We report the first lidar observations of neutral Fe layers with gravity wave signatures in the thermosphere from 110–155 km at McMurdo, Antarctica in May 2011. The thermospheric Fe densities are low, ranging from ∼200 cm−3 at 120 km to ∼20 cm−3 at 150 km. The measured temperatures from 115–135 km are considerably warmer than MSIS and appear to be related to Joule heating enhanced by aurora. The observed waves originate in the lower atmosphere and show periods of 1.5–2 h through 77–155 km. The vertical wavelength increases from ∼13 km at 115 km to ∼70 km at 150 km altitude. These wave characteristics are strikingly similar to the traveling ionospheric disturbances caused by internal gravity waves. The thermospheric Fe layers are likely formed through the neutralization of vertically converged Fe+ layers that descend in height following the gravity wave downward phase progression.

We examine the characteristics of secondary gravity waves (GWs) excited by a localized (in space) and intermittent (in time) body force in the atmosphere. This force is a horizontal acceleration of the background flow created when primary GWs dissipate and deposit their momentum on spatial and temporal scales of the wave packet. A broad spectrum of secondary GWs is excited with horizontal scales much larger than that of the primary GW. The polarization relations cause the temperature spectrum of the secondary GWs generally to peak at larger intrinsic periods τIr and horizontal wavelengths λH than the vertical velocity spectrum. We find that the one‐dimensional spectra (with regard to frequency or wave number) follow lognormal distributions. We show that secondary GWs can be identified by a horizontally displaced observer as “fishbone” or “>” structures in z − t plots whereby the positive and negative GW phase lines meet at the “knee,” zknee, which is the altitude of the force center. We present two wintertime cases of lidar temperature measurements at McMurdo, Antarctica (166.69°E, 77.84°S) whereby fishbone structures are seen with zknee=43 and 52 km. We determine the GW parameters and density‐weighted amplitudes for each. We show that these parameters are similar below and above zknee. We verify that the GWs with upward (downward) phase progression are downward (upward) propagating via use of model background winds. We conclude that these GWs are likely secondary GWs having ground‐based periods τr=6–10 hr and vertical wavelengths λz=6–14 km, and that they likely propagate primarily southward.

NRLMSIS  2.0 is an empirical atmospheric model that extends from the ground to the exobase and describes the average observed behavior of temperature, 8 species densities, and mass density via a parametric analytic formulation. The model inputs are location, day of year, time of day, solar activity, and geomagnetic activity. NRLMSIS 2.0 is a major, reformulated upgrade of the previous version, NRLMSISE-00. The model now couples thermospheric species densities to the entire column, via an effective mass profile that transitions each species from the fully mixed region below ~70 km altitude to the diffusively separated region above ~200 km. Other changes include the extension of atomic oxygen down to 50 km and the use of geopotential height as the internal vertical coordinate. We assimilated extensive new lower and middle atmosphere temperature, O, and H data, along with global average thermospheric mass density derived from satellite orbits, and we validated the model against independent samples of these data. In the mesosphere and below, residual biases and standard deviations are considerably lower than NRLMSISE-00. The new model is warmer in the upper troposphere and cooler in the stratosphere and mesosphere. In the thermosphere, N2 and O densities are lower in NRLMSIS 2.0; otherwise, the NRLMSISE-00 thermosphere is largely retained. Future advances in thermospheric specification will likely require new in situ mass spectrometer measurements, new techniques for species density measurement between 100 and 200 km, and the reconciliation of systematic biases among thermospheric temperature and composition datasets, including biases attributable to long-term changes.