Separating Sensor-like Anomalies from Regional Oceanographic Events: A Machine-Learning-Assisted, Physics-Guided, Event-Preserving Quality-Control Framework for Coastal Buoy Temperature Records
Abstract
Coastal upwelling and typhoon-driven mixing can cool a buoy record by several degrees within hours. Sensor faults do the same. Quality-control schemes that flag anomalies by residual magnitude alone therefore risk discarding real events. We analysed 30 min temperature records from six buoys and three depths off the east coast of Korea, spanning 2008–2024, and built a machine-learning-assisted, physics-guided, event-preserving quality-control framework that adds new labels without altering any observation or existing flag. Cooling events were catalogued from changes in the observed surface temperature and in the surface-to-bottom temperature difference and classified using three physically interpretable axes: spatial coherence with neighbouring buoys, vertical consistency between layers, and atmospheric forcing from ERA5 and typhoon best-track data. A station-wise ridge prediction model, fitted to the surface layer at five of the six stations, supplied prediction residuals that served only to flag candidates. Residual magnitude separated sensor-like anomalies from regional-event candidates poorly (direction-free AUC 0.52–0.56); upwelling-candidate and typhoon-related events produced residuals as large as those of the sensor-like reference group, or larger. The physical axes showed much stronger internal operational separability, reaching pairwise AUC values up to 1.000 and a multivariate cross-validated mean AUC of 0.987. These values do not represent external validation because the groups were partly defined using the same axes. The framework preserved regional-event candidates while affecting derived monthly means by at most about 0.0005 °C, yet retained event-scale cooling of up to about 8 °C. Prediction residuals are therefore useful for broad anomaly-candidate detection but insufficient for final event classification, which should rely on physically interpretable, multi-station criteria.