Dynamic Basis Function Interpolation for Adaptive In Situ Data Integration in Ocean Modeling
This addresses the challenge of adaptive data integration for ocean modeling, which is incremental as it builds on existing ESMs and buoy datasets.
The paper tackles the problem of improving ocean temperature prediction accuracy by combining in situ buoy measurements with Earth system models, resulting in a method that corrects errors in localized temperature predictions using MPAS-O and buoy data.
We propose a new method for combining in situ buoy measurements with Earth system models (ESMs) to improve the accuracy of temperature predictions in the ocean. The technique utilizes the dynamics \textit{and} modes identified in ESMs alongside buoy measurements to improve accuracy while preserving features such as seasonality. We use this technique, which we call Dynamic Basis Function Interpolation, to correct errors in localized temperature predictions made by the Model for Prediction Across Scales Ocean component (MPAS-O) with the Global Drifter Program's in situ ocean buoy dataset.