Satellite Altimeter Based Sea Level Analysis Of China Seas For The Period 1992-2004

Sea level rise is a global problem. Many Chinese scholars have made researches on the sea level change in China Sea using the data at tide gauge stations and the short period satellite altimeter data. But there are limitations in the data sources. Till now the span of the longest sea level dataset has been 11.3 years, and these data could help us find more information.On the other hand, aiming at the problems in the present marine Geographical Information System, the Lab of Marine Geographical Information System (MGISL) at the Ocean Remote Sensing Institute (ORSI) in Ocean University of China (OUC) develops a satellite Remote Sensing data based Marine and Atmospheric Geographical Information System (MAGIS), which could manage marine and atmospheric Remote Sensing data automatically, routinely and efficiently. This paper introduces the designs and implementations of the spatial-temporal analysis module and the visualization module that the author participated in.The spatial-temporal analysis shows many valuable characteristics of the sea level variation in China Sea:(1).The northern part of China Sea has a relatively higher level than the southern part, the distributions of sea level in summer and winter are totally opposite. The main facts that affect China Sea level is the sun radiation, monsoon and the ocean currents;(2). The E1 Nino events could decrease the sea level, while the La Nina events could increase the sea level. E1 Nino-La Nina events make most effects on the South China Sea;(3). The sea level in China Sea shows a general ascending trend in the 11.3 years, and the rise rate is 0. 36cm/a. The rise rate of the Bohai Sea, the Yellow Sea, the East China Sea and the South China Sea are: 0. 12cm/a, 0. 36cm/a, 0. 64cm/a and 0. 46cm/a;(4).The dominant signal of China Sea level is annual variability, and there're also semiannual and 2 months signals;(5). The Yellow Sea and the East China Sea are similar in mode composition, the amplitude and the phase, etc, while The Bohai Sea and the South China Sea are totally different regional seas.Gray Model, Regular Mode approach and Self-Adaptive Filtering methods areused to predict the sea Level in China Seas. The results show that in the next 30 years, the sea level in China Sea will increase 11cm. Also, the prediction results are used to analyze the preconditions of using the three methods. The Gray Model is proved improper for sea level prediction. The Regular Mode method could simulate the sea level, variation on condition that all the variation signals are included. The result of Self-Adaptive Filtering is highly correlated with the period of the data.In this study, we also use the near-real time altimeter data of Jason-1 to monitor the tsunami happened in the Indian Ocean on Dec. 26 2004, and the result is very inspiring. The perspective of using altimeter data to monitor tsunami is stated at the end.