Short-term prediction of UT1-UTC and LOD via Dynamic Mode Decomposition and combination of least-squares and vector autoregressive model

• Autorzy: Michalczak Maciej , Ligas Marcin

Identyfikatory

DOI

10.2478/rgg-2024-0006

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

UT1-UTC; length of day; dynamic mode decomposition; autoregression; prediction

PL

UT1-UTC; długość dnia; rozkład w trybie dynamicznym; autoregresja; prognoza

• Wydawca: Wydział Geodezji i Kartografii Politechniki Warszawskiej
• Czasopismo: Reports on Geodesy and Geoinformatics
• Rocznik: 2024
• Tom: Vol. 117
• Strony: 45--54
• Opis fizyczny: Bibliogr. 31 poz., tab., wykr.

Twórcy

autor

Michalczak Maciej

• 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

• https://orcid.org/0000-0001-6859-988X

autor

Ligas Marcin

• 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

• https://orcid.org/0000-0002-0970-128X+

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).