ORIGINAL ARTICLE
Short-term prediction of UT1-UTC and LOD via Dynamic Mode Decomposition and combination of least-squares and vector autoregressive model
1
Department of Integrated Geodesy and Cartography, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Kraków
These authors had equal contribution to this work
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2023-11-07
Final revision date: 2024-02-20
Acceptance date: 2024-02-20
Publication date: 2024-03-06
Corresponding author
Maciej Michalczak
Department of Integrated Geodesy and Cartography, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059, Krakow, Poland
Department of Integrated Geodesy and Cartography, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059, Krakow, Poland
Reports on Geodesy and Geoinformatics 2024;117:45-54
KEYWORDS
TOPICS
ABSTRACT
This study presents a short-term forecast of UT1-UTC and LOD using two methods, i.e. Dynamic Mode Decomposition (DMD) and combination of Least-Squares and Vector Autoregression (LS+VAR). The prediction experiments were performed separately for yearly time spans, 2018-2022. The prediction procedure started on January 1 and ended on December 31, with 7-day shifts between subsequent 30-day forecasts. Atmospheric Angular Momentum data (AAM) were used as an auxiliary time series to potentially improve the prediction accuracy of UT1-UTC and LOD in LS+VAR procedure. An experiment was also conducted with and without elimination of effect of zonal tides from UT1-UTC and LOD time series. Two approaches to using the best steering parameters for the methods were applied:. First, an adaptive approach, which observes the rule that before every single forecast, a preliminary one must be performed on the pre-selected sets of parameters, and the one with the smallest prediction error is then used for the final prediction; and second, an averaged approach, whereby several forecasts are made with different sets of parameters (the same parameters as in adaptive approach) and the final values are calculated as the averages of these predictions. Depending on the method and data combination mean absolute prediction errors (MAPE) for UT1-UTC vary from 0.63 ms to 1.43 ms for the 10th day and from 3.07 ms to 8.05 ms for the 30th day of the forecast. Corresponding values for LOD vary from 0.110 ms to 0.245 ms for the 10th day and from 0.148 ms to 0.325 ms for the 30th day.
ACKNOWLEDGEMENTS
Research project supported by program „Excellence initiative – research university” for the AGH University of Krakow.
The paper is a result of research on geospatial methods carried out within the statutory research grant no. 16.16.150.545 at the Department of Integrated Geodesy and Cartography, AGH University of Krakow.
We gratefully acknowledge Poland’s high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) for providing computer facilities and support within computational grant Nos. PLG/2022/015968 and PLG/2023/016281.
Intermediate results are available on the website accompanying this article: home.agh.edu.pl/mmichalc
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