Quantifying 3D Gravity Wave Drag in a Library of Tropical Convection‐Permitting Simulations for Data‐Driven Parameterizations
Quantifying 3D Gravity Wave Drag in a Library of Tropical Convection‐Permitting Simulations for Data‐Driven Parameterizations
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DOI：
10.1029/2022ms003585
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发表时间：
2023-05
影响因子：
6.8
通讯作者：
Y. Q. Sun;P. Hassanzadeh;M. Alexander;C. Kruse
中科院分区：
文献类型：
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作者：
Y. Q. Sun;P. Hassanzadeh;M. Alexander;C. Kruse
大气重力波(GW)跨越了大范围的长度尺度。因此，在大气环流模式(GCM)中，未分辨和欠分辨的GWS必须用次网格尺度(SGS)参数化来表示。近年来，机器学习(ML)技术已经成为气候过程SGS建模的新方法。在广泛使用的监督(离线)学习方法中，必须从高保真数据(例如，GW分辨率模拟)中适当地提取SGS项的真实表示。然而，这不是一项微不足道的任务，基于ML的参数化的质量在很大程度上取决于这些SGS项的质量。在这里，我们比较了三种从高分辨率模拟中提取3DGW通量和由此产生的阻力(重力波阻力[GWD])的方法：Helmholtz分解和空间滤波来计算雷诺应力和全部SGS应力。除了以前只关注GWS垂直通量的研究外，我们还量化了由于横向动量通量引起的SGS GWD。我们利用天气研究和预报模式建立并利用了热带高分辨率(Δx=3公里)模拟库。结果表明，SGS的横向动量通量对GWD总量的贡献较大。此外，在估算由于侧向效应引起的GWD时，需要考虑SGS和所解析的大尺度流动之间的相互作用。结果对不同的过滤器类型和长度尺度(依赖于GCM分辨率)的敏感性也被考察，以告知数据驱动的参数化发展中的尺度意识。
Atmospheric gravity waves (GWs) span a broad range of length scales. As a result, the un‐resolved and under‐resolved GWs have to be represented using a sub‐grid scale (SGS) parameterization in general circulation models (GCMs). In recent years, machine learning (ML) techniques have emerged as novel methods for SGS modeling of climate processes. In the widely used approach of supervised (offline) learning, the true representation of the SGS terms have to be properly extracted from high‐fidelity data (e.g., GW‐resolving simulations). However, this is a non‐trivial task, and the quality of the ML‐based parameterization significantly hinges on the quality of these SGS terms. Here, we compare three methods to extract 3D GW fluxes and the resulting drag (Gravity Wave Drag [GWD]) from high‐resolution simulations: Helmholtz decomposition, and spatial filtering to compute the Reynolds stress and the full SGS stress. In addition to previous studies that focused only on vertical fluxes by GWs, we also quantify the SGS GWD due to lateral momentum fluxes. We build and utilize a library of tropical high‐resolution (Δx = 3 km) simulations using weather research and forecasting model. Results show that the SGS lateral momentum fluxes could have a significant contribution to the total GWD. Moreover, when estimating GWD due to lateral effects, interactions between the SGS and the resolved large‐scale flow need to be considered. The sensitivity of the results to different filter type and length scale (dependent on GCM resolution) is also explored to inform the scale‐awareness in the development of data‐driven parameterizations.