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We propose a new method for combining in situ buoy measurements with Earthsystem models (ESMs) to improve the accuracy of temperature predictions in theocean. The technique utilizes the dynamics \textit{and} modes identified inESMs alongside buoy measurements to improve accuracy while preserving featuressuch as seasonality. We use this technique, which we call Dynamic BasisFunction Interpolation, to correct errors in localized temperature predictionsmade by the Model for Prediction Across Scales Ocean component (MPAS-O) withthe Global Drifter Program's in situ ocean buoy dataset.

Dynamic Basis Function Interpolation for Adaptive In Situ Data Integration in Ocean Modeling