Investigating the Relationship Between Length of Day and El-Niño Using Wavelet Coherence Method

Raut, Shrishail; Modiri, Sadegh; Heinkelmann, Robert; Balidakis, Kyriakos; Belda, Santiago; Kitpracha, Chaiyaporn; Schuh, Harald

doi:10.1007/1345_2022_167

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…It is shown that oceanic angular momentum (OAM), hydrological angular momentum (HAM), and sea level angular momentum (SLAM) can have a nonnegligible effect on polar motion and dUT1 as well (Bizouard & Seoane, 2010; Brzezinski & Nastula, 2002; Chin et al., 2004; Dahlen, 1976; Dill & Dobslaw, 2010; Dobslaw et al., 2010; Gross, 2008; Kiani Shahvandi, Schartner, & Soja, 2022; Luo et al., 2022; Nastula & Ponte, 1999). Furthermore, phenomena such as El Niño Southern Oscillation (ENSO) can have some influence on the rate of dUT1 (Raut et al., 2022; Xu et al., 2022). This can be analyzed using climatic indices (CIs) like the multivariate ENSO index (MEI, Wolter & Timlin, 1993), the Madden Julian Oscillation index (MJI, Kiladis et al., 2014), and the North Atlantic Oscillation index (NAI, Visbeck et al., 2001).…”

Section: Introductionmentioning

confidence: 99%

Geophysically Informed Machine Learning for Improving Rapid Estimation and Short‐Term Prediction of Earth Orientation Parameters

Kiani Shahvandi,

Dill,

Dobslaw

et al. 2023

JGR Solid Earth

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Rapid provision of Earth Orientation Parameters (EOPs, here polar motion and dUT1) is indispensable in many geodetic applications and also for spacecraft navigation. There are, however, discrepancies between the rapid EOPs and the final EOPs that have a higher latency, but the highest accuracy. To reduce these discrepancies, we focus on a data‐driven approach, present a novel method named ResLearner, and use it in the context of deep ensemble learning. Furthermore, we introduce a geophysically‐constrained approach for ResLearner. We show that the most important geophysical information to improve the rapid EOPs is the effective angular momentum functions of atmosphere, ocean, land hydrology, and sea level. In addition, semi‐diurnal, diurnal, and long‐period tides coupled with prograde and retrograde tidal excitations are important features. The influence of some climatic indices on the prediction accuracy of dUT1 is discussed and El Niño Southern Oscillation is found to be influential. We developed an operational framework, providing the improved EOPs on a daily basis with a prediction window of 63 days to fully cover the latency of final EOPs. We show that under the operational conditions and using the rapid EOPs of the International Earth Rotation and Reference Systems Service (IERS) we achieve improvements as high as 60%, thus significantly reducing the differences between rapid and final EOPs. Furthermore, we discuss how the new final series IERS 20 C04 is preferred over 14 C04. Finally, we compare against EOP hindcast experiments of the European Space Agency, on which ResLearner presents comparable improvements.

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Section: Introductionmentioning

confidence: 99%

Geophysically Informed Machine Learning for Improving Rapid Estimation and Short‐Term Prediction of Earth Orientation Parameters

Kiani Shahvandi,

Dill,

Dobslaw

et al. 2023

JGR Solid Earth

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“…The LOD is the most challenging parameter to forecast since the greatest difficulties come from the occurrence of extreme events such as El Nino, which have demonstrated themselves in LOD signals [ 7 , 8 , 9 ]. The changes in the LOD could be due to tidal or non-tidal origins.…”

Section: Introductionmentioning

confidence: 99%

The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)

Guessoum

Belda

Ferrándiz

et al. 2022

Sensors

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Accurate Earth orientation parameter (EOP) predictions are needed for many applications, e.g., for the tracking and navigation of interplanetary spacecraft missions. One of the most difficult parameters to forecast is the length of day (LOD), which represents the variation in the Earth’s rotation rate since it is primarily affected by the torques associated with changes in atmospheric circulation. In this study, a new-generation time-series prediction algorithm is developed. The one-dimensional convolutional neural network (1D CNN), which is one of the deep learning methods, is introduced to model and predict the LOD using the IERS EOP 14 C04 and axial Z component of the atmospheric angular momentum (AAM), which was taken from the German Research Centre for Geosciences (GFZ) since it is strongly correlated with the LOD changes. The prediction procedure operates as follows: first, we detrend the LOD and Z-component series using the LS method, then, we obtain the residual series of each one to be used in the 1D CNN prediction algorithm. Finally, we analyze the results before and after introducing the AAM function. The results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict the LOD for up to 7 days.

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EOP Prediction Based on Multi and Single Technique Space Geodetic Solution

Modiri,

Thaller,

Belda

et al. 2024

International Association of Geodesy Symposia

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Real-time Earth Orientation Parameters (EOP) are crucial in various space geodetic applications, from satellite navigation to weather forecasting. This study introduces a refined prediction package leveraging diverse EOP series from the Federal Agency of Cartography and Geodesy (BKG), including rapid and final series, Satellite Laser Ranging (SLR) series, and International Earth Rotation and Reference Systems Service (IERS) C04. Our approach yields substantial improvements in EOP prediction accuracy. Results highlight superior performance in critical parameters such as Polar Motion, (UT1-UTC) dUT1, and Length of Day (LOD) predictions. Notably, our predictions surpass benchmarks from the Second EOP Prediction Comparison Campaign (2nd EOP-PCC)” organized by International Association of Geodesy (IAG) and IERS, showcasing the effectiveness of our methodology. Additionally, BKG’s Rapid EOP stands out with remarkable accuracy, featuring a shorter latency of 1 to 2 days. This study contributes to our understanding of Earth’s rotational dynamics. It provides practical advancements in real-time EOP predictions, demonstrating the potential impact on a wide range of scientific and operational applications.

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Investigating the Relationship Between Length of Day and El-Niño Using Wavelet Coherence Method

Cited by 6 publications

References 15 publications

Geophysically Informed Machine Learning for Improving Rapid Estimation and Short‐Term Prediction of Earth Orientation Parameters

Geophysically Informed Machine Learning for Improving Rapid Estimation and Short‐Term Prediction of Earth Orientation Parameters

The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)

EOP Prediction Based on Multi and Single Technique Space Geodetic Solution

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