Research On The Processing Methods Of The Crosspoints Of Satellite Altimeter Data

The instantaneity and uncertainties of sea surface height influence precision of crossover adjustment,and lead to time-varying effects of sea surface level.Conducting time series analysis for altimetry data can help finding out the instantaneous law of sea level variation and furthermore establishing correction model for sea level variation.It would not only reduce the time-varying effects of sea surface variable but also improve the accuracy of crossover processing,which is important for satellite altimetry data application.In this paper,the regional and seasonal variation of the sea level anomaly value is modelled,which is later used in the processing of Jason-1 satellite SGDR data for seven consecutive years to correct the crossing point difference.Then the corrected sea level and the MSS_CNES_CLS11 model are compared to evaluate the accuracy.The main work and conclusions are as follows:(1)In the same study area,a comparison between two methods shows that the weighted distance method is better than latitude weighted method for getting sea surface height on normal points by 2.6 of RMS.After adopting the posteriori compensation theory,the RMS of processing result enhanced 2.1cm,weakening the influence of the radial orbit errors.(2)In the respect of data characteristics of coastal areas,the distance weighted average method can better ensure the accuracy and stability of data than latitude weighted methods.Analysis the time series(including monthly,seasonal and annual variation)of single pathway sea surface height data and the power spectrum shows that regional sea level height variation includes a main period of about 32 months and sub-period of 11 months,which proving a certain seasonal variation.(3)A model of 14 latitude-zones sea surface height anomaly in time series is make and examined respectively from residual error sequence properties,the single passway data of recent years,other track data and the data of the whole offshore area.The RMS of sea surface height anomaly data is reduced by 1~2cm,improving the precision of altimetry data adjustment handling.(4)After calculating the Chinese coastal sea level,the next work is based on the sea surface height along-track data switching,qualitative and quantitative comparison between the corrected model and MSS_CNES_CLS11 model,whose trend of contour is exactly the same.The RMS of the mean sea level along the rail height anomaly data reduced from 9.3cm to 6.1cm,proving the validity of seasonal changes correction model for mean sea level.