用AI同时实现墨西哥湾海洋模拟与高分辨率降尺度，保持物理一致性。
Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators
- 基于深度学习构建自回归框架，实现8公里分辨率海洋模拟。
- 在十年尺度上无非物理解漂移，且可同步下放到4公里分辨率。
- 适合关注区域海洋高精度模拟的气候与海洋研究者。
在人工智能驱动大气模拟取得成功的基础上，本文提出一种面向墨西哥湾高分辨率区域海洋的AI模拟与降尺度框架。区域海洋模拟因复杂地形、侧边界条件以及深度学习框架固有的不稳定性与幻觉问题而面临独特挑战。本文开发了一种深度学习框架，可自回归地整合墨西哥湾的海表变量，在8公里空间分辨率下运行，且在十年时间尺度上无非物理解漂移；同时利用物理约束生成模型，将结果下放并偏差校正至4公里分辨率。该框架在短期预测和长期均值与变率统计方面均表现出色。
原文摘要 · Abstract (English)
Building on top of the success in AI-based atmospheric emulation, we propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean over Gulf of Mexico. Regional ocean emulation presents unique challenges owing to the complex bathymetry and lateral boundary conditions as well as from fundamental biases in deep learning-based frameworks, such as instability and hallucinations. In this paper, we develop a deep learning-based framework to autoregressively integrate ocean-surface variables over the Gulf of Mexico at $8$ Km spatial resolution without unphysical drifts over decadal time scales and simulataneously downscale and bias-correct it to $4$ Km resolution using a physics-constrained generative model. The framework shows both short-term skills as well as accurate long-term statistics in terms of mean and variability.
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