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Improved Prediction of Polar Motions by Piecewise Parameterization Cover

Improved Prediction of Polar Motions by Piecewise Parameterization

By: Yuanwei Wu,  Xin Zhao and  Xinyu Yang  
Open Access
|Jan 2023

Figures & Tables

Figure 1.

Flowchart for EOP prediction of Dill2019 method
Flowchart for EOP prediction of Dill2019 method

Figure 2.

The transformed X (left panel) and Y (right panel) components of GAM, ESMGFZ EAM, and differences between GAM and EAM
The transformed X (left panel) and Y (right panel) components of GAM, ESMGFZ EAM, and differences between GAM and EAM

Figure 3.

Correlation between GAM and EAM. Left panel: the X component; right panel: the Y component.
Correlation between GAM and EAM. Left panel: the X component; right panel: the Y component.

Figure 4.

Piecewise continusous least squares fit of GAM-EAM (upper panels), and full GAM series (lower panels), with left panels for x component and right panels for y component. Blue lines denote data, red lines show the LS fittig results, grey lines are the LS fit residuals.
Piecewise continusous least squares fit of GAM-EAM (upper panels), and full GAM series (lower panels), with left panels for x component and right panels for y component. Blue lines denote data, red lines show the LS fittig results, grey lines are the LS fit residuals.

Figure 5.

Mean absolute error (MAE) of prediction of polar motion x (left panels) and y (right panels) for different parameter choices for AR. Upper panels are predictions within 6 days and lower panels are predictions within 7-90 days.
Mean absolute error (MAE) of prediction of polar motion x (left panels) and y (right panels) for different parameter choices for AR. Upper panels are predictions within 6 days and lower panels are predictions within 7-90 days.

Figure 6.

Absolute difference between polar motions series of IERS EOP C04 and our predition up to 90 days
Absolute difference between polar motions series of IERS EOP C04 and our predition up to 90 days

Figure 7.

Comparing the MAE of our 90-day prediction with the MAE of IERS bulletin A 90-day prediction
Comparing the MAE of our 90-day prediction with the MAE of IERS bulletin A 90-day prediction

Best autoregressive parameters for prediction of PMX

Future dayp for PMXp for PMYlag for PMXlag for PMY
1-26060515
3-66060115
7-1018181616
11-13, 37-38, 41-428822
145311
15-20, 24, 28-292311
21-23, 25-27, 304422
31-3451911
35-36,39-406622
43-57, 65-758833
58, 62-6420201818
59-6119191717
75-90101066

Best autoregressive parameters for prediction of PMY

Future dayp for PMXp for PMYlag for PMXlag for PMY
1-6191911
719191717
8-1120201818
126622
13-14,16-67,73-904422
153211
68-728822

PM prediction errors (MAE) at different future days

1 days5 days10 days20 days40 days60 days90 days
PMX forecast of this paper (milli arcsec)0.301.042.744.577.6210.5813.78
PMX forecast of IERS (milli arcsec)0.311.562.934.957.089.5111.57
PMX forecast error reduction (%)2.6232.986.597.74−7.67−11.23−19.07
PMY forecast of this paper (milli arcsec)0.190.541.462.103.313.975.60
PMY forecast of IERS (milli arcsec)0.241.071.762.624.425.287.89
PMY forecast error Reduction (%)20.7748.9817.5320.0525.1324.9728.93
DOI: https://doi.org/10.2478/arsa-2022-0025 | Journal eISSN: 2083-6104 | Journal ISSN: 1509-3859
Language: English
Page range: 290 - 299
Submitted on: Jul 1, 2022
Accepted on: Dec 9, 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 Yuanwei Wu, Xin Zhao, Xinyu Yang, published by Polish Academy of Sciences, Space Research Centre
This work is licensed under the Creative Commons Attribution 4.0 License.