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Equatorial Plasma Bubbles (EPBs) are a region of depleted ionospheric densities. EPBs are known to fluctuate both seasonally and day to day, and have been linked to changes in solar activity, geomagnetic activity, and seeding resulting from dynamics occurring at lower altitudes. Here, EPB activity is investigated over a 15-day period with overlapping coincident ground-based 630 nm oxygen airglow measurements, near-infrared hydroxyl mesospheric temperature mapper (MTM) measurements, and Rate Of change of Total Electron Content Index (ROTI) values. The data are compared with the Navy Global Environmental Model (NAVGEM) reanalysis over the same time period. It is found that several days with strong EPB activity coincided with the positive/northward meridional wind phase of the quasi-two-day wave (QTDW) in the mesosphere. These initial observations indicate correlations of the QTDW phase and the occurrence rates of EPBs, and suggest a need for further investigations to assess potential causal relationships that may affect the variability and prevalence of EPBs. 

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.