Forecasting irregular variations of UT1-UTC and LOD data caused by ENSO

Computer Science

Scientific paper

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Scientific paper

The research focuses on prediction of LOD and UT1-UTC time series up to one-year in the future with the particular emphasis on the prediction improvement during El Nĩ o or La Nĩ a n n events. The polynomial-harmonic least-squares model is applied to fit the deterministic function to LOD data. The stochastic residuals computed as the difference between LOD data and the polynomial- harmonic model reveal the extreme values driven by El Nĩ o or La Nĩ a. These peaks are modeled by the n n stochastic bivariate autoregressive prediction. This approach focuses on the auto- and cross-correlations between LOD and the axial component of the atmospheric angular momentum. This technique allows one to derive more accurate predictions than purely univariate forecasts, particularly during El Nĩ o/La n Nĩ a events. n

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