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.

翻译：我们提出了一种将原位浮标测量数据与地球系统模型（ESM）相结合的新方法，旨在提高海洋温度预测的准确性。该技术同时利用ESM中识别的动力学模态与浮标测量数据，在保持季节性等特征的同时提升预测精度。我们应用这一称为动态基函数插值的技术，结合全球漂流浮标计划（GDP）的原位海洋浮标数据集，对跨尺度海洋预测模型（MPAS-O）的局域温度预测误差进行校正。