Space-time analysis of sea level and Multivariate autoregressive modeling in the Oman Sea from Satellite altimetry

Spatial and temporal sea level variability in the Oman Sea is investigated using . Topex / Poseidon satellite altimeter data. Time series of sea level on 1°× 1° grid are analyzed Empirical Orthogonal Function analysis Principal (EOF) OR Component Analysis (PCA) is used to obtain a spatio- temporal description of the sea level field. We determine the spatial structure of the four leading modes and also the coefficients of the expansion as the principal component (PC) time series. 95% of the variation was captured in the first four EOF components of the sea surface height. A strong annual spectral density signal was discerned in all PCs. The leading pattern of the seasonal field reflects the dominance of a stable annual cycle over the study area and with contribution of the monthly cycle. The spatial pattern has a dipole like structure . one domain of high SSH in the upwelling region and one of low SSH at off Sultanate of Oman coast with a region of high variability between them. Te partial autocorrelations become close to 0 after about one lag while the autocorrelations remain strong through all the lags, indicating a mixed (ARIMA) model . The results proved that ARIMAI(2,0,2) model can provides reliable and satisfactory predictions for PCI time series. There was good agreement between data predictions using ARIMA statistical models.

کلیدواژه ها:

satellite altimetry ، sea level change ، PCA ، multivariate autoregressive