Automated denoising techniques for sea surface temperature and salinity data from opportunity vessels: application to the Ocean Globe Race 2023-2024

Ever more often, opportunity vessels are used to provide in-situ sea surface temperature and salinity data. In particular, sailing vessels participating in oceanic races are often utilized, as they usually cover remote areas not reached by commercial vessels, such as the southern oceans. The received signal from temperature and salinity sensors -especially the latter- is often disturbed either by bubbles, due to strong turbulent flows, or by non-renewal of the water in contact with the sensor. Until now, only manual methods have been successfully used to filter this data, since no automated procedure has been developed. In this paper, we present (i) a sensor housing to be placed on the keel, designed to reduce the aforementioned physical issues, and (ii) an automatic filtering method to override the manual procedure. The physical system was mounted on the historic sailboat Pen Duick VI and has served to collect data along the Ocean Globe Race route (2023-2024). This initiative was a collaboration between the crew of the boat, the Institute of Marine Sciences (ICM-CSIC) in Barcelona, and the Laboratoire d’Océanographie Physique et Spatiale (Ifremer). The housing for sensors consisted of a 3D-printed hydrodynamic support, designed to reduce drag. The automated filtering approach was based on wavelet denoising techniques and simple moving averages. The results are presented in an open dataset and show that procedure yielded good performance in identifying and rejecting outliers, while operating with far greater speed than manual filtering. The method is intended to become a standard procedure for similar in-situ datasets, and an open-source software is provided for this purpose. This work is a step forward in oceanographic data processing and aims to provide a tool with a wide range of applications.