Earth Rotation Parameters Prediction and Climate Change Indicators in it

Xu, Xueqing; Zhou, Yonghong; Xu, Cancan

doi:10.2478/arsa-2022-0023

Cited by 3 publications

(4 citation statements)

References 31 publications

(38 reference statements)

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“…It is also worth noting that the accuracy of both UT1-UTC and LOD predictions for 2020 and 2022 decreased in almost all cases. The large values of MAPE may be caused by, among other things, such phenomena as El Niño and La Niña occurring at the same time (Xu et al 2022a; https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ ensostuff/ONI_v5.php).…”

Section: Discussionmentioning

confidence: 99%

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

Michalczak,

Ligas

2024

Reports on Geodesy and Geoinformatics

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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 1.43ms for the 10th day and from 3.07 ms to 8.05ms 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.

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Section: Discussionmentioning

confidence: 99%

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

Michalczak,

Ligas

2024

Reports on Geodesy and Geoinformatics

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“…Regarding the phase lead exhibited in the latest triple-dip La Niña detected in Earth's rotation rate series, here, we calibrated this discrepancy by moving the climate-related ∆LOD series 3 months forward. Afterward, the climate event forecast could be investigated by extracting information from ∆LOD predictions [17]. Considering the results of the first Earth orientation parameter prediction comparison campaign (EOP PCC), the least square plus auto-regressive (LS+AR) model has been suggested to be the suitable method on average for EOP prediction [23].…”

Section: The Follow-up El Niño Event Indicated Over 2023mentioning

confidence: 99%

“…Based on the climate-related terms isolated from ∆LOD observations, the latest 2020-2021 and 2021-2022 La Niña events were detected explicitly. Moreover, the third trough of this continuous cold phenomenon was forecasted using ∆LOD predictions successfully [17].…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the data precision improvements in geophysical fluid, these tiny sources could be taken into account. Additionally, there are phase deviations in ENSO event detections at both ends of the ∆LOD series, due to the edge effect that cannot be eliminated completely [17]. Further investigations of ENSO event forecast using ∆LOD predictions are necessary.…”

Section: Introductionmentioning

confidence: 99%

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Interannual Variation in Earth’s Rotation Rate and Its Role as a Climate Change Indicator

Zhou

2023

Atmosphere

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Exploring the relationship between climate phenomena and anomalies in Earth’s rotation during a corresponding period is helpful for constraining the assessment of global change, even for the early warning of climate events. This study selected the latest length of day changes (ΔLOD) observations, and extracted the interannual terms solely related to climatic variations, employing a difference plus frequency domain stepwise regression (Difference+FDSR) method. Afterward, we calculated the correlations of different data compositions between surface fluid contributions (AAM, OAM and HAM) and the climate-related ΔLOD. Finally, the anomalies during the period of 1993–2023 were focused on, by comparing the high-precision fluid forcing and the climate-related ΔLOD with El Niño–Southern Oscillation (ENSO) indices. Our results show that superimposing the HAM can improve fluid correlations ~5% with climate-related ΔLOD, but the OAM contribution is not obvious. Additionally, we detected a triple-dip La Niña in the last 3 years, and the corresponding minimum values of climate-related ΔLOD were between −0.11 and −0.23 ms. Furthermore, we investigated the short-term forecast of a climate event with ΔLOD predictions based on the climate change information on Earth‘s rotation rate, wherein a follow-up El Niño is indicated.

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Assessment of length-of-day and universal time predictions based on the results of the Second Earth Orientation Parameters Prediction Comparison Campaign

Śliwińska-Bronowicz,

Kur,

Wińska

et al. 2024

J Geod

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Predicting Earth Orientation Parameters (EOP) is crucial for precise positioning and navigation both on the Earth’s surface and in space. In recent years, many approaches have been developed to forecast EOP, incorporating observed EOP as well as information on the effective angular momentum (EAM) derived from numerical models of the atmosphere, oceans, and land-surface dynamics. The Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) aimed to comprehensively evaluate EOP forecasts from many international participants and identify the most promising prediction methodologies. This paper presents the validation results of predictions for universal time and length-of-day variations submitted during the 2nd EOP PCC, providing an assessment of their accuracy and reliability. We conduct a detailed evaluation of all valid forecasts using the IERS 14 C04 solution provided by the International Earth Rotation and Reference Systems Service (IERS) as a reference and mean absolute error as the quality measure. Our analysis demonstrates that approaches based on machine learning or the combination of least squares and autoregression, with the use of EAM information as an additional input, provide the highest prediction accuracy for both investigated parameters. Utilizing precise EAM data and forecasts emerges as a pivotal factor in enhancing forecasting accuracy. Although several methods show some potential to outperform the IERS forecasts, the current standard predictions disseminated by IERS are highly reliable and can be fully recommended for operational purposes.

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Earth Rotation Parameters Prediction and Climate Change Indicators in it

Cited by 3 publications

References 31 publications

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

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

Interannual Variation in Earth’s Rotation Rate and Its Role as a Climate Change Indicator

Assessment of length-of-day and universal time predictions based on the results of the Second Earth Orientation Parameters Prediction Comparison Campaign

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