Developed by the ICM-CSIC, the system enables the automatic cleaning of oceanographic data, paving the way for more efficient and accurate monitoring in remote areas.
A team from the Institut de Ciències del Mar (ICM-CSIC), in collaboration with the Faculty of Nautical Studies of Barcelona (FNB-UPC), has developed a new technology to collect and process sea surface temperature and salinity data in an automatic, efficient, and reliable way. This innovation, recently published in Frontiers in Marine Science, represents a significant step forward in monitoring remote ocean regions—particularly relevant in the context of global climate change.
The system combines two major innovations: a set of sensors mounted on a 3D-printed hydrodynamic structure installed on the keel of a sailing vessel, and an automated data-filtering method based on signal noise-cleaning techniques. Together, these advances reduce measurement errors and eliminate the need for manual data cleaning, which until now was essential but subjective and time-consuming.
"With this system, we not only obtain high-quality data from very hard-to-reach areas, but we also streamline its processing in a standardized and replicable way. It’s a tool that could transform how real-time global data is collected and processed," explains Nicolas Werner Pelletier, ICM researcher and lead author of the study.
A new pathway for data collection
The sensors were installed on the sailing vessel Pen Duick VI during its participation in the 2023–2024 Ocean Globe Race round-the-world voyage. This historic vessel, captained by Marie Tabarly, sailed through areas of the Southern Ocean that are virtually unexplored from a scientific perspective.
The devices were housed in a custom-designed casing aimed at reducing the turbulence and interferences that typically affect ocean data collection. This structure, developed in collaboration with FNB-UPC, was 3D printed, allowing for optimized hydrodynamic efficiency and adaptation to the specifics of the vessel.
This methodology opens new possibilities for using non-scientific vessels with research-relevant routes as environmental monitoring platforms—particularly useful in areas not reached by commercial ships.
Automated filtering
One of the biggest challenges when working with data collected in dynamic conditions is noise—values affected by bubbles, vibrations, or lack of water renewal at the sensor. Traditionally, this data was cleaned manually—a slow process prone to bias.
To overcome this, the ICM team developed an automatic filtering method using image processing techniques and moving averages, capable of identifying and removing anomalous values with high accuracy and speed.
"This automated system drastically reduces processing time and removes the subjectivity of manual cleaning. We've made the code open-source so other research groups can use and adapt it to their own data," says Marta Umbert, ICM researcher and co-author of the paper.
Advancing climate research
Although the main objective of the study was not to conduct a scientific analysis of the data content, preliminary results show temperature and salinity patterns consistent with expectations for remote southern ocean regions. This validates both the quality of the measurements and the system’s potential to contribute to climate system knowledge.
In the context of the climate emergency, the ability to collect and analyze data quickly, reliably, and at scale is key to understanding global trends and calibrating climate models. For this reason, the new methodology marks a concrete step toward more open, scalable, and robust ocean observation.
The collected data is available in an open-access repository, and the developed filtering software has been released as an open-source tool to encourage its adoption by other scientific institutions and initiatives.