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

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dc.citation.articleNumbere2022MS003585en_US
dc.citation.issueNumber5en_US
dc.citation.journalTitleJournal of Advances in Modeling Earth Systemsen_US
dc.citation.volumeNumber15en_US
dc.contributor.authorSun, Y. Qiangen_US
dc.contributor.authorHassanzadeh, Pedramen_US
dc.contributor.authorAlexander, M. Joanen_US
dc.contributor.authorKruse, Christopher G.en_US
dc.date.accessioned2023-07-21T16:13:36Zen_US
dc.date.available2023-07-21T16:13:36Zen_US
dc.date.issued2023en_US
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.en_US
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.en_US
dc.identifier.digital2023-Sunen_US
dc.identifier.doihttps://doi.org/10.1029/2022MS003585en_US
dc.identifier.urihttps://hdl.handle.net/1911/114969en_US
dc.language.isoengen_US
dc.publisherWileyen_US
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.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_US
dc.titleQuantifying 3D Gravity Wave Drag in a Library of Tropical Convection-Permitting Simulations for Data-Driven Parameterizationsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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