Gravity anomalies determined from mean sea surface model data over the Gulf of Mexico

With the improvements in the density and quality of satellite altimetry data,a high-precision and high-resolution mean sea surface model containing abundant information regarding a marine gravity field can be calculated from long-time series multi-satellite altimeter data.Therefore,in this study,a method was proposed for determining marine gravity anomalies from a mean sea surface model.Taking the Gulf of Mexico(15°–32°N,80°–100°W)as the study area and using a removal-recovery method,the residual gridded deflections of the vertical(DOVs)are calculated by combining the mean sea surface,mean dynamic topography,and XGM2019e_2159 geoid,and then using the inverse Vening-Meinesz method to determine the residual marine gravity anomalies from the residual gridded DOVs.Finally,residual gravity anomalies are added to the XGM2019e_2159 gravity anomalies to derive marine gravity anomaly models.In this study,the marine gravity anomalies were estimated with mean sea surface models CNES_CLS15MSS,DTU21MSS,and SDUST2020MSS and the mean dynamic topography models CNES_CLS18MDT and DTU22MDT.The accuracy of the marine gravity anomalies derived by the mean sea surface model was assessed based on ship-borne gravity data.The results show that the difference between the gravity anomalies derived by DTU21MSS and CNES_CLS18MDT and those of the ship-borne gravity data is optimal.With an increase in the distance from the coast,the difference between the gravity anomalies derived by mean sea surface models and ship-borne gravity data gradually decreases.The accuracy of the difference between the gravity anomalies derived by mean sea surface models and those from ship-borne gravity data are optimal at a depth of 3–4 km.The accuracy of the gravity anomalies derived by the mean sea surface model is high.

The global mean sea surface model WHU2013

The mean sea surface （MSS） model is an important reference for the study of charting datum and sea level change. A global MSS model named WHU2013, with 2′ × 2′ spatial resolution between 80° S and 84°N, is established in this paper by combining nearly 20 years of multi-satellite altimetric data that include Topex/Poseidon （T/P）, Jason-1, Jason-2, ERS-2, ENVISAT and GFO Exact Repeat Mission （ERM） data, ERS-1/168, Jason-1/C geodetic mission data and Cryosat-2 low resolution mode （LRM） data. All the ERM data are adjusted by the collinear method to achieve the mean along-track sea surface height （SSH）, and the combined dataset of T/P, Jason-1 and Jason-2 from 1993 to 2012 after collinear adjustment is used as the reference data. The sea level variations in the non-ERM data （geodetic mission data and LRM data） are mainly investigated, and a combined method is proposed to correct the sea level variations between 66°S and 66°N by along-track sea level variation time series and beyond 66°S or 66°N by seasonal sea level variations. In the crossover adjustment between multi-altimetric data, a stepwise method is used to solve the problem of inconsistency in the reference data between the high and low latitude regions. The proposed model is compared with the CNES-CLS2011 and DTU13 MSS models, and the standard derivation （STD） of the differences between the models is about S cm between 80°S and 84°N, less than 3 cm between 66°S and 66°N, and less than 4 cm in the China Sea and its adjacent sea. Furthermore, the three models exhibit a good agreement in the SSH differences and the along-track gradient of SSH following comparisons with satellite altimetry data.

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’× 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.