重力波が大気に与える影響の解読 (Decoding atmospheric effects of gravity waves)

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2024-12-23 オークリッジ国立研究所(ORNL)

スタンフォード大学、欧州中期予報センター(ECMWF)、オークリッジ国立研究所(ORNL)の研究者は、スーパーコンピューター「Summit」を活用し、大気重力波の詳細なシミュレーションを実施しました。この研究では、気候モデルとデータ解析を通じて、従来より2倍の解像度で重力波の動態を解明しました。この成果により、極端気象の予測精度向上が期待されています。また、これらのデータは機械学習モデルのトレーニングにも役立つとされています。

<関連情報>

1kmグローバルECMWF統合予報システムによる重力波運動量フラックス Gravity Wave Momentum Fluxes from 1 km Global ECMWF Integrated Forecast System

Aman Gupta,Aditi Sheshadri & Valentine Anantharaj
Scientific Data  Published:21 August 2024
DOI:https://doi.org/10.1038/s41597-024-03699-x

重力波が大気に与える影響の解読 (Decoding atmospheric effects of gravity waves)

Abstract

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.

1702地球物理及び地球化学
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