标题： 基于物理一致性的深度学习区域海洋模拟器的同步模拟和降尺度 显示英文标题

标题： Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators

摘要： 在基于人工智能的大气模拟取得成功的基础上，我们提出了一种基于人工智能的海洋模拟和降尺度框架，重点关注墨西哥湾的高分辨率区域海洋。 区域海洋模拟由于复杂的地形和侧向边界条件以及基于深度学习的框架中的基本偏差（如不稳定性和幻觉）而面临独特的挑战。 在本文中，我们开发了一个基于深度学习的框架，能够在墨西哥湾以$8$ Km 的空间分辨率自回归地整合海洋表层变量，在十年时间尺度上没有不合理的漂移，并同时使用物理约束的生成模型将其降尺度并偏差校正到$4$ Km 分辨率。 该框架在均值和变异性方面既具有短期技能，也具有准确的长期统计特性。 显示英文摘要

摘要： 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.