza

et al. 2016

Pure Appl. Geophys.

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Abstract-The article presents methodological considerations on the spatial interpolation of Ewert's index of continentality for Poland. The primary objective was to perform spatial interpolation and generate maps of the index combined with selection of an optimal interpolation method and validation of the use of the decision tree proposed by Szymanowski et al. (Meteorol Z 22:577-585, 2013). The analysis involved four selected years and a multi-year average of the period 1981-2010 and was based on data from 111 meteorological stations. Three regression models: multiple linear regression (MLR), geographically weighted regression (GWR), and mixed geographically weighted regression were used in the analysis as well as extensions of two of them to the residual kriging form. The regression models were compared demonstrating a better fit of the local model and, hence, the non-stationarity of the spatial process. However, the decisive role in the selection of the interpolator was assigned to the possibility of extension of the regression model to residual kriging. A key element here is the autocorrelation of the regression residuals, which proved to be significant for MLR and irrelevant for GWR. This resulted in exclusion of geographically weighted regression kriging from further analysis. The multiple linear regression kriging was found as the optimal interpolator. This was confirmed by cross validation combined with an analysis of improvement of the model in accordance with the criterion of the mean absolute error (MAE). The results obtained facilitate modification of the scheme of selection of an optimal interpolator and development of guidelines for automation of interpolation of Ewert's index of continentality for Poland.

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“…Assuming that the mean l of CV errors is l CV ffi 0, andr MAE for an n element set with standard deviation r CV can be calculated with the formula (Kalarus et al 2010; Eq. 8):…”

Section: Statistical and Spatial Analysis Methodsmentioning

confidence: 99%

Spatial Interpolation of Ewert’s Index of Continentality in Poland

Szymanowski

Bednarczyk

Kryza

et al. 2016

Pure Appl. Geophys.

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“…The forecast accuracy of the nutation-precession corrections dψ , dε for 1-year in the future is of the order of their observational error, which is of the order of 100ȝas, because the current IAU2000/IAU2006 nutation-precession model fits very well to the observations (Kalarus et al, 2010). Nevertheless, p x , p y pole coordinates and in particular UT1-UTC vary rapidly and are unpredictable in time.…”

Section: Introductionmentioning

confidence: 93%

“…Therefore, determinations and forecasts of the three parameters are an ongoing challenge. Usually, the prediction accuracy of the three parameters even for a few days in the future are much worse than their observational accuracy, which now corresponds to 50-100ȝas in the case of p x , p y pole coordinates and 5-10ȝs in the case of UT1-UTC (Kalarus et al, 2010). Among these five EOPs, the UT1-UTC or its first derivative, length of day (LOD), which represents the variations of the Earth's rotation rate, is the most difficult to forecast.…”

Section: Introductionmentioning

confidence: 99%

“…In October 2005 the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) was started in an effort to evaluate different methods available for forecasting EOP data under the same rules and conditions. A main conclusion of the EOP PCC is that there is not one particular prediction method superior to the others for all EOP components and all prediction intervals (Kalarus et al, 2010). The campaign has prompted researchers for more activity in this domain to improve the existing prediction approaches (Guo et al, 2013;Xu and Zhou, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

Guo

Zhao

et al. 2016

Artificial Satellites

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ABSTRACT.A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (<10days) predictions of universal time (UT1-UTC). The results of predictions are analyzed and compared with those obtained by other methods. It is shown that the accuracy of the predictions is comparable with that obtained by other prediction methods. The proposed method is able to yield an exact prediction even though only a few observations are provided. Hence it is very valuable in the case of a small size dataset since traditional methods, e.g., least-squares (LS) extrapolation, require longer data span to make a good forecast. In addition, these results can be obtained without making any assumption about an original dataset, and thus is of high reliability. Another advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.

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“…A list of participants who supported the LOD predictions can be found in Kalarus et al (2010). What can be said with the information available from the comparison is that the accuracy of ultra short-term predictions by the GPR is inferior to the prediction accuracy of the number one (Gross et al (1998)) and the number two (Kalarus et al (2010)). For short-term predictions an accuracy is obtained which is inferior to the best presently available prediction method developed by Gross et al (1998).…”

Section: Y U L E I a N D O T H E R S Vol 68mentioning

confidence: 99%

Prediction of Length-of-day Using Gaussian Process Regression

Guo

Cai

et al. 2015

J. Navigation

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The predictions of Length-Of-Day (LOD) are studied by means of Gaussian Process Regression (GPR). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. Firstly, well known effects that can be described by functional models, for example effects of the solid Earth and ocean tides or seasonal atmospheric variations, are removed a priori from the C04 time-series. Only the differences between the modelled and actual LOD, i.e. the irregular and quasi-periodic variations, are employed for training and prediction. Different input patterns are discussed and compared so as to optimise the GPR model. The optimal patterns have been found in terms of the prediction accuracy and efficiency, which conduct the multi-step ahead predictions utilising the formerly predicted values as inputs. Finally, the results of the predictions are analysed and compared with those obtained by other prediction methods. It is shown that the accuracy of the predictions are comparable with that of other prediction methods. The developed method is easy to use. K E Y WO R D S 1. Length-Of-Day (LOD).2. Prediction. 3. Gaussian Process Regression (GPR).

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Achievements of the Earth orientation parameters prediction comparison campaign

Cited by 113 publications

References 7 publications

Spatial Interpolation of Ewert’s Index of Continentality in Poland

Spatial Interpolation of Ewert’s Index of Continentality in Poland

Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

Prediction of Length-of-day Using Gaussian Process Regression

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