Gupta Aman, Sheshadri Aditi, Anantharaj Valentine
Department of Earth System Science, Stanford University, Stanford, USA.
Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
Sci Data. 2024 Aug 21;11(1):903. doi: 10.1038/s41597-024-03699-x.
Progress in understanding the impact of mesoscale variability, including gravity waves (GWs), on atmospheric circulation is often limited by the availability of global fine-resolution observations and simulated data. This study presents momentum fluxes due to atmospheric GWs extracted from four months of an experimental "nature run", integrated at a 1 km resolution (XNR1K) using the Integrated Forecast System (IFS) model. Helmholtz decomposition is used to compute zonal and meridional flux of vertical momentum from ~1.5 petabytes of data; quantities often emulated by climate model parameterization of GWs. The fluxes are validated using ERA5 reanalysis, both during the first week after initialization and over the boreal winter period from November 2018 to February 2019. The agreement between reanalysis and IFS demonstrates its capability to generate reliable flux distributions and capture mesoscale dynamic variability in the atmosphere. The dataset could be valuable in advancing our understanding of GW-planetary wave interactions, GW evolution around atmospheric extremes, and as high-quality training data for machine learning (ML) simulation of GWs.
在理解中尺度变率(包括重力波)对大气环流的影响方面,进展常常受到全球高分辨率观测数据和模拟数据可用性的限制。本研究展示了从一个为期四个月的实验性“自然运行”中提取的大气重力波引起的动量通量,该实验使用综合预报系统(IFS)模型以1公里分辨率(XNR1K)进行积分。利用亥姆霍兹分解从约1.5PB的数据中计算垂直动量的纬向和经向通量;这些量通常由重力波的气候模型参数化来模拟。通量在初始化后的第一周以及2018年11月至2019年2月的北半球冬季期间,使用ERA5再分析进行了验证。再分析与IFS之间的一致性表明其有能力生成可靠的通量分布并捕捉大气中的中尺度动力变率。该数据集对于推进我们对重力波 - 行星波相互作用、大气极端事件周围重力波演变的理解,以及作为重力波机器学习(ML)模拟的高质量训练数据可能具有重要价值。