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We have discovered that the peak phase time of predawn thermosphere‐ionosphere Na (TINa) layers (∼110–150 km altitude) undergoes clear annual variations with the earliest occurrence in summer and latest in winter over Boulder (40.13°N, 105.24°W), which are closely correlated to annual phase variations of sunrise and tidal winds. Such discoveries were enabled by the first characterization of 12 monthly composites of TINa layers from January through December using 7 years of lidar observations (2011–2017). Despite their tenuous densities, the predawn TINa layers have nearly 100% occurrence rate (160 out of 164 nights of observations). Monthly composites show downward‐phase‐progression TINa descending at similar phase speeds as Climatological Tidal Model of the Thermosphere tidal winds. These TINa layers occur in ion convergence but neutral divergence regions, modeled using tidal winds. These results support the formation mechanism (neutralization of converged TINa⁺forming TINa) proposed previously and suggest that migrating tidal winds experience annual phase variations.

We report the first simultaneous lidar observations of thermosphere‐ionosphere sporadic nickel and Na (TISNi and TISNa) layers in altitudes ∼105–120 km over Yanqing (40.42°N, 116.02°E), Beijing. From two years of data spanning April 2019 to April 2020 and July 2020 to June 2021, TISNi layers in May and June possess high densities with a maximum of 818 cm⁻³on 17 May 2021, exceeding the density of main layer peak (∼85 km) by ∼4 times. They correlate with strong sporadic E layers observed nearby. TISNa layers occur at similar altitudes as TISNi with spatial‐temporal correlation coefficients of ∼1. The enrichment of Ni in TISNi is evident as the [TISNi]/[TISNa] column abundance ratios are ∼1, about 10 times the main layer [Ni]/[Na] ratios. These results are largely explained by neutralization of converged Ni⁺and Na⁺ions via recombination with electrons. Calculations show direct recombination dominating over dissociative recombination above ∼105 km.

We report the first lidar observations of vertical fluxes of sensible heat and meteoric Na from 78 to 110 km in late May 2020 at McMurdo, Antarctica. The measurements include contributions from the complete temporal spectrum of gravity waves and demonstrate that wave‐induced vertical transport associated with atmospheric mixing by non‐breaking gravity waves, Stokes drift imparted by the wave spectrum, and perturbed chemistry of reactive species, can make significant contributions to constituent and heat transport in the mesosphere and lower thermosphere (MLT). The measured sensible heat and Na fluxes exhibit downward peaks at 84 km (−3.0 Kms⁻¹and −5.5 × 10⁴ cm⁻²s⁻¹) that are ∼4 km lower than the flux peak altitudes observed at midlatitudes. This is likely caused by the strong downwelling over McMurdo in late May. The Na flux magnitude is double the maximum at midlatitudes, which we believe is related to strong persistent gravity waves in the MLT at McMurdo. To achieve good agreement between the measured Na flux and theory, it was necessary to infer that a large fraction of gravity wave energy was propagating downward, especially between 80 and 95 km where the Na flux and wave dissipation were largest. These downward propagating waves are likely secondary waves generated in‐situ by the dissipation of primary waves that originate from lower altitudes. The sensible heat flux transitions from downward below 90 km to upward from 97 to 106 km. The observations are explained with the fully compressible solutions for polarization relations of primary and secondary gravity waves withλ_z > 10 km.

Random‐noise‐induced biases are inherent issues to the accurate derivation of second‐order statistical parameters (e.g., variances, fluxes, energy densities, and power spectra) from lidar and radar measurements. We demonstrate here for the first time an altitude‐interleaved method for eliminating such biases, following the original proposals by Gardner and Chu (2020,https://doi.org/10.1364/ao.400375) who demonstrated a time‐interleaved method. Interleaving in altitude bins provides two statistically independent samples over the same time period and nearly the same altitude range, thus enabling the replacement of variances that include the noise‐induced biases with covariances that are intrinsically free of such biases. Comparing the interleaved method with previous variance subtraction (VS) and spectral proportion (SP) methods using gravity wave potential energy density calculated from Antarctic lidar data and from a forward model, this study finds the accuracy and precision of each method differing in various conditions, each with its own strengths and weakness. VS performs well in high‐SNR, yet its accuracy fails at lower‐SNR as it often yields negative values. SP is accurate and precise under high‐SNR, remaining accurate in worse conditions than VS would, yet develops a positive bias under low‐SNR. The interleaved method is accurate in all SNRs but requires a large number of samples to drive random‐noise terms in covariances toward zero and to compensate for the reduced precision due to the splitting of return signals. Therefore, selecting the proper bias removal/elimination method for actual signal and sample conditions is crucial in utilizing lidar/radar data, as neglecting this can conceal trends or overstate atmospheric variability.

The precision of lidar measurements is limited by noise associated with the optical detection process. Photon noise also introduces biases in the second-order statistics of the data, such as the variances and fluxes of the measured temperature, wind, and species variations, and establishes noise floors in the computed fluctuation spectra. When the signal-to-noise ratio is low, these biases and noise floors can completely obscure the atmospheric processes being observed. We describe a novel data processing technique for eliminating the biases and noise floors. The technique involves acquiring two statistically independent datasets, covering the same altitude range and time period, from which the various second-order statistics are computed. The efficacy of the technique is demonstrated using Na Doppler lidar observations of temperature in the upper mesosphere and lower thermosphere acquired recently at McMurdo Station, Antarctica. The results show that this new technique enables observations of key atmospheric parameters in regions where the signal-to-noise ratio is far too low to apply conventional processing approaches.