A deep learning (DL) model, based on a transformer architecture, is trained on a climate-model dataset and compared with a standard linear inverse model (LIM) in the tropical Pacific. We show that the DL model produces more accurate forecasts compared to the LIM when tested on a reanalysis dataset. We then assess the ability of an ensemble Kalman filter to reconstruct the monthly-averaged upper ocean from a noisy set of 24 sea-surface temperature observations designed to mimic existing coral proxy measurements, and compare results for the DL model and LIM. Due to signal damping in the DL model, we implement a novel inflation technique by adding noise from hindcast experiments. Results show that assimilating observations with the DL model yields better reconstructions than the LIM for observation averaging times ranging from one month to one year. The improved reconstruction is due to the enhanced predictive capabilities of the DL model, which map the memory of past observations to future assimilation times.

使用基于Transformer架构的深度学习模型，与标准线性逆模型（LIM）在热带太平洋上进行训练与比较。结果表明，当在重分析数据集上测试时，与LIM相比，深度学习模型产生更准确的预测。然后我们评估集合卡尔曼滤波器从一组噪声较大的24个海表温度观测中重建月平均上层海洋的能力，并比较DL模型和LIM的结果。由于DL模型中信号的衰减，我们引入了一种新的膨胀技术，通过从回查实验中添加噪声来实现。结果显示，使用DL模型同化观测数据比LIM在观测时间从一个月到一年的范围内获得更好的重建效果。这种改进的重建是由于DL模型的增强预测能力，它可以将过去观测的信息映射到未来的同化时间。

利用深度学习模型进行在线数据同化重建热带太平洋上层海洋