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Data Reconstruction And Assimilation Experiment Of Satellite Sea Surface Temperature And Suspended Sediment Concentration

Posted on:2010-06-20Degree:DoctorType:Dissertation
Country:ChinaCandidate:Y Z DingFull Text:PDF
GTID:1118360302498980Subject:Pattern Recognition and Intelligent Systems
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At present, satellite remote sensing and numerical simulation are the two major means by which we learn more about ocean processes. Satellite remote sensing is characterized by periodicity, macroscopy, real-time and low cost, which is the reason why it is widely used in ocean monitoring. Numerical simulation can grasp the rules of ocean spatial-temporal variations as a whole, playing an important role in ocean forecasting. Because of the clouds coverage over the ocean and changes in scanning orbit of sensors, the satellite remote sensing data obtained by the visible and infrared bands often show missing data in a large proportion. Besides, thin clouds which are difficult to precisely detect could result in abnormal data retrieval。The control functions in numerical simulation predigest the real world. And errors of model, initial conditions and boundary conditions will reduce the forecast abilities. Combining the advantages of satellite remote sensing and numerical simulation, we can make use of the data assimilation method, merge the remote data and simulated data, construct the ocean data assimilation system and improve the accuracy of ocean forecast.In response to the above problems, we advance an EMD-EOF data reconstruction method, which combines empirical mode decomposition (EMD) and Empirical Orthogonal Function (EOF). By applying the new method, we reconstruct the five-day-average sea surface temperature (SST) and suspended sediment concentration (SSC) data of Changjiang estuary sea area in 2003.The conclusions are as follows. Firstly, the root mean squared error(RMSE) of SST reconstruction is 0.9℃and log RMSE of SSC reconstruction is 0.137(log1O mg/L). Secondly, the calculating time of EMD-EOF method is less than half of that of the DINEOF method raised by Alvera, and the reconstruction precision is comparatively improved. Thirdly, the EMD-EOF method can effectively eliminate the abnormal data which result from undetected thin clouds in remote sensing retrieve, improving the precision of original remote sensing images. Lastly, the EMD-EOF method can effectively reconstruct remote sensing images of little data, which leads to reanalysis remote sensing products of high spatial-resolution and full coverage.Sea temperature and suspended sediment affect the growth of phytoplankton in China Adjacent Seas and they are also the basis of ocean ecological simulation and forecast. Using singular evolutive extended kalman filter(SEEK), combined with the simulation result of COHERENS model and remote sensing observation data, we initially build the three-dimensional data assimilation system of sea surface temperature and suspended sediment in Hangzhou Bay. This system is further tested via hindcast validation experiment by using the remote sensing data of SST and SSC of Spring in 2003.Our research results are as follows. Firstly, compared with the remote sensing SST, the RMSEs of simulated data, forecast data and analyzed data are 2.13,1.65 and 0.75℃respectively, and compared with the remote sensing SSC, the log RMSEs of simulated data, forecast data and analyzed data are 0.62,0.53 and 0.26 (log10 mg/L) respectively. Secondly, as the difference between the analyzed data and remote sensing data and the difference between the analyzed data and forecast data show, the analyzed data are identical to the forecast data in terms of distributing trend and the analyzed data are close to the observed data in terms of numerical value. Therefore, observation has obvious effect on assimilation. Lastly, the data assimilation method can effectively combine the advantages of both remote sensing observation and numerical simulation, improving the precision of numerical forecast.In order to better utilize the remote sensing data and improve the precision of ocean numerical forecasting, further research work is to be complemented from two perspectives. On the one hand, other remote sensing data (CHL-a, SDD eg.) are to be reconstructed by using the EMD-EOF method. Meanwhile, by forecasting the time-coefficients of EOF decomposition, we can build a short ocean remote sensing forecasting system. On the other hand, to enhance the precision of ocean ecological simulation and forecast, the data assimilation method is to be used to assimilate such remote sensing data as CHL-a and Particulate Organic Carbon (POC).
Keywords/Search Tags:Remote Sensing, Data Reconstruction, Data Assimilation, EOF, SEEK, SST, SSC, COHERENS model
PDF Full Text Request
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