Modeling and forecast of the polar motion excitation functions for short-term polar motion prediction

2004 ◽  
Vol 78 (6) ◽  
pp. 343-353 ◽  
Author(s):  
T.M. Chin ◽  
R.S. Gross ◽  
J.O. Dickey
Keyword(s):  
Polar Motion ◽  
Motion Prediction ◽  
Short Term ◽  
Excitation Functions ◽  
Polar Motion Prediction ◽  
Polar Motion Excitation

Selecting data for autoregressive modeling in polar motion prediction

2019 ◽  
Vol 54 (4) ◽  
pp. 557-566 ◽  
Author(s):  
Fei Wu ◽  
Guobin Chang ◽  
Kazhong Deng ◽  
Wuyong Tao
Keyword(s):  
Polar Motion ◽  
Motion Prediction ◽  
Autoregressive Modeling ◽  
Polar Motion Prediction

Short periodic variations of polar motion and hemispheric atmospheric angular momentum excitation functions in the period 1984-1992

1995 ◽  
Vol 13 (2) ◽  
pp. 217-225 ◽  
Author(s):  
J. Nastula
Keyword(s):  
Angular Momentum ◽  
Excitation Function ◽  
Polar Motion ◽  
Southern Oscillation ◽  
Periodic Variation ◽  
Atmospheric Angular Momentum ◽  
Japan Meteorological Agency ◽  
National Geodetic Survey ◽  
Excitation Functions ◽  
Polar Motion Excitation

Abstract. Short periodic oscillations with the periods from 10 up to 110 days of the hemispheric components of effective atmospheric angular momentum (EAAM) excitation function and their correlation with polar motion excitation function have been analyzed. The EAAM data of the Japan Meteorological Agency (JMA) computed for the two hemispheres and the very long baseline interferometry (VLBI) polar motion NGS 92 R01 data (NGS 1992), determined by the National Geodetic Survey were applied. The distinct oscillations with periods of about 28, 35-55 and 60-80 days were detected in the χy-component of both polar motion excitation function and northern EAAM excitation functions containing wind and pressure, with and without inverted barometric correction terms. The χy-component of the polar motion excitation function is significanly correlated (correlation coefficient equal to 0.55-0.75) with the χy-components of the northern EAAM excitation functions mentioned above, which are mostly induced by the atmospheric circulation over lands. No meaningful correlation between polar motion excitation function and the southern EAAM excitation functions was found. The χx-components of the EAAM and polar motion excitation functions are not significantly correlated. The strong short periodic variation of the length of day (LOD) and χy in the early 1988 seems to be caused by the above-mentioned 35-55 days oscillations of the northern hemisphere atmosphere. This variation can be related to the rapid passing from the El Niño to the La Niña phenomenon or from the minimum to the maximum in the Southern Oscillation Index in 1987-1989.

scholarly journals Polar motion prediction using the combination of SSA and Copula-based analysis

2018 ◽  
Vol 70 (1) ◽  
Author(s):  
Sadegh Modiri ◽  
Santiago Belda ◽  
Robert Heinkelmann ◽  
Mostafa Hoseini ◽  
José M. Ferrándiz ◽  
...  
Keyword(s):  
Polar Motion ◽  
Motion Prediction ◽  
Polar Motion Prediction

scholarly journals Long-term polar motion prediction using normal time–frequency transform

2013 ◽  
Vol 88 (2) ◽  
pp. 145-155 ◽  
Author(s):  
Xiaoqing Su ◽  
Lintao Liu ◽  
Hsu Houtse ◽  
Guocheng Wang
Keyword(s):  
Polar Motion ◽  
Motion Prediction ◽  
Time Frequency ◽  
Polar Motion Prediction

The influence of Antarctic and Greenland ice loss on polar motion: an assessment based on GRACE and multi-mission satellite altimetry

2021 ◽  
Author(s):  
Franziska Göttl ◽  
Andreas Groh ◽  
Maria Kappelsberger ◽  
Undine Strößenreuther ◽  
Ludwig Schröder ◽  
...  
Keyword(s):  
Gravity Field ◽  
Polar Motion ◽  
Satellite Altimetry ◽  
Global Climate ◽  
Pole Position ◽  
Position Vector ◽  
Random Errors ◽  
Laser Altimeter ◽  
Excitation Functions ◽  
Polar Motion Excitation

<p>Increasing ice loss of the Antarctic and Greenland Ice Sheets (AIS, GrIS) due to global climate change affects the orientation of the Earth’s spin axis with respect to an Earth-fixed reference system (polar motion). Ice mass changes in Antarctica and Greenland are observed by the Gravity Recovery and Climate Experiment (GRACE) in terms of time variable gravity field changes and derived from surface elevation changes measured by satellite radar and laser altimeter missions such as ENVISAT, CryoSat-2 and ICESat. Beside the limited spatial resolution, the accuracy of GRACE ice mass change estimates is limited by signal noise (meridional error stripes), leakage effects and uncertainties of the glacial isostatic adjustment (GIA) models, whereas the accuracy of satellite altimetry derived ice mass changes is limited by waveform retracking, slope related relocation errors, firn compaction and the density assumption used in the volume-to-mass conversion.</p><p> </p><p>In this study we use different GRACE gravity field models (CSR RL06M, JPL RL06M, ITSG-Grace2018) and satellite altimetry data (from TU Dresden, University of Leeds, Alfred Wegener Institute) to assess the accuracy of the gravimetry and altimetry derived polar motion excitation functions. We show that due to the combination of individual solutions, systematic and random errors of the data processing can be reduced and the robustness of the geodetic derived AIS and GrIS polar motion excitation functions can be increased. Based on these investigations we found that AIS mass changes induce the pole position vector to drift along the 60° East meridian by 2 mas/yr during the study period 2003-2015, whereas GrIS mass changes cause the pole vector to drift along the 45° West meridian by 3 mas/yr.</p>

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