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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

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Artificial Satellites
Proceedings of the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) Workshop, online, February 15-16, 2022

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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

ERP Slope (a / CI) Intercept (b / CI) [“ / s] Decision
IERS-CODE PMx

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-IGS PMx

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-GFZ PMx

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

ERP Mean difference [“ / s] CI lower limit [“ / s] CI upper limit [“ / s] Decision
IERS-CODE PMx -0,00002657 -0,00002828 -0,00002486 Reject
PMy -0,00000364 -0,00000493 -0,00000234 Reject
LOD -0.00000790 -0.00000829 -0.00000751 Reject
IERS-IGS PMx -0,00001092 -0,00001240 -0,00000945 Reject
PMy -0,00000896 -0,00000996 -0,00000796 Reject
LOD 0.00000006 -0.00000021 0.00000034 Accept
IERS-GFZ PMx -0,00000536 -0,00000719 -0,00000353 Reject
PMy -0,00002593 -0,00002780 -0,00002406 Reject
LOD -0.00001515 -0.00001561 -0.00001468 Reject

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

ERP Slope Intercept [“ / s] Decision
IERS-CODE PMx

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-IGS PMx

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-GFZ PMx

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
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