Prediction Analysis of UT1-UTC Time Series by Combination of the Least-Squares and Multivariate Autoregressive Method
The objective of this paper is to extensively discuss the theory behind the multivariate autoregressive prediction technique used elsewhere for forecasting Universal Time (UT1-UTC) and to characterise its performance depending on input geodetic and geophysical data. This method uses the bivariate time series comprising length-of-day and the axial component of atmospheric angular momentum data and needs to be combined with a least-squares extrapolation of a polynomial-harmonic model. Two daily length-of-day time series, i.e. EOPC04 and EOPC04_05 spanning the time interval from 04.01.1962 to 02.05.2007, are utilised. These time series are corrected for tidal effects following the IERS Conventions model. The data on the axial component of atmospheric angular momentum are processed to gain the 1-day sampling interval and cover the time span listed above. The superior performance of the multivariate autoregressive prediction in comparison to autoregressive forecasting is noticed, in particular during El Niño and La Niña events. However, the accuracy of the multivariate predictions depends on a particular solution of input length-of-day time series. Indeed, for EOPC04-based analysis the multivariate autoregressive predictions are more accurate than for EOPC04_05-based one. This finding can be interpreted as the meaningful influence of smoothing on forecasting performance.
KeywordsAtmospheric angular momentum El Niño/Southern Ociallation Length of day Multivariate autoregressive model Prediction
The research was financed from the Polish science funds for the period of 2009-2011 provided by Polish Ministry of Science and Higher Education through the grant no. N N526 160136 under leadership of Dr Tomasz Niedzielski at the Space Research Centre of Polish Academy of Sciences. The first author was also supported by EU EuroSITES project. The authors of R 2.9.0 – A Language and Environment and additional packages are acknowledged.
- Akyilmaz O, Kutt erer H (2004) Prediction of Earth rotation parameters by fuzzy inference systems. J Geodes 78:82–93Google Scholar
- Freedman AP, Steppe JA, Dickey JO, Eubanks TM, Sung LY (1994) The short-term prediction of universal time and length of day using atmospheric angular momentum. J Geophys Res 99(B4):6981–6996Google Scholar
- Kalarus M, Kosek W (2004) Prediction of Earth orientation parameters by artificial neural networks. Artificial Satellites 39:175–184Google Scholar
- Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds B, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471CrossRefGoogle Scholar
- Kosek W (1992) Short periodic autoregressive prediction of the Earth rotation parameters. Artificial Satellites 27:9–17Google Scholar
- Kosek W, Kalarus M, Johnson TJ, Wooden WH, McCarthy DD, Popiński W(2005) A comparison of LOD and UT1-UTC forecasts by different combination prediction techniques. Artificial Satellites 40:119–125Google Scholar
- McCarthy DD, Petit G (eds) (2004) IERS Conventions 2003 IERS Technical Note No. 32, Verlag des Bundesamts für Kartographie und Geodäsie, Frankfurt am MainGoogle Scholar