Automated Polynya Identification Tool (APIT)

• Main Authors: Hickson, James, Catany, Rafael, Arias, Manuel, Naveira-Garabato, Alberto, Silvano, Alessandro, Turiel, Antonio, García Espriu, Aina, González-Haro, Cristina, González Gambau, Verónica, Olmedo, Estrella
• Format: Still Image
• Language: English
• Published: European Space Agency 2022
• Subjects: Weddell; Weddell Sea; Sea ice
• Online Access: http://hdl.handle.net/10261/331980

Living Planet Symposium, 23-27 May 2022, Bonn, Germany The Automated Polynya Identification Tool (APIT) is a machine learning based tool that aims to identify and define polynya formations in both space and time. These often small and short-lived phenomena are frequently undetected and are important for climate scientists to understand polar systems change. The APIT tool is a rapid, computationally efficient, low-cost and more time-efficient method for locating polynya formations relative to current in-situ surveying methods. APIT is currently in early-development and at a prototype stage, where MODIS imagery is the only sensor to have been applied using the widely recognised Weddell Sea polynya from 2017 as a way of training the tool. The use of an optical sensor in the polar regions has been found to be limited due to the quantity of cloud cover present and polar seasonal day-light hours; therefore, going forward, this tool will look to integrate alternative Earth Observation data, including but not limited to Sentinel-1, Soil Moisture and Ocean Salinity (SMOS) and CryoSat-2. Next APIT development stages contemplate the use of other auxiliary datasets, including those from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and the European Centre for Medium-Range Weather Forecasts (ECMWF), before implementing a machine learning detection process. Furthermore, for the provision of early-warning predictions, APIT will provide patterns and other oceanographic conditions taking place at different polynya evolutionary formation stages (i.e. before, during and after each event). The deployment of APIT will not only contribute to climate science as a way of providing near-real time locality information of polynya openings, but will also act as an early warning system, using machine-learning algorithms alongside open-source near-real time data enabling the re-routing of research vessels to take in-situ measurements. Providing opportunities for field research to take place during the life cycle of a ...