非局部重力波传播的机器学习全球模拟
Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation
AI总结：
研究提出基于Attention U-Net架构的机器学习模型，利用WINDSET数据集首次实现大气重力波通量的全球非局部模拟，克服了传统单柱参数化忽略水平演变的缺陷。
摘要：
全球气候模型通常在数百公里的网格分辨率下运行，无法解析云、降水和重力波（GWs）等大气中尺度过程。模型对这些过程及其源的表征对全球环流和行星能量收支至关重要，但来自这些过程的次网格尺度贡献通常仅通过参数化方案在模型中被近似表征。这些参数化方案受限于近似和理想化，影响了其能力和精度。其中最极端的近似是“单柱近似”，它完全忽略了这些过程的水平演变，导致当前气候模型出现关键偏差。以大气GWs为重点，我们首次利用在WINDSET数据集上训练的机器学习（ML）模型进行了大气GW通量的全球模拟，以模拟大气中的全球GW，作为传统单柱参数化方案的替代。采用基于Attention U-Net的架构并在全球解析的GW动量通量上进行训练，我们证明了在使用数据驱动方案模拟GWs时，全球非局部性的重要性与有效性。
英文摘要：
Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs). Model representation of these processes and their sources is essential to the global circulation and planetary energy budget, but subgrid scale contributions from these processes are often only approximately represented in models using parameterizations. These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy. The most drastic of these approximations is the "single-column approximation" which completely neglects the horizontal evolution of these processes, resulting in key biases in current climate models. With a focus on atmospheric GWs, we present the first-ever global simulation of atmospheric GW fluxes using machine learning (ML) models trained on the WINDSET dataset to emulate global GW emulation in the atmosphere, as an alternative to traditional single-column parameterizations. Using an Attention U-Net-based architecture trained on globally resolved GW momentum fluxes, we illustrate the importance and effectiveness of global nonlocality, when simulating GWs using data-driven schemes.
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