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2 june 2023 15:10:36

 
Remote Sensing, Vol. 15, Pages 2910: The CNES CLS 2022 Mean Sea Surface: Short Wavelength Improvements from CryoSat-2 and SARAL/AltiKa High-Sampled Altimeter Data (Remote Sensing)
 


A new mean sea surface (MSS) was determined by focusing on the accuracy provided by exact-repeat altimetric missions (ERM) and the high spatial coverage of geodetic (or drifting) missions. The goal was to obtain a high-resolution MSS that would provide centimeter-level precision. Particular attention was paid to the homogeneity of the oceanic content of this MSS, and specific processing was also carried out, particularly on the data from the geodetic missions. For instance, CryoSat-2 and SARAL/AltiKa data sampled at high frequencies were enhanced using a dedicated filtering process and corrected from oceanic variability using the results of the objective analysis of sea-level anomalies provided by DUACS multi-missions gridded sea-level anomalies fields (MSLA). Particular attention was also paid to the Arctic area by combining traditional sea-surface height (SSH) with the sea levels estimated within fractures in the ice (leads). The MSS was determined using a local least-squares collocation technique, which provided an estimation of the calibrated error. Furthermore, our technique takes into account altimetric noises, ocean-variability-correlated noises, and along-track biases, which are determined independently for each observation. Moreover, variable cross-covariance models were fitted locally for a more precise determination of the shortest wavelengths, which were shorter than 30 km. The validations performed on this new MSS showed an improvement in the finest topographic structures, with amplitudes exceeding several cm, while also continuing to refine the correction of the oceanic variability. Overall, the analysis of the precision of this new CNES_CLS 2022 MSS revealed an improvement of 40% compared to the previous model, from 2015.


 
89 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 15, Pages 2911: Automated VIIRS Boat Detection Based on Machine Learning and Its Application to Monitoring Fisheries in the East China Sea (Remote Sensing)
Remote Sensing, Vol. 15, Pages 2905: A Crustal Deformation Pattern on the Northeastern Margin of the Tibetan Plateau Derived from GPS Observations (Remote Sensing)
 
 
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