A combined denoising method of empirical mode decomposition and singular spectrum analysis applied to Jason altimeter waveforms: A case of the Caspian Sea

During the satellite pulse propagation and reception, the altimeter waveform is inevitably affected by noise. To reduce the noise level in Jason altimeter waveforms, we used singular spectrum analysis (SSA), empirical mode decomposition (EMD), and the combination of SSA and EMD to obtain the denoised waveforms. The advantages of the combined method were verified and the accuracy of the mean sea surface height (MSSH) model was improved. Comparing the denoising effect of the three methods, the results show that the signal-to-noise ratio (SNR), correlation coefficient and root-mean-square error are effectively improved by the combination of SSA and EMD. The sea surface heights (SSHs) were remeasured with a 50% threshold retracker of denoised waveforms, and the MSSH model of the Caspian Sea with a grid of 1' x 1' was established from the retracked SSHs of Jason-1/2/3. Taking the mean value of the four models as a control, it is found that the model calculated by the combined denoising method has the highest accuracy. This indicates that using the combined denoising method to reduce the noise level is beneficial to improve the accuracy of the MSSH model. (C) 2022 Editorial office of Geodesy and Geodynamics. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.

Automated summary

This study used singular spectrum analysis, empirical mode decomposition, and the combination of both methods to reduce the noise level in Jason altimeter waveforms. The combined method was found to effectively improve the signal-to-noise ratio, correlation coefficient, and root-mean-square error. By re-measuring the sea surface heights and establishing the MSSH model, it was demonstrated that the combined denoising method resulted in the highest accuracy.