<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>On seanonal timescale, the variation of Earth rotation is mainly regulated by angular momentum exchanges between the solid Earth and the fluidal atmosphere, ocean and hydrosphere. In the 2nd EOP PCC, we developed Dill2019’s method for polar motion prediction, using piecewise autoagressive parameters. The maximum prediction errors within 90 days are 36 and 16 mas for polar motion <italic>x</italic> and <italic>y</italic> components, respectively. Compared with Bulletin A, the mean absolute error of polar motion <italic>y</italic> prediction is improved by 20% in all timescale, and with a maximum improvement of 49% on the 5th day. Whereas, for polar motion <italic>x</italic>, the performance is slightly better (2% - 8%) within 30 days but worse (−7%~ −19%) within 30~90 days. We found that the prediction accuracy is very sensitive to the quality of the angular momentum data. For example, on average, the prediction of polar motion <italic>y</italic> is around 2 times better than polar motion <italic>x</italic>. In addition, we found the accuracy of 30-90 days prediction is dramatically decreased in the year 2020. We suspect that such deterioration might be due to the pandemic of coronavirus COVID-19, which suppressed global airline activities by more than 60%, then result in a lose of air-borne meteorological data, which are important for weather forecast.</p>
</abstract>

On seanonal timescale, the variation of Earth rotation is mainly regulated by angular momentum exchanges between the solid Earth and the fluidal atmosphere, ocean and hydrosphere. In the 2nd EOP PCC, we developed Dill2019’s method for polar motion prediction, using piecewise autoagressive parameters. The maximum prediction errors within 90 days are 36 and 16 mas for polar motion x and y components, respectively. Compared with Bulletin A, the mean absolute error of polar motion y prediction is improved by 20% in all timescale, and with a maximum improvement of 49% on the 5th day. Whereas, for polar motion x, the performance is slightly better (2% - 8%) within 30 days but worse (−7%~ −19%) within 30~90 days. We found that the prediction accuracy is very sensitive to the quality of the angular momentum data. For example, on average, the prediction of polar motion y is around 2 times better than polar motion x. In addition, we found the accuracy of 30-90 days prediction is dramatically decreased in the year 2020. We suspect that such deterioration might be due to the pandemic of coronavirus COVID-19, which suppressed global airline activities by more than 60%, then result in a lose of air-borne meteorological data, which are important for weather forecast.

Improved Prediction of Polar Motions by Piecewise Parameterization

Artificial Satellites

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Improved Prediction of Polar Motions by Piecewise Parameterization"}

On seanonal timescale, the variation of Earth rotation is mainly regulated by angular momentum exchanges between the solid Earth and the fluidal atmosphere,...

Improved Prediction of Polar Motions by Piecewise Parameterization

Data publikacji: 05 sty 2023

Zakres stron: 290 - 299

Otrzymano: 01 lip 2022

Przyjęty: 09 gru 2022

DOI: https://doi.org/10.2478/arsa-2022-0025

Słowa kluczowe
Earth rotation prediction, Effective angular momentum functions, AAM, OAM

© 2022 Yuanwei Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Improved Prediction of Polar Motions by Piecewise Parameterization

Data publikacji: 05 sty 2023

Zakres stron: 290 - 299

Otrzymano: 01 lip 2022

Przyjęty: 09 gru 2022

DOI: https://doi.org/10.2478/arsa-2022-0025

Słowa kluczoweEarth rotation prediction, Effective angular momentum functions, AAM, OAM

© 2022 Yuanwei Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Earth rotation prediction, Effective angular momentum functions, AAM, OAM