技艺娴熟的全球海洋模拟与相关意识损失的作用
Skillful Global Ocean Emulation and the Role of Correlation-Aware Loss
AI总结：
本文提出一种专为海洋设计的模拟器，利用相关意识损失提升预测能力，通过空间相关性分析改进全球海洋动力学的背景预测。
摘要：
机器学习模拟器在预测大气状态方面表现出色，其应用于全球海洋动力学也具有类似前景。本文将GraphCast架构改编为专用海洋模拟器，由预设的大气条件驱动，用于中短期预测。模拟器在NOAA的UFS-Replay数据集上训练，采用24小时时间步长、单初始条件和非自回归训练，产生一个能够提供10-15天预测能力的模拟器。进一步展示使用Mahalanobis距离作为损失函数，相较于均方误差损失，通过显式考虑目标变量趋势之间的相关性，提高了预测能力。通过预报字段的空间相关性分析，还表明所提出的相关意识损失作为统计-动力学正则化器，有助于全球海洋的慢速、相关动态，为下游任务如数据同化提供更好的背景预测。
英文摘要：
Machine learning emulators have shown extraordinary skill in forecasting atmospheric states, and their application to global ocean dynamics offers similar promise. Here, we adapt the GraphCast architecture into a dedicated ocean-only emulator, driven by prescribed atmospheric conditions, for medium-range predictions. The emulator is trained on NOAA's UFS-Replay dataset. Using a 24 hour time step, single initial condition, and without using autoregressive training, we produce an emulator that provides skillful forecasts for 10-15 day lead times. We further demonstrate the use of Mahalanobis distance as loss that improves the forecast skill compared to the Mean Squared Error loss by explicitly accounting for the correlations between tendencies of the target variables. Using spatial correlation analysis of the forecasted fields, we also show that the proposed correlation-aware loss acts as a statistical-dynamical regularizer for the slow, correlated dynamics of the global oceans, offering a better background forecast for downstream tasks like data assimilation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
