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Abstract.A mesospheric front was observed with an allsky airglow imager on the night of 9-10 July 2007 at Ferraz Station (62 • S, 58 • W), located on King George island on the Antarctic Peninsula. The observed wave propagated from southwest to northeast with a well defined wave front and a series of crests behind the main front. The wave parameters were obtained via a 2-D Fourier transform of the imager data providing a horizontal wavelength of 33 km, an observed period of 6 min, and a horizontal phase speed of 92 m s −1 . Simultaneous mesospheric winds were measured with a medium frequency (MF) radar at Rothera Station (68 • S, 68 • W) and temperature profiles were obtained from the SABER instrument on the TIMED satellite. These wind and temperature profiles were used to estimate the propagation environment of the wave event. A wavelet technique was applied to the wind in the plane of wave propagation at the OH emission height spanning three days centered on the front event to define the dominant periodicities. Results revealed a dominance of near-inertial periods, and semi-diurnal and terdiurnal tides suggesting that the ducting structure enabling mesospheric front propagation occurred on large spatial scales. The observed tidal motions were used to reconstruct the winds employing a least-squares method, which were then compared to the observed ducting environment. Results suggest an important contribution of largescale winds to the ducting structure, but with buoyancy frequency variations in the vertical also expected to be important. These results allow us to conclude that the wave front event was supported by a duct including contributions from both winds and temperature.Correspondence to:

Abstract.A medium frequency spaced-antenna radar has been operating at Rothera station, Antarctica (67 • S, 68 • W) for two periods, between 1997-1998 and since 2002, measuring winds in the mesosphere and lower thermosphere. In this paper monthly mean winds are derived and presented along with three years of radiosonde balloon data for comparison with the HWM-93 model atmosphere and other high latitude southern hemisphere sites. The observed meridional winds are slightly more northwards than those predicted by the model above 80 km in the winter months and below 80 km in summer. In addition, the altitude of the summer time zero crossing of the zonal winds above the westward jet is overestimated by the model by up to 8 km. These data are then merged with the wind climatology obtained from falling sphere measurements made during the PORTA campaign at Rothera in early 1998 and the HWM-93 model atmosphere to generate a complete zonal wind climatology between 0 and 100 km as a benchmark for future studies at Rothera. A westwards (eastwards) maximum of 44 ms −1 at 67 km altitude occurs in mid December (62 ms −1 at 37 km in mid July). The 0 ms −1 wind contour reaches a maximum altitude of 90 km in mid November and a minimum altitude of 18 km in January extending into mid March at 75 km and early October at 76 km.

A mesospheric bore was observed with an all-sky airglow imager on the night of 9–10 July 2007 at Ferraz Station (62° S, 58° W), located on King George island on the Antarctic Peninsula. The observed bore propagated from southwest to northeast with a well defined wave front and a series of crests behind the main front. There was no evidence of dissipation during its propagation within the field of view. The wave parameters were obtained via a 2-D Fourier transform of the imager data providing a horizontal wavelength of 33 km, an observed period of 6 min, and a horizontal phase speed of 92 m s^−1. Simultaneous mesospheric winds were measured with a medium frequency (MF) radar at Rothera Station (68° S, 68° W) and temperature profiles were obtained from the SABER instrument on the TIMED satellite. These wind and temperature profiles were used to estimate the propagation environment of the bore. A wavelet technique was applied to the wind in the plane of bore propagation at the OH emission height spanning three days centered on the bore event to define the dominant periodicities. Results revealed a dominance of near-inertial periods, and semi-diurnal and terdiurnal tides suggesting that the ducting structure enabling bore propagation occurred on large spatial scales. The observed tidal motions were used to reconstruct the winds employing a least-squares method, which were then compared to the observed ducting environment. Results suggest an important contribution of large-scale winds to the ducting structure, but with buoyancy frequency variations in the vertical also expected to be important. These results allow us to conclude that the bore was supported by a duct including contributions from both winds and temperature (or stability). A co-located airglow temperature imager operated simultaneously with the all-sky imager confirmed that the bore event was the dominant small-scale wave event during the analysis interval

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. GWs are well described by the Navier-Stokes equations, but solving these equations extending to very small scales remains daunting, and is limited by the computational costs of resolving the smallest important spatio-temporal features in a large-scale environment. This has led to developments of a wide variety of GW parameterization schemes for regional and global atmospheric models. Traditionally, GW parameterizations are based on linear theory, and no parameterization of secondary GWs (SGWs) has been developed to date. In addition, there remain many aspects of GW dynamics (e.g., GW self-acceleration, instability, GW breaking, SGW generation, and multi-scale interactions) that are important to describe, but cannot be addressed by linear theory or existing schemes. Here we describe an initial, two-dimensional (2-D), machine learning model – the Compressible Atmosphere Model Neural Network (CAMNet) - intended as a first step toward a more general, three-dimensional, highly-efficient, model for applications to nonlinear GW dynamics description. CAMNet employs a physics-informed neural operator and GPU hardware to dramatically accelerate GW and SGW simulations applied to two GW sources to date. CAMNet 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 SGW generation in two wind environments are described here. Results show that CAMNet can capture the key 2-D dynamics modeled by CGCAM with high precision. Spectral characteristics of primary and SGWs estimated by CAMNet agree well with those from CGCAM. Our results show that CAMNet 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.