Authors:

Aman Gupta, Aditi Sheshadri, and Valentine Anantharaj

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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.

Plain Language Summary:

This study presents data on atmospheric gravity waves (GWs) obtained from a high-resolution global simulation conducted by the European Centre for Medium-Range Weather Forecasts (ECMWF). The simulation, referred to as a “nature run,” was performed at a 1 km horizontal resolution over a four-month period. The dataset includes momentum fluxes associated with GWs, which are crucial for understanding their impact on atmospheric dynamics. By providing detailed information on GW momentum fluxes, this dataset aims to enhance the representation of GWs in weather and climate models, leading to improved predictions and a better grasp of atmospheric processes.