大气动力学神经闭合的高频与稀有事件障碍
The High-Frequency and Rare Events Barriers to Neural Closures of Atmospheric Dynamics
AI总结：
本文指出神经网络参数化气候湍流模型的两个根本障碍：稀有事件采样困难与多尺度高频慢变耦合，并以1980年Lorenz模型为例说明。
摘要：
近年来，利用神经网络对气候和湍流模型中的小尺度或快速过程进行参数化的兴趣激增。在这篇短文中，我们指出了这一努力中的两个基本问题。第一个问题涉及神经网络在捕捉稀有事件时可能遇到的困难，这是由于数据采样方式的局限性所致。第二个问题源于这些系统固有的多尺度性质。它们将高频成分（如惯性重力波）与较慢的演化过程（地转运动）相结合。这种多尺度性质为神经闭合带来了重大障碍。为了说明这些挑战，我们聚焦于大气1980年Lorenz模型，这是驱动气候模型的原始方程的一个简化版本。该模型作为一个引人注目的例子，因为它抓住了这些困难的本质。
英文摘要：
Recent years have seen a surge in interest for leveraging neural networks to parameterize small-scale or fast processes in climate and turbulence models. In this short paper, we point out two fundamental issues in this endeavor. The first concerns the difficulties neural networks may experience in capturing rare events due to limitations in how data is sampled. The second arises from the inherent multiscale nature of these systems. They combine high-frequency components (like inertia-gravity waves) with slower, evolving processes (geostrophic motion). This multiscale nature creates a significant hurdle for neural network closures. To illustrate these challenges, we focus on the atmospheric 1980 Lorenz model, a simplified version of the Primitive Equations that drive climate models. This model serves as a compelling example because it captures the essence of these difficulties.
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。
