SLA Determination in Coastal Areas Using Least-Squares Collocation and Cryosat-2 Data

Satellite altimetry has provided during the last 30 years an unprecedented amount of high-resolution and high-accuracy data for the state of the oceans. With the latest altimetric satellites utilizing the SAR and SAR-in modes, reliable sea surface heights close to the coastline can be determined more efficiently. The main purpose of this paper is to estimate Sea Level Anomalies (SLAs) close to the coastline and to areas where data are absent, while Least Squares Collocation (LSC) has been used to carry out the prediction. The selected study area is the entire Mediterranean Sea and the estimation of SLA values was carried out using raw Cryosat-2 observations. For LSC to be applied, empirical and analytical covariance function models are defined and evaluated for estimating SLAs within 10° block windows. In order to investigate the accuracy of the analytical covariance functions that provide the most accurate results, prediction has been carried out at a single point, randomly selected in the Greek region being close to the coastline. From the analysis carried out, three types of analytical covariance functions were deemed as the optimal ones, providing a mean prediction accuracy at the 3.7 cm level. These models were then used for the SLA estimation at the 10° windows, specifying local empirical and analytical covariance function models. The prediction accuracies achieved range between 3.7 cm and 12.5 cm depending on the presence of islands.