The Study Of Sea Level Anomalies Based On Multi-satellite Altimeter Data In China Seas

The sea level anomaly(SLA)is the change of instantaneous sea level relative to mean sea level,which is widely used in the study of marine dynamic research.Since the beginning of the 1990 s,satellite altimetry technology has become more and more mature,and its data rely on large-scale,all-weather,abundant data volume,has become the most important method to obtain the global SLA data.The multiple altimetry satellites in orbit simultaneously promote multiple merged data generation,and the spatial and temporal distribution of merged data is more regular and more accurate,which provides data protection for the mesoscale marine dynamic research.Based on the combined use of ten multi-satellite altimeter data,the multiple in-situ data was merged to get uniform grided SLA data,which carry out the study of sea level change of China seas.In this paper,a merged SLA data of T/P,ERS-1/2,Jason-1/2 and so on from1993-2015 was used.Firstly,along-track SLA data were low-pass filtered with Lanczos cut-off filter with wavelength choosing in research.Secondly,according to Gauss-Markon least squares theory,the covariance function is improved in the objective space-time analysis,studying on the merged method of multiple SLA data,and the filtered results and merged data are evaluated respectively.According to the Gauss Markov least squares theory,the covariance function is improved for the objective analysis method of space and time,and the data fusion method of multi-source satellite altimetry is carried out.The fusion data of multi-source satellite sea surface height are obtained,and the filtering result and fusion data are respectively Evaluation.Results show that the average deviation' s RMS of each satellite filtered data and AVISO filtered data is 0.5867 cm,the mean correlation is 0.998,the Lanczos filter method has ideal effect through optimal selecting wavelength on along-track data.For example,China Seas' s merged SLA data,by comparing with AVISO products,their means,RMS,standard deviation,RMSE and correlation all showedthat the merged SLA data has high accuracy and reliability,which can accurately reflect the global or regional SLA distribution.Finaly,the merged SLA data and ECCO2 reanalysis data are used to study the interannual and decadal variability of SAL and steric sea level near the China Seas.The results show that the total sea-level rising rate of China Seas is about 3.65mm/a in1993-2015,and the SLA low frequency signals exist about 1.2 to 1.5 years and 2.3years interannual periodic signals and the about 11 years decadal periodic signal.The total sea level change in China is mainly caused by steric sea level change.The interannual periodic signal of sea level change in China Seas is in sync with the trend of sea level in the Western equatorial Pacific.Adopting the multivariate linear regression model,ENSO and PDO have been found to cause negative SLA in Western Pacific by changing the steric sea level,then cause the interannual and decadal fluctuations of SLA in China Seas.