Quantifying 3D Gravity Wave Drag in a Library of Tropical Convection-Permitting Simulations for Data-Driven Parameterizations

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dc.citation.articleNumbere2022MS003585
dc.citation.issueNumber5
dc.citation.journalTitleJournal of Advances in Modeling Earth Systems
dc.citation.volumeNumber15
dc.contributor.authorSun, Y. Qiang
dc.contributor.authorHassanzadeh, Pedram
dc.contributor.authorAlexander, M. Joan
dc.contributor.authorKruse, Christopher G.
dc.date.accessioned2023-07-21T16:13:36Z
dc.date.available2023-07-21T16:13:36Z
dc.date.issued2023
dc.description.abstractAtmospheric 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.
dc.identifier.citationSun, Y. Qiang, Hassanzadeh, Pedram, Alexander, M. Joan, et al.. "Quantifying 3D Gravity Wave Drag in a Library of Tropical Convection-Permitting Simulations for Data-Driven Parameterizations." <i>Journal of Advances in Modeling Earth Systems,</i> 15, no. 5 (2023) Wiley: https://doi.org/10.1029/2022MS003585.
dc.identifier.digital2023-Sun
dc.identifier.doihttps://doi.org/10.1029/2022MS003585
dc.identifier.urihttps://hdl.handle.net/1911/114969
dc.language.isoeng
dc.publisherWiley
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial (CC BY-NC) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of Fair Use or other exemptions to copyright law must be obtained from the copyright holder.
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.titleQuantifying 3D Gravity Wave Drag in a Library of Tropical Convection-Permitting Simulations for Data-Driven Parameterizations
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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