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Prediction of Earth Rotation Parameters with the Use of Rapid Products from IGS, Code and GFZ Data Centres Using Arima and Kriging – A Comparison Cover

Prediction of Earth Rotation Parameters with the Use of Rapid Products from IGS, Code and GFZ Data Centres Using Arima and Kriging – A Comparison

Open Access
|Jan 2023

Figures & Tables

Figure 1.

Spectrogram for LOD
Spectrogram for LOD

Figure 2.

Spectrogram for PMx
Spectrogram for PMx

Figure 3.

Spectrogram for PMy
Spectrogram for PMy

Figure 4.

Diagram of the whole prediction process with rapid products
Diagram of the whole prediction process with rapid products

Figure 5.

Comparison of MAPEs for 15-day PMx prediction for ARIMA and kriging for various analysis centres (CODE, GFZ and IGS denote rapid time series and IERS final time series)
Comparison of MAPEs for 15-day PMx prediction for ARIMA and kriging for various analysis centres (CODE, GFZ and IGS denote rapid time series and IERS final time series)

Figure 6.

Comparison of MAPEs for 15-day PMy prediction for ARIMA and kriging for various analysis centres
Comparison of MAPEs for 15-day PMy prediction for ARIMA and kriging for various analysis centres

Figure 7.

Comparison of MAPEs for 15-day LOD prediction for ARIMA and kriging for various analysis centres
Comparison of MAPEs for 15-day LOD prediction for ARIMA and kriging for various analysis centres

Figure 8.

Comparison of MAPEs for 30-day PMx prediction for ARIMA and kriging for various analysis centres
Comparison of MAPEs for 30-day PMx prediction for ARIMA and kriging for various analysis centres

Figure 9.

Comparison of MAPEs for 30-day PMy prediction for ARIMA and kriging for various analysis centres
Comparison of MAPEs for 30-day PMy prediction for ARIMA and kriging for various analysis centres

Figure 10.

Comparison of MAPEs for 30-day LOD prediction for ARIMA and kriging for various analysis centres
Comparison of MAPEs for 30-day LOD prediction for ARIMA and kriging for various analysis centres

Passing-Bablok regression, final IERS vs rapid CODE, IGS and GFZ ERP products

ERPSlope (a / CI)Intercept (b / CI) [“ / s]Decision
IERS-CODEPMx

1.0000432

(1.00002075; 1.00006572) (R)

0.0000170

(0.00001399; 0.00001988) (R)

Reject
PMy

1.00000000

(0.99997632; 1.00001322) (A)

0.00000200

(-0.00000264; 0.00001059) (A)

Accept
LOD

1.00100267

(1.00045893; 1.00155259) (R)

0.00000759

(0.00000738; 0.00000778) (R)

Reject
IERS-IGSPMx

0.99999126

(0.99997446; 1.00000467) (A)

0.00000545

(0.00000385; 0.00000720) (R)

Reject

(partially satisfied)

PMy

0.99996812

(0.99995422; 0.99998202) (R)

0.00001770

(0.00001268; 0.00002273) (R)

Reject
LOD

1.00029612

(1.00000000; 1.00063857) (A)

-0.00000018

(-0.00000026; -0.00000010) (R)

Reject

(partially satisfied)

IERS-GFZPMx

1.00006766

(1.00004284; 1.00009256) (R)

-0.00000174

(-0.00000476; 0.00000115) (A)

Reject

(partially satisfied)

PMy

1.000052582

(1.00002540; 1.00008007) (R)

0.00000175

(-0.00000836; 0.00001128) (A)

Reject

(partially satisfied)

LOD

0.99948267

(0.99882881; 1.00013364) (A)

0.00001554

(0.00001524; 0.00001582) (R)

Reject

(partially satisfied)

Matched-pair t – test between final IERS and rapid CODE, IGS and GFZ ERP products

ERPMean difference [“ / s]CI lower limit [“ / s]CI upper limit [“ / s]Decision
IERS-CODEPMx-0,00002657-0,00002828-0,00002486Reject
PMy-0,00000364-0,00000493-0,00000234Reject
LOD-0.00000790-0.00000829-0.00000751Reject
IERS-IGSPMx-0,00001092-0,00001240-0,00000945Reject
PMy-0,00000896-0,00000996-0,00000796Reject
LOD0.00000006-0.000000210.00000034Accept
IERS-GFZPMx-0,00000536-0,00000719-0,00000353Reject
PMy-0,00002593-0,00002780-0,00002406Reject
LOD-0.00001515-0.00001561-0.00001468Reject

Deming regression, final IERS vs rapid CODE, IGS and GFZ ERP products

ERPSlopeIntercept [“ / s]Decision
IERS-CODEPMx

1.00003832

t = 3.04479892; p = 0.00235965 (R)

0.00002657

t = 30.52319805; p = 0.00000000 (R)

Reject
PMy

0.99999094

t = -0.89663540; p = 0.37002469 (A)

0.00000364

t = 5.50072407; p = 0.00000004 (R)

Reject

(partially satisfied)

LOD

1.00089118

t = 3.15274029; p = 0.00164218 (R)

0.00000790

t = 39.80528652; p = 0.00000000 (R)

Reject
IERS-IGSPMx

0.99998689

t = -1.17060095; p = 0.24190291 (A)

0.00001092

t = 14.52462344; p = 0.00000000 (R)

Reject

(partially satisfied)

PMy

0.99996457

t = -4.60782629; p = 0.00000433 (R)

0.00000896

t = 17.59538975; p = 0.00000000 (R)

Reject
LOD

1.00020499

t = 1.16628098; p = 0.24364414 (A)

-0.00000006

t = -0.46240588; p = 0.64384211 (A)

Accept
IERS-GFZPMx

1.00008012

t = 5.82033049; p = 0.00000001 (R)

0.00000536

t = 5.74191721; p = 0.00000001 (R)

Reject
PMy

1.00006274

t = 4.07633940; p =0.00004760 (R)

0.00002593

t = 27.24852927; p = 0.00000000 (R)

Reject
LOD

0.99916958

t = -2.62639779; p = 0.00869725 (R)

0.00001515

t = 63.59095306; p = 0.00000000 (R)

Reject
DOI: https://doi.org/10.2478/arsa-2022-0024 | Journal eISSN: 2083-6104 | Journal ISSN: 1509-3859
Language: English
Page range: 274 - 289
Submitted on: Jul 18, 2022
Accepted on: Oct 6, 2022
Published on: Jan 5, 2023
Published by: Polish Academy of Sciences, Space Research Centre
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Maciej Michalczak, Marcin Ligas, Jacek Kudrys, published by Polish Academy of Sciences, Space Research Centre
This work is licensed under the Creative Commons Attribution 4.0 License.