Importance of atmospheric angular momentum function in non-linear prediction of length of day variation

Details Importance of atmospheric angular momentum function in non-linear prediction of length of day variation Importance of atmospheric angular momentum function in non-linear prediction of length of day variation

Jan 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008acasn..49...93w&link_type=abstract

Acta Astronomica Sinica, vol. 49, no. 1, p.93-100

Statistics

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1

Astrometry: Earth Rotation, Methods: Numerical

Scientific paper

Prediction of the variations of the length of day (LOD) is of great importance in both scientific issues and practical applications. However, due to the complex time-variable characteristics of the LOD variation, it's usually difficult to obtain satisfied prediction results by conventional linear time series analysis methods. The artificial neural networks (ANN) is a non-linear information processing system. This study employs the ANN to predict the LOD change. The topology of the ANN model is determined based on the criterion of minimization of the root mean square error (RMSE). For most of the studies that use ANN to predict the LOD, the influence of global atmospheric movements on the variations of the LOD hasn't been considered. Considering the close connection between the LOD variation and the atmospheric circulation movement, and the capability of simulating and forecasting the axial atmospheric angular momentum (AAM) function with global atmospheric circulation pattern, the axial AAM is added into the ANN model as an additional input parameter to predict the LOD variation. The daily LOD series in this study are from the C04 series of the International Earth rotation and reference systems service (IERS), spanning from 1962 to 2005. We first removed the contributions of the 62 zonal Earth tides from the LOD changes with periods from 5 days to 18.6 years according to IERS Convention 2003, and the effects that can be described by functional models, e.g. the annual and semi-annual oscillations, the terms whose periods are 1, 1/2, 1/3 of the length of the whole data set. Only the residuals between the modeled and the observed LODRs, are used for training. Likewise, the axial AAM series are also de-trended. The residuals of LODR and axial AAM series are used to train the networks. The trained networks are applied to predict the LODR variation for time interval of 1 to 40 days. For comparisons, we also use the LODR only to construct the ANN model and to predict the LODR variations. The results show that the accuracy of LODR prediction is significantly improved after introducing the AAM into the ANN model, and the prediction results of the LODR residuals are in good agreement with the observed ones, in terms of both amplitude and phase information.

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