SalaciaML-2-Arctic — A deep learning quality control algorithm for Arctic ocean temperature and salinity data

Sebastian Mieruch^1*Gastón Kreps¹Mohamed Chouai²Felix Reimers³Myriel Vredenborg¹Benjamin Rabe¹Sandra Tippenhauer¹Axel Behrendt⁴

• ¹Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven, Germany
• ²Technische Hochschule Ingolstadt, Ingolstadt, Germany
• ³Hogskolen i Ostfold, Halden, Norway
• ⁴Independent Researcher, Holzminden, Germany

We have extended a classical quality control (QC) algorithm by integrating a deep learning neural network, resulting in SalaciaML-2-Arctic, a tool for automated QC of Arctic Ocean temperature and salinity profile data. The neural network component was trained on the Unified Database for Arctic and Subarctic Hydrography (UDASH), which has been quality-controlled and labeled by expert oceanographers. SalaciaML-2-Arctic successfully reproduces human expertise by correcting misclassifications made by the classical algorithm, reducing False Negatives (samples incorrectly classified as "bad") by 96 % for temperature and 99 % for salinity. When used in combination with a visual post-QC by human experts, it achieves a workload reduction of approximately 60 % for temperature and 85 % for salinity. All code and data required to reproduce the analysis or apply the method to other datasets are openly available via PANGAEA and GitHub. Moreover, SalaciaML-2-Arctic is accessible as a browser-based application at https: //mvre.autoqc.cloud.awi.de, enabling its use without software installation or programming knowledge.