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

TL;DR

This paper introduces Dynamic Basis Function Interpolation, a novel method that combines in situ buoy data with Earth system models to enhance ocean temperature prediction accuracy while maintaining seasonal features.

Contribution

It presents a new interpolation technique that leverages model dynamics and modes to improve in situ data integration in ocean modeling.

Findings

Improved temperature prediction accuracy in ocean models.

Effective correction of localized temperature errors.

Preservation of seasonal features in predictions.

Abstract

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.