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Abstract Results of two‐dimensional and narrow three‐dimensional (2‐D and 2.5‐D) simulations of a gravity wave (GW) packet localized in altitude and along its propagation direction employing a new, versatile compressible model are described. The simulations explore self‐acceleration and instability dynamics in an idealized atmosphere at rest under mean solar conditions in a domain extending to an altitude of 260 km and 1,800 km horizontally without artificial dissipation. High resolution in the central 2.5‐D domain enables the description of 3‐D instability dynamics accounting for breaking, dissipation, and momentum deposition within the GW packet. 2‐D results describe responses to localized self‐acceleration effects, including generation of secondary GWs (SGWs) at larger scales able to propagate to much higher altitudes. 2.5‐D results exhibit instability forms consistent with previous 3‐D simulations of instability dynamics and cause SGW generation and propagation at smaller spatial scales to weaken significantly compared to the 2‐D results. SGW responses at larger scales are driven primarily by GW/mean flow interactions arising at early stages of the self‐acceleration dynamics prior to strong GW instabilities and dissipation. As a result, they exhibit similar responses in both the 2‐D and 2.5‐D simulations and readily propagate to high altitudes at large distances from the initial GW packet. A companion paper examines these dynamics for an initial GW packet localized in three dimensions and evolving in a representative 3‐D tidal wind field.
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Accelerating Atmospheric Gravity Wave Simulations Using Machine Learning: Kelvin‐Helmholtz Instability and Mountain Wave Sources Driving Gravity Wave Breaking and Secondary Gravity Wave Generation
Abstract Gravity waves (GWs) and their associated multi‐scale dynamics are known to play fundamental roles in energy and momentum transport and deposition processes throughout the atmosphere. We describe an initial machine learning model—the Compressible Atmosphere Model Network (CAM‐Net). CAM‐Net is trained on high‐resolution simulations by the state‐of‐the‐art model Complex Geometry Compressible Atmosphere Model (CGCAM). Two initial applications to a Kelvin‐Helmholtz instability source and mountain wave generation, propagation, breaking, and Secondary GW (SGW) generation in two wind environments are described here. Results show that CAM‐Net can capture the key 2‐D dynamics modeled by CGCAM with high precision. Spectral characteristics of primary and SGWs estimated by CAM‐Net agree well with those from CGCAM. Our results show that CAM‐Net can achieve a several order‐of‐magnitude acceleration relative to CGCAM without sacrificing accuracy and suggests a potential for machine learning to enable efficient and accurate descriptions of primary and secondary GWs in global atmospheric models.
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- PAR ID:
- 10441540
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 50
- Issue:
- 15
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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